System

Abstract

A system is provided.SOLUTION: A system comprising: means for analyzing traffic information using a AI algorithm; means for optimizing a traffic route based on the analyzed traffic information; and means for recommending sustainable transportation based on the optimized traffic route.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Urban transportation faces many problems, including air pollution and congestion, and rising carbon emissions are a particularly significant challenge. Furthermore, many cities struggle to optimize their efficient public transportation systems. This situation is caused by the ineffective use of sustainable transportation modes and inappropriate traffic route planning. As a result, the city's environmental burden and the quality of life of its residents are declining. Therefore, there is a need to realize efficient and eco-friendly public transportation systems in urban areas. [Means for solving the problem]

[0005] In order to solve the above-mentioned problems, the present invention provides a system that uses an AI algorithm to analyze traffic data, generate an optimal traffic route, and recommend sustainable transportation modes based on the generated route. The system includes the following means:

[0006] 1. A means of analyzing traffic data using AI algorithms.

[0007] 2. A means of optimizing traffic routes based on analyzed traffic data.

[0008] 3. A means of recommending sustainable transport modes based on optimised transport routes.

[0009] Furthermore, by recording the recommended sustainable transportation modes in a data frame and including a means to calculate the average latitude and longitude of each cluster, more specific and effective optimization and sustainability of the transportation system can be achieved. This system will enable cities to reduce their carbon footprint and build an eco-friendly and efficient public transportation system.

[0010] An "AI algorithm" is a computational procedure for analyzing and processing data based on artificial intelligence technology.

[0011] "Traffic data" is data that includes information related to traffic flow, speed, location, etc.

[0012] "Analysis" is the process of examining collected data in detail to find patterns and regularities.

[0013] A "transportation route" is a route traveled between a starting point and a destination.

[0014] "Optimization" is the process of making something best suited to a specific purpose or condition.

[0015] "Sustainable transport" is a means of transporting people in a way that minimises the burden on the environment.

[0016] A "data frame" is a two-dimensional data structure that represents data in the form of rows and columns.

[0017] In data analysis, a "cluster" is a collection of data points that have similar characteristics.

[0018] "Latitude" is a coordinate that indicates a location on Earth in a north or south direction.

[0019] "Longitude" is a coordinate that indicates a location on Earth in an east or west direction.

[0020] A "centroid" is a coordinate that indicates the average location of data points within a cluster. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0022] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0023] First, the terms used in the following description will be explained.

[0024] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0025] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0026] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0027] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0028] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0032] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0033] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0034] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0035] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0036] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0039] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0040] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0041] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0042] The present invention provides a system for analyzing traffic data, generating optimal traffic routes, and recommending sustainable transportation modes. This system is implemented with the following configuration, centered around a server.

[0043] System program processing contents

[0044] 1. Loading traffic data

[0045] The server collects traffic data (including latitude and longitude) and converts it into a data frame, which reflects actual traffic conditions and contains location data for each point within a city.

[0046] 2. Traffic pattern analysis

[0047] The server analyzes the collected traffic data using the K-means clustering algorithm. This analysis classifies the traffic data into several clusters. Each cluster is a set of locations with similar traffic patterns. As a result of the analysis, each data point is assigned a cluster identifier to which it belongs.

[0048] 3. Calculating the cluster center (centroid)

[0049] The server calculates the average latitude and longitude of all data points within each cluster, which gives the center point (centroid) of each cluster, allowing for the generation of optimal travel routes that take into account distance and time efficiency.

[0050] 4. Promoting sustainable transportation

[0051] The server randomly recommends the best mode for each cluster based on a predefined list of sustainable modes (e.g., bus, e-bike, tram, etc.) and records the recommended mode as a new column in the data frame.

[0052] As a concrete example, the process is as follows: For example, transportation data is collected as latitude and longitude data for five points in a city. This data is loaded into a server and the K-means clustering algorithm is applied to divide each data point into five clusters. Next, the average latitude and longitude values ​​for each cluster are calculated and the optimal transportation route is determined based on this. After that, the optimal sustainable transportation mode is recommended for each cluster and the information is recorded in a data frame.

[0053] In this way, the system of the present invention can create an efficient and eco-friendly public transport system, contributing to reducing the carbon footprint of cities.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] The server loads the traffic data and converts it into a Pandas dataframe, which contains the latitude and longitude of each point.

[0057] Step 2:

[0058] The server applies the K-means clustering algorithm to the loaded traffic data, which classifies the data points into five clusters. Each data point is given a 'cluster' column with the cluster identifier to which it belongs.

[0059] Step 3:

[0060] The server calculates the average latitude and longitude of each cluster and determines the center point (centroid) of each cluster. This centroid becomes the base point for the optimal transportation route.

[0061] Step 4:

[0062] The server creates a list of predefined sustainable transport modes (bus, e-bike, tram, etc.), then randomly selects and recommends a sustainable transport mode based on the value of the 'cluster' column for each data point, and adds this recommendation to a new 'recommended_mode' column in the data frame.

[0063] Step 5:

[0064] The server records and stores information on optimized travel routes and sustainable transportation modes in a data frame, which is then provided to devices and users via APIs and dashboards as needed.

[0065] Example 1

[0066] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0067] Traffic congestion and environmental impacts are major problems in modern urban transportation systems. To address these issues, there is a need for methods to improve traffic efficiency and reduce environmental impacts by effectively analyzing traffic data, generating optimal transportation routes, and recommending sustainable transportation modes.

[0068] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0069] In this invention, the server includes means for collecting traffic data and converting it into a data frame, means for analyzing the collected traffic data using a clustering algorithm, and means for calculating the average latitude and longitude of data points belonging to each cluster, thereby enabling effective analysis of the traffic data, generation of optimal traffic routes, and recommendation of sustainable transportation modes.

[0070] "Transportation Data" refers to location information, including latitude and longitude, of each point within a city and includes those data points.

[0071] "Data frame" refers to a tabular data structure that structures data in a matrix format to make it easier to manage.

[0072] A "clustering algorithm" is a computational method for dividing data points into groups based on similarity, and in the present invention primarily refers to K-means clustering.

[0073] A "cluster" refers to a group of data points that results from using a clustering algorithm.

[0074] "Average latitude and longitude" means the sum of the latitude and longitude values ​​of all data points belonging to a particular cluster, divided by the number of data points.

[0075] A "transportation route" refers to the optimal route for traveling from one point to another, and is determined taking into account the most efficient means of transportation and time efficiency.

[0076] "Sustainable transportation" refers to transportation that reduces environmental impact and minimizes energy consumption, and examples include buses, electric bicycles, and trams.

[0077] The present invention is a system that analyzes traffic data, generates optimal traffic routes, and recommends sustainable transportation modes. This system performs the following specific processes, centered on a server:

[0078] First, the server collects traffic data, including the latitude and longitude of each point in the city, collected via GPS devices and smartphone applications. The collected data is then converted into a data frame for easy handling. This conversion is performed using the Python Pandas library.

[0079] The server then applies a clustering algorithm to the collected traffic data to analyze traffic patterns. This analysis process uses the Scikit-learn library to run the K-means clustering algorithm, which divides data points into groups based on similarity. The analysis results can be classified into areas that are congested during rush hour and areas that are congested even during normal times.

[0080] The server then calculates the average latitude and longitude of the data points in each cluster, which gives the center point (centroid) of each cluster. The calculated centroid is recorded in a new data frame and used to generate optimal traffic routes.

[0081] The server then identifies the optimal mode for each cluster based on a predefined list of sustainable modes (e.g., bus, e-bike, tram, etc.). The selected mode is randomly assigned and finally recorded as a new column in the data frame.

[0082] For example, transportation data is collected from the latitude and longitude of five points in a city. This data is loaded onto a server and the K-means clustering algorithm is applied to divide each data point into five clusters. The average latitude and longitude values ​​of each cluster are then calculated to determine the optimal transportation route. Finally, the optimal sustainable transportation mode is recommended for each cluster and the information is recorded in a data frame.

[0083] For example, the following can be entered as a prompt to a generative AI model:

[0084] "Please describe the processing steps of a system that uses transportation data (latitude and longitude) from five locations in a city to generate optimal transportation routes using K-means clustering and recommend sustainable transportation modes. These steps should include loading transportation data, analyzing traffic patterns, calculating cluster centers, and recommending sustainable transportation modes. Also, please include the specific actions taken at each step."

[0085] In this way, the system of the present invention can create an efficient and eco-friendly public transport system, contributing to reducing the carbon footprint of cities.

[0086] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0087] Step 1:

[0088] Collecting and loading traffic data

[0089] The server collects traffic data, including the latitude and longitude of each point in the city, obtained from GPS devices and smartphone applications.

[0090] Input: Latitude and longitude data for each location

[0091] Output: Data frame

[0092] The server uses Python's Pandas library to convert the collected data into a data frame, which is then used in subsequent analysis steps.

[0093] Step 2:

[0094] Traffic pattern analysis

[0095] The server applies a clustering algorithm to the collected traffic data, in this case the K-means clustering algorithm.

[0096] Input: DataFrame

[0097] Output: Data frame with cluster identifiers for each data point

[0098] The server uses the Scikit-learn library to perform K-means clustering to classify data points into clusters based on their similarity.

[0099] Step 3:

[0100] Calculating cluster centroids

[0101] The server calculates the average latitude and longitude of the data points that belong to each cluster.

[0102] Input: Data frame with cluster identifiers

[0103] Output: A data frame representing the centroids of each cluster

[0104] The server uses the Pandas library to calculate the average latitude and longitude values ​​for each cluster and record them in a new data frame.

[0105] Step 4:

[0106] Generate optimal transportation routes

[0107] The server generates an optimal traffic route based on the calculated cluster centroid.

[0108] Input: A data frame representing the centroids of each cluster

[0109] Output: Data frame containing optimal transportation routes

[0110] The server uses an algorithm to determine the best route between centroids, taking into account distance and travel time efficiency.

[0111] Step 5:

[0112] Promoting sustainable transportation

[0113] The server recommends the best mode of transport for each cluster based on a predefined list of sustainable modes of transport (e.g., bus, e-bike, tram, etc.).

[0114] Input: A data frame containing optimal transit routes

[0115] Output: A data frame with a new column of sustainable transport recommendations

[0116] The server assigns the optimal transportation mode to each cluster randomly or based on specific conditions and records the information in a data frame.

[0117] In this way, the server analyzes traffic data and recommends optimal routes and sustainable transportation methods. This process improves urban traffic efficiency and creates an eco-friendly environment.

[0118] (Application example 1)

[0119] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0120] In recent years, traffic congestion and environmental pollution have become serious problems in urban areas. Furthermore, conventional methods often do not achieve sufficient accuracy when selecting optimal transportation routes or recommending sustainable transportation modes. Furthermore, there are challenges in analyzing traffic data in real time and proposing optimal routes using the results.

[0121] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0122] In this invention, the server includes means for analyzing traffic data using an AI algorithm, means for optimizing traffic routes based on the analyzed traffic data, means for recommending sustainable transportation modes based on the optimized traffic routes, means for analyzing traffic patterns using a K-means clustering algorithm, and means for displaying optimal routes in an application installed on a smartphone. This makes it possible to alleviate traffic congestion and reduce environmental pollution in urban areas, while also enabling highly accurate route suggestions in real time.

[0123] An "AI algorithm" is a calculation method that uses artificial intelligence technology to analyze data and make predictions.

[0124] "Traffic Data" means a collection of information including traffic conditions, such as latitude, longitude, time, and other traffic-related data.

[0125] A "means for optimizing traffic routes" is a method for calculating the most efficient travel route based on collected traffic data.

[0126] "Sustainable transportation" refers to transportation that can be operated over the long term while minimizing the burden on the environment. Examples include electric vehicles and carpooling.

[0127] A "traffic pattern" is a characteristic that indicates the traffic conditions and their fluctuations in a particular area or time period.

[0128] The "K-means clustering algorithm" is a machine learning algorithm for classifying data into K clusters, and is widely used in analyzing traffic data.

[0129] An "application installed on a smartphone" is software that runs on a smartphone device and provides functionality through a user interface.

[0130] A "data frame" is a data structure for managing a collection of data consisting of rows and columns, and is used for analysis and manipulation.

[0131] "Means for collecting traffic data in real time" refers to a method for instantly obtaining current traffic conditions.

[0132] "Means for displaying optimal route" means a method for visually showing the calculated optimal travel route to the user.

[0133] System program generation

[0134] The system for realizing the present invention is implemented through the following steps.

[0135] 1. Loading traffic data

[0136] The server collects traffic data from storage such as a database and converts it into a data frame using a data frame library such as Pandas. For example, PostgreSQL is used to query traffic data including latitude, longitude, and time, and read it as a data frame.

[0137] 2. Traffic pattern analysis

[0138] The server uses the K-means clustering algorithm to analyze the collected traffic data. It uses the scikit-learn library to classify data points into clusters that indicate specific traffic patterns.

[0139] 3. Calculating the cluster center (centroid)

[0140] The server calculates the average latitude and longitude of all data points within each cluster, which gives the center point (centroid) of each cluster, and then plans optimal traffic routes based on that.

[0141] 4. Promoting sustainable transportation

[0142] The server recommends a sustainable transportation mode for each cluster by randomly selecting it from the sustainable transportation mode list and recording it in a data frame.

[0143] 5. Display on smartphone applications

[0144] The application installed on the user's smartphone receives information on optimal routes and sustainable transportation methods sent from the server and displays it in real time. It is preferable to have a flat user interface, for example, to display the route on a map using Google Maps API.

[0145] Hardware and software used

[0146] Hardware: Servers, smartphones, network devices

[0147] Software: Pandas, scikit-learn, PostgreSQL, Flask, Google Maps API

[0148] Data processing and calculation

[0149] 1. Processing of traffic data

[0150] Querying from the database

[0151] Dataframe transformation with Pandas

[0152] 2. Traffic pattern calculation

[0153] Classification by K-means clustering

[0154] Calculate the centroid of each cluster

[0155] 3. Selecting sustainable transportation options

[0156] Update a dataframe with random selection

[0157] Specific examples

[0158] The operation of the system will be explained in detail through the following concrete example.

[0159] When a user searches for a route from "Shinjuku Station" to "Tokyo Tower," the system performs the following process.

[0160] 1. Loading traffic data

[0161] The server loads the traffic data for "Shinjuku-ku" and "Minato-ku" with the following query:

[0162] SELECT latitude, longitude, timestamp FROM traffic_data WHERE region IN ('Shinjuku', 'Minato');

[0163] 2. Traffic pattern analysis

[0164] Using the loaded data, apply K-means clustering as follows:

[0165] KMeans(n_clusters=5).fit(data_frame)

[0166] 3. Cluster centroid calculation

[0167] Calculate the center point of each cluster and get the optimal route as follows:

[0168] data_frame.groupby('cluster').mean()

[0169] 4. Promoting sustainable transportation

[0170] Randomly select electric vehicles or carpools for each cluster and add them to the dataframe as follows:

[0171] data_frame['sustainable_transport'] = np.random.choice(['electric_car', 'carpool'], len(data_frame))

[0172] 5. Display on smartphone applications

[0173] The calculated route and recommended transportation methods are displayed on a map using the Google Maps API, providing a visual representation to the user.

[0174] In this way, the system of the present invention can provide an efficient and sustainable means of transportation and contribute to improving traffic problems in urban areas.

[0175] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0176] Step 1: Loading traffic data

[0177] The server queries the traffic data from the database and converts it into a Pandas dataframe. This query retrieves traffic data for a specified area, including latitude, longitude, and time. For example, it executes the following SQL query:

[0178] SELECT latitude, longitude, timestamp FROM traffic_data WHERE region IN ('Shinjuku', 'Minato');

[0179] Input: Database

[0180] Output: Pandas dataframe

[0181] Step 2: Analyze traffic patterns

[0182] The server analyzes the loaded traffic data using the K-means clustering algorithm to classify data points into multiple clusters. It uses the scikit-learn library to perform the clustering. Specifically, it applies K-means clustering as follows:

[0183] from sklearn.cluster import KMeans

[0184] kmeans = KMeans(n_clusters=5).fit(data_frame)

[0185] data_frame['cluster'] = kmeans.labels_

[0186] Input: Pandas dataframe

[0187] Output: Data frame with cluster identifiers

[0188] Step 3: Calculate the cluster centers (centroids)

[0189] The server calculates the average latitude and longitude within each cluster to determine the centroid, which is the center point of the cluster and is used to set the optimal traffic route. Specifically, it calculates as follows:

[0190] centroids = data_frame.groupby('cluster').mean()

[0191] Input: Data frame with cluster identifiers

[0192] Output: Centroid data frame for each cluster

[0193] Step 4: Promote sustainable transportation

[0194] The server randomly selects a sustainable transportation mode for each cluster and records it in a data frame. Sustainable transportation modes include electric vehicles and carpooling. Specifically, the following settings are used:

[0195] import numpy as np

[0196] data_frame['sustainable_transport'] = np.random.choice(['electric_car', 'carpool'], len(data_frame))

[0197] Input: Centroid data frame of clusters

[0198] Output: A data frame containing sustainable transport modes

[0199] Step 5: Display on smartphone application

[0200] The smartphone device receives information on optimal routes and sustainable transportation methods sent from the server and displays it on a map in real time. It uses the Google Maps API to provide visual route information. Specifically, it sets markers on the map as follows:

[0201] map.addMarker(new google.maps.Marker({

[0202] position: {lat: 35.6895, lng: 139.6917}, / / Example coordinates

[0203] map: map,

[0204] title: 'Start Point'

[0205] }));

[0206] Input: A data frame containing sustainable transport modes

[0207] Output: A smartphone application that displays the optimal route and transportation options

[0208] Through the application, users can view the best routes and sustainable transportation options in real time, enabling them to travel eco-friendly.

[0209] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0210] The present invention combines a system for analyzing traffic data, generating optimal traffic routes, and recommending sustainable transportation modes with an emotion engine that recognizes user emotions. This system is implemented in the following configuration, including a server, a terminal, and a user.

[0211] System program processing contents

[0212] Loading traffic data

[0213] The server loads the traffic data and converts it into a Pandas data frame, which contains the latitude and longitude of each point, so that the system can access the latitude and longitude of each point within the city.

[0214] Traffic pattern analysis

[0215] The server applies the K-means clustering algorithm to the loaded traffic data, which classifies the data points into five clusters. Each data point is given a 'cluster' column with the cluster identifier to which it belongs.

[0216] Calculating cluster centroids

[0217] The server calculates the average latitude and longitude of all data points within each cluster, and determines the center point (centroid) of each cluster. This centroid becomes the base point for the optimal traffic route.

[0218] Promoting sustainable transportation

[0219] The server creates a list of predefined sustainable transport modes (bus, e-bike, tram, etc.), then randomly selects and recommends a sustainable transport mode based on the value of the 'cluster' column for each data point, and adds this recommendation to a new 'recommended_mode' column in the data frame.

[0220] User sentiment analysis

[0221] The emotion engine recognizes and analyzes the user's emotions. User emotion data is collected, for example, from the user's smartphone or other devices. This can be done using technologies such as facial recognition, voice analysis, and text analysis. This emotion analysis helps identify which transportation modes the user has positive or negative feelings about.

[0222] Emotion-based optimization of sustainable transportation

[0223] The server integrates the emotion data obtained from the emotion engine into the analysis results and further optimizes the recommendation of sustainable transportation modes based on the emotion data.For example, if a user has a strong preference for a particular transportation mode based on the user's emotion data, the server prioritizes the recommendation of that transportation mode.

[0224] For example, transportation data is collected from the latitude and longitude of five points in a city. This data is loaded into a server and the K-means clustering algorithm is applied to divide each data point into five clusters. Next, the average latitude and longitude of each cluster is calculated and the optimal transportation route is determined based on this. After that, the optimal sustainable transportation mode is recommended for each cluster and the information is recorded in a data frame.

[0225] Furthermore, when a user sends emotional data to the server via their smartphone, the emotion engine analyzes the data and, based on the analysis results, recommends a more appropriate mode of transportation that matches the emotional data.

[0226] In this way, the system of the present invention can build an efficient and eco-friendly public transportation system, contributing to reducing the carbon footprint of cities. Furthermore, by taking into account the user's emotions, it can provide an environment where users can travel without feeling stressed.

[0227] The processing flow will be explained below.

[0228] Step 1:

[0229] The server loads the traffic data and converts it into a Pandas data frame, which contains the latitude and longitude of each point, allowing the system to capture the location of each point within the city.

[0230] Step 2:

[0231] The server applies the K-means clustering algorithm to the loaded traffic data, which classifies the data points into five clusters. Each data point is given a 'cluster' column with the cluster identifier to which it belongs.

[0232] Step 3:

[0233] The server calculates the average latitude and longitude of all data points within each cluster, and determines the center point (centroid) of each cluster. This centroid becomes the base point for the optimal traffic route.

[0234] Step 4:

[0235] The server creates a list of sustainable transport modes (bus, e-bike, tram, etc.). It then randomly selects and recommends a sustainable transport mode based on the value of the 'cluster' column for each data point. This recommendation is added to a new 'recommended_mode' column in the data frame.

[0236] Step 5:

[0237] The device collects emotional data from the user. This emotional data is acquired using, for example, the smartphone's camera or microphone. The emotional data includes facial expressions, voice tone, text content, and so on.

[0238] Step 6:

[0239] The device transmits the collected emotion data to a server, which then analyzes the user's emotions using an emotion engine and obtains the analysis results.

[0240] Step 7:

[0241] The server integrates the emotion data obtained from the emotion engine with the transportation data. Based on the user's emotion data, the server further optimizes the recommendation of sustainable transportation modes. For example, if a user has a favorable emotion toward a particular transportation mode, the server prioritizes the recommendation of that mode.

[0242] Step 8:

[0243] The server records and stores information on optimized travel routes and sustainable transportation modes in a data frame, which is then provided to devices and users via APIs and dashboards as needed.

[0244] For example, traffic data is collected from the latitude and longitude of five points in a city. This data is loaded onto a server and the K-means clustering algorithm is applied to divide each data point into five clusters. Next, the average latitude and longitude of each cluster is calculated, and the optimal traffic route is determined based on this.

[0245] The device then collects the user's emotional data and sends it to the server. The server analyzes the emotional data and optimizes the recommendation of sustainable transportation methods based on the results of the emotional analysis. The recommended transportation methods based on the emotional data are recorded in a data frame and provided to the user as necessary information. This reduces the city's carbon footprint and provides an environment where users can travel without stress.

[0246] Example 2

[0247] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0248] There is a need to efficiently analyze transportation data, determine optimal transportation routes, and effectively recommend sustainable transportation modes. It is also necessary to provide a transportation system that is more convenient and satisfies users by optimizing transportation modes based on user emotions. However, while current systems are capable of analyzing transportation data and selecting sustainable transportation modes, they do not adequately optimize transportation modes based on user emotions. This leads to lower user satisfaction and efficiency, and hinders the promotion of sustainable transportation modes.

[0249] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0250] In this invention, the server includes means for collecting traffic data and converting it into a data frame format, means for applying a clustering algorithm to the converted traffic data to classify data points into clusters, means for calculating the center point (average latitude and longitude) of each cluster, means for randomly selecting sustainable transportation modes and recommending the transportation modes, means for analyzing user emotion data, and means for optimizing the recommended transportation modes based on the emotion data. This not only enables efficient analysis of traffic data and recommendation of sustainable transportation modes, but also enables optimization of transportation modes based on user emotion.

[0251] "Traffic data" refers to data that includes various information related to traffic conditions, such as latitude, longitude, traffic volume, speed, and time of day.

[0252] The "data frame format" is a two-dimensional data structure with rows and columns, and is a format for representing tabular data used in Python's Pandas library.

[0253] A "clustering algorithm" is an algorithm for classifying data points into groups (clusters) that share common characteristics.

[0254] A "cluster" is a collection of data points that share common characteristics, grouped together using a clustering algorithm.

[0255] A "centroid" is a point that represents the average location of all data points within a cluster.

[0256] "Sustainable transport" refers to eco-friendly transport methods (e.g. buses, electric bicycles, trams, etc.) that aim to reduce carbon footprints and protect the environment.

[0257] "Emotional data" is data that indicates a user's emotional state and is collected using technologies such as facial recognition, voice analysis, and text analysis.

[0258] An "emotion engine" is a computer program or algorithm that analyzes a user's emotional data and identifies their emotional state.

[0259] "Optimization" is the process of arranging resources and conditions most effectively to achieve a particular objective.

[0260] The present invention is a system that combines multiple pieces of hardware and software to analyze traffic data, generate optimal traffic routes, and recommend sustainable transportation modes. Specific embodiments are described below.

[0261] First, the server collects traffic data and converts it into a data frame using Python's Pandas library. This data includes the latitude and longitude of each location. Next, the server applies a clustering algorithm using the Python library Sci-kit Learn to classify the data points into clusters. During this process, each data point is assigned a cluster identifier.

[0262] The server calculates the average latitude and longitude of data points in each cluster and uses this as the centroid, which represents the cluster. The server then creates a list of sustainable transportation modes (e.g., bus, electric bicycle, tram, etc.) and randomly selects and recommends a sustainable transportation mode based on each cluster identifier. This recommendation information is added to a new column 'recommended_mode' in the data frame.

[0263] The server also collects emotion data from the user's device. This emotion data is collected using technologies such as facial recognition, voice analysis, and text analysis. For example, facial recognition is performed using technologies such as OpenCV and Google Cloud Vision API, and emotions are analyzed from the user's facial expressions. Based on the collected emotion data, the emotion engine analyzes the user's emotions and sends them to the server.

[0264] The server integrates the emotion data obtained from the emotion engine with the analysis results and optimizes the recommendation of sustainable transportation modes based on the emotion data. For example, if a user has a strong preference for a particular transportation mode, the recommendation of that mode will be prioritized, improving the accuracy of the recommendation.

[0265] For example, the latitude and longitude data of five points in a city are collected as transportation data and loaded onto a server. The K-means clustering algorithm is applied to this data to classify each data point into five clusters. Next, the average latitude and longitude values ​​of each cluster are calculated, and the optimal transportation route is determined based on this. After that, a sustainable transportation mode is randomly selected for each cluster, recommended, and the information is recorded in a data frame.

[0266] For example, a server receives emotional data sent by a user from a smartphone and performs an emotional analysis. Based on the analysis results, the system recommends the user's preferred mode of transportation. Through this process, the system of the present invention supports the creation of an efficient and eco-friendly public transportation system, contributing to reducing a city's carbon footprint and improving user satisfaction.

[0267] Example prompts for generative AI models:

[0268] "Using the following location data, generate code that generates optimal transportation routes and recommends sustainable modes of transportation. Additionally, include a section that analyzes user sentiment data to optimize recommended modes of transportation."

[0269] In this way, the embodiments of the present invention enable efficient analysis of traffic data, recommendation of sustainable transportation modes, and even optimization of transportation modes based on user sentiment data, thereby improving the efficiency of the overall transportation system and user satisfaction.

[0270] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0271] Step 1:

[0272] Traffic data collection and conversion

[0273] The server collects traffic data from the Internet or an internal database. For example, it uses an API to obtain traffic data and receives a dataset containing information such as latitude and longitude. It then converts this data into a data frame using the Python Pandas library. The input is raw traffic data, and the output is the traffic data converted into a data frame.

[0274] Specific actions

[0275] The server retrieves the traffic data from the API and then converts it into a data frame using Pandas, which contains the latitude and longitude of each point.

[0276] Step 2:

[0277] Traffic pattern analysis

[0278] The server applies Sci-Kit Learn's K-means clustering algorithm to the converted traffic data. The input is traffic data in a data frame format, and the output is the clustered data. This classifies each data point into a cluster and assigns a cluster identifier to each data point.

[0279] Specific actions

[0280] The server applies K-means clustering and assigns a cluster identifier to each data point, and the results are added as a new column in the data frame.

[0281] Step 3:

[0282] Calculating cluster centroids

[0283] The server calculates the average latitude and longitude within each cluster and defines this as the centroid. The input is traffic data with cluster identifiers, and the output is the centroid position of each cluster.

[0284] Specific actions

[0285] The server averages the latitude and longitude of all data points within each cluster and records this as the center point of each cluster in the data frame.

[0286] Step 4:

[0287] Promoting sustainable transportation

[0288] The server prepares a list of sustainable transport modes (e.g., buses, e-bikes, trams) and randomly selects one based on each cluster identifier. The input is the transport data with calculated centroids, and the output is the recommended sustainable transport mode.

[0289] Specific actions

[0290] The server randomly selects from the list of sustainable transportation options and adds the recommendations to a new column in the data frame.

[0291] Step 5:

[0292] User sentiment analysis

[0293] The emotion engine analyzes emotion data collected from the user's device. The input is the results of facial recognition, voice analysis, and text analysis sent from the user's device, and the output is the analyzed emotion data.

[0294] Specific actions

[0295] The user's device collects emotional data through a camera and microphone and sends it to a server, which then uses an emotion engine to analyze it and identify the user's emotional state.

[0296] Step 6:

[0297] Emotion-based optimization of sustainable transportation

[0298] The server integrates the emotion data obtained from the emotion engine with the analysis results to optimize the recommendation of sustainable transportation modes. The input is the emotion data from the emotion engine and existing recommendation information, and the output is the recommended transportation modes optimized based on the emotion data.

[0299] Specific actions

[0300] The server updates the recommendation list based on the user's preferred mode of transportation based on the emotion data. For example, if the user prefers electric bicycles, electric bicycles will be prioritized in the recommendation list.

[0301] (Application example 2)

[0302] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0303] Optimizing transportation routes and recommending sustainable modes of transportation are important for reducing a city's carbon footprint and providing efficient mobility. However, existing systems do not take into account the user's emotional state, which can lead to lower user satisfaction and stress levels. This reduces users' willingness to use the recommended modes of transportation, resulting in a problem of reduced effectiveness of the overall system.

[0304] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0305] In this invention, the server includes means for analyzing traffic data using an AI algorithm, means for optimizing a traffic route based on the analyzed traffic data, means for recommending a sustainable means of transportation based on the optimized traffic route, emotion recognition means for analyzing a user's emotion, and means for optimizing the recommended sustainable means of transportation based on the user's emotion data. This allows the server to recommend the optimal means of transportation according to the user's emotional state, thereby improving user satisfaction and system efficiency.

[0306] Key Word Definitions

[0307] An "AI algorithm" is a computational procedure that uses artificial intelligence technology to analyze data and discover specific patterns.

[0308] "Traffic data" is a collection of data containing information related to traffic, such as location information and traffic conditions.

[0309] "Means for optimizing traffic routes" refers to a method for calculating optimal routes based on analyzed traffic data.

[0310] "Sustainable transport" is transport designed to minimise environmental impact, including public transport and electric vehicles.

[0311] "Emotion recognition means" refers to technology for detecting and analyzing a user's emotions, and uses techniques such as facial recognition and voice analysis.

[0312] "User emotion data" is data that indicates the user's emotional state, and is acquired in the form of an image, sound, text, or the like.

[0313] The "means for optimizing sustainable transportation" is a method for adjusting recommendations for sustainable transportation based on user sentiment data.

[0314] A "data frame" is a two-dimensional data structure consisting of rows and columns, used for data processing and analysis.

[0315] "Location data" is information that indicates the latitude and longitude of a specific point.

[0316] MODE FOR CARRYING OUT THE INVENTION

[0317] This invention relates to a system that uses AI algorithms to analyze traffic data and recommend optimal routes and sustainable transportation methods based on user emotion data. This system is implemented with the following components, including a server, a terminal, and a user.

[0318] 1. Loading and analyzing traffic data

[0319] The server loads and analyzes traffic data, which includes information such as location and traffic conditions. The server manages the traffic data using a Pandas data frame and classifies the data points using the K-means clustering algorithm.

[0320] 2. Traffic route optimization

[0321] The server generates the optimal traffic route based on the analyzed traffic data. At this stage, it calculates the average value of the location data (latitude and longitude) of each cluster to determine the centroid. This allows the optimal traffic route to be designed.

[0322] 3. Promoting sustainable transportation

[0323] The server recommends sustainable transportation modes based on the optimized transportation route. The recommended transportation modes are randomly selected from a predefined list. The recommended transportation modes are recorded in a data frame.

[0324] 4. Emotion analysis

[0325] The server collects emotion data from users' smartphones and other devices and analyzes it using an emotion engine, which uses technologies such as facial recognition and voice analysis to recognize the user's emotions.

[0326] 5. Emotion-Based Optimization

[0327] The server optimizes the recommended sustainable transportation modes based on the sentiment data: if the user has a positive sentiment towards a particular transportation mode, it prioritizes the recommendation of that mode.

[0328] Specific examples

[0329] Transportation data is collected from multiple locations in a city using latitude and longitude data. This data is loaded onto a server and the K-means clustering algorithm is applied to separate the data points into clusters. The average latitude and longitude of each cluster is calculated to determine the centroid. The optimal sustainable transportation mode (e.g., electric bus or electric bicycle) is then recommended for each cluster and the information is recorded in a data frame.

[0330] Furthermore, when users send emotion data to the server via their smartphones, the emotion engine analyzes the data and re-recommends the optimal sustainable transportation mode based on the emotion data. In this way, the system can provide the optimal transportation mode that improves user satisfaction.

[0331] Prompt Sentence Examples

[0332] This includes examples of inputting prompts like the following into a generative AI model:

[0333] Create a system that suggests sustainable transportation options based on the user's emotional data ('happy', 'relaxed', etc.). For example:

[0334] If the user's emotion is "happy," suggest an electric bus.

[0335] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0336] System processing flow

[0337] Step 1:

[0338] The server loads the traffic data. The traffic data is read from a CSV file and converted into a Pandas data frame. The input at this stage is traffic data including location information and traffic conditions, and the output is traffic data in data frame format.

[0339] Step 2:

[0340] The server applies the K-means clustering algorithm to the loaded traffic data. This process classifies each data point into a specific cluster. The input is the data frame obtained in step 1, and the output is a data frame with the cluster identifiers added.

[0341] Step 3:

[0342] The server calculates the average latitude and longitude of each cluster, which determines the center point (centroid) of the cluster. The input is the data frame with cluster identifiers obtained in step 2, and the output is the centroid position data of each cluster.

[0343] Step 4:

[0344] The server recommends sustainable transportation modes based on the optimized transportation route. A predefined list of sustainable transportation modes (e.g., electric bus, electric bicycle, tram) is used in this stage. The input is the centroid location data for each cluster, and the output is a data frame with the recommended transportation modes added.

[0345] Step 5:

[0346] The device collects the user's emotional data. This emotional data is obtained from smartphones and other devices and uses technologies such as facial recognition and voice analysis. The input is the user's biometric data and voice data, and the output is analyzed emotional data.

[0347] Step 6:

[0348] The server analyzes the user's emotional data using an emotion engine, which identifies the user's current emotional state. The input is the emotional data obtained in step 5, and the output is the user's emotional state (e.g., happy, relaxed, etc.).

[0349] Step 7:

[0350] The server optimizes the recommended sustainable transportation modes based on the user's emotional data. For example, if the user is happy, it preferentially recommends electric buses. The input is the emotional state obtained in step 6 and the recommended transportation modes obtained in step 4, and the output is a data frame with the optimized transportation modes added based on the emotional state.

[0351] Specifically, the smartphone app captures the user's face with a camera and sends the image data to the server. The server then performs emotion recognition and analyzes the user's emotional state in real time. It then suggests an appropriate transportation method and sends a notification to the user's smartphone.

[0352] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0353] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0354] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0355] [Second embodiment]

[0356] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0357] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0358] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0359] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0360] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0361] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0362] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0363] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0364] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0365] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0366] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0367] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0368] The present invention provides a system for analyzing traffic data, generating optimal traffic routes, and recommending sustainable transportation modes. This system is implemented with the following configuration, centered around a server.

[0369] System program processing contents

[0370] 1. Loading traffic data

[0371] The server collects traffic data (including latitude and longitude) and converts it into a data frame, which reflects actual traffic conditions and contains location data for each point within a city.

[0372] 2. Traffic pattern analysis

[0373] The server analyzes the collected traffic data using the K-means clustering algorithm. This analysis classifies the traffic data into several clusters. Each cluster is a set of locations with similar traffic patterns. As a result of the analysis, each data point is assigned a cluster identifier to which it belongs.

[0374] 3. Calculating the cluster center (centroid)

[0375] The server calculates the average latitude and longitude of all data points within each cluster, which gives the center point (centroid) of each cluster, allowing for the generation of optimal travel routes that take into account distance and time efficiency.

[0376] 4. Promoting sustainable transportation

[0377] The server randomly recommends the best mode for each cluster based on a predefined list of sustainable modes (e.g., bus, e-bike, tram, etc.) and records the recommended mode as a new column in the data frame.

[0378] As a concrete example, the process is as follows: For example, transportation data is collected as latitude and longitude data for five points in a city. This data is loaded into a server and the K-means clustering algorithm is applied to divide each data point into five clusters. Next, the average latitude and longitude values ​​for each cluster are calculated and the optimal transportation route is determined based on this. After that, the optimal sustainable transportation mode is recommended for each cluster and the information is recorded in a data frame.

[0379] In this way, the system of the present invention can create an efficient and eco-friendly public transport system, contributing to reducing the carbon footprint of cities.

[0380] The processing flow will be explained below.

[0381] Step 1:

[0382] The server loads the traffic data and converts it into a Pandas dataframe, which contains the latitude and longitude of each point.

[0383] Step 2:

[0384] The server applies the K-means clustering algorithm to the loaded traffic data, which classifies the data points into five clusters. Each data point is given a 'cluster' column with the cluster identifier to which it belongs.

[0385] Step 3:

[0386] The server calculates the average latitude and longitude of each cluster and determines the center point (centroid) of each cluster. This centroid becomes the base point for the optimal transportation route.

[0387] Step 4:

[0388] The server creates a list of predefined sustainable transport modes (bus, e-bike, tram, etc.), then randomly selects and recommends a sustainable transport mode based on the value of the 'cluster' column for each data point, and adds this recommendation to a new 'recommended_mode' column in the data frame.

[0389] Step 5:

[0390] The server records and stores information on optimized travel routes and sustainable transportation modes in a data frame, which is then provided to devices and users via APIs and dashboards as needed.

[0391] Example 1

[0392] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0393] Traffic congestion and environmental impacts are major problems in modern urban transportation systems. To address these issues, there is a need for methods to improve traffic efficiency and reduce environmental impacts by effectively analyzing traffic data, generating optimal transportation routes, and recommending sustainable transportation modes.

[0394] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0395] In this invention, the server includes means for collecting traffic data and converting it into a data frame, means for analyzing the collected traffic data using a clustering algorithm, and means for calculating the average latitude and longitude of data points belonging to each cluster, thereby enabling effective analysis of the traffic data, generation of optimal traffic routes, and recommendation of sustainable transportation modes.

[0396] "Transportation Data" refers to location information, including latitude and longitude, of each point within a city and includes those data points.

[0397] "Data frame" refers to a tabular data structure that structures data in a matrix format to make it easier to manage.

[0398] A "clustering algorithm" is a computational method for dividing data points into groups based on similarity, and in the present invention primarily refers to K-means clustering.

[0399] A "cluster" refers to a group of data points that results from using a clustering algorithm.

[0400] "Average latitude and longitude" means the sum of the latitude and longitude values ​​of all data points belonging to a particular cluster, divided by the number of data points.

[0401] A "transportation route" refers to the optimal route for traveling from one point to another, and is determined taking into account the most efficient means of transportation and time efficiency.

[0402] "Sustainable transportation" refers to transportation that reduces environmental impact and minimizes energy consumption, and examples include buses, electric bicycles, and trams.

[0403] The present invention is a system that analyzes traffic data, generates optimal traffic routes, and recommends sustainable transportation modes. This system performs the following specific processes, centered on a server:

[0404] First, the server collects traffic data, including the latitude and longitude of each point in the city, collected via GPS devices and smartphone applications. The collected data is then converted into a data frame for easy handling. This conversion is performed using the Python Pandas library.

[0405] The server then applies a clustering algorithm to the collected traffic data to analyze traffic patterns. This analysis process uses the Scikit-learn library to run the K-means clustering algorithm, which divides data points into groups based on similarity. The analysis results can be classified into areas that are congested during rush hour and areas that are congested even during normal times.

[0406] The server then calculates the average latitude and longitude of the data points in each cluster, which gives the center point (centroid) of each cluster. The calculated centroid is recorded in a new data frame and used to generate optimal traffic routes.

[0407] The server then identifies the optimal mode for each cluster based on a predefined list of sustainable modes (e.g., bus, e-bike, tram, etc.). The selected mode is randomly assigned and finally recorded as a new column in the data frame.

[0408] For example, transportation data is collected from the latitude and longitude of five points in a city. This data is loaded onto a server and the K-means clustering algorithm is applied to divide each data point into five clusters. The average latitude and longitude values ​​of each cluster are then calculated to determine the optimal transportation route. Finally, the optimal sustainable transportation mode is recommended for each cluster and the information is recorded in a data frame.

[0409] For example, the following can be entered as a prompt to a generative AI model:

[0410] "Please describe the processing steps of a system that uses transportation data (latitude and longitude) from five locations in a city to generate optimal transportation routes using K-means clustering and recommend sustainable transportation modes. These steps should include loading transportation data, analyzing traffic patterns, calculating cluster centers, and recommending sustainable transportation modes. Also, please include the specific actions taken at each step."

[0411] In this way, the system of the present invention can create an efficient and eco-friendly public transport system, contributing to reducing the carbon footprint of cities.

[0412] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0413] Step 1:

[0414] Collecting and loading traffic data

[0415] The server collects traffic data, including the latitude and longitude of each point in the city, obtained from GPS devices and smartphone applications.

[0416] Input: Latitude and longitude data for each location

[0417] Output: Data frame

[0418] The server uses Python's Pandas library to convert the collected data into a data frame, which is then used in subsequent analysis steps.

[0419] Step 2:

[0420] Traffic pattern analysis

[0421] The server applies a clustering algorithm to the collected traffic data, in this case the K-means clustering algorithm.

[0422] Input: DataFrame

[0423] Output: Data frame with cluster identifiers for each data point

[0424] The server uses the Scikit-learn library to perform K-means clustering to classify data points into clusters based on their similarity.

[0425] Step 3:

[0426] Calculating cluster centroids

[0427] The server calculates the average latitude and longitude of the data points that belong to each cluster.

[0428] Input: Data frame with cluster identifiers

[0429] Output: A data frame representing the centroids of each cluster

[0430] The server uses the Pandas library to calculate the average latitude and longitude values ​​for each cluster and record them in a new data frame.

[0431] Step 4:

[0432] Generate optimal transportation routes

[0433] The server generates an optimal traffic route based on the calculated cluster centroid.

[0434] Input: A data frame representing the centroids of each cluster

[0435] Output: Data frame containing optimal transportation routes

[0436] The server uses an algorithm to determine the best route between centroids, taking into account distance and travel time efficiency.

[0437] Step 5:

[0438] Promoting sustainable transportation

[0439] The server recommends the best mode of transport for each cluster based on a predefined list of sustainable modes of transport (e.g., bus, e-bike, tram, etc.).

[0440] Input: A data frame containing optimal transit routes

[0441] Output: A data frame with a new column of sustainable transport recommendations

[0442] The server assigns the optimal transportation mode to each cluster randomly or based on specific conditions and records the information in a data frame.

[0443] In this way, the server analyzes traffic data and recommends optimal routes and sustainable transportation methods. This process improves urban traffic efficiency and creates an eco-friendly environment.

[0444] (Application example 1)

[0445] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0446] In recent years, traffic congestion and environmental pollution have become serious problems in urban areas. Furthermore, conventional methods often do not achieve sufficient accuracy when selecting optimal transportation routes or recommending sustainable transportation modes. Furthermore, there are challenges in analyzing traffic data in real time and proposing optimal routes using the results.

[0447] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0448] In this invention, the server includes means for analyzing traffic data using an AI algorithm, means for optimizing traffic routes based on the analyzed traffic data, means for recommending sustainable transportation modes based on the optimized traffic routes, means for analyzing traffic patterns using a K-means clustering algorithm, and means for displaying optimal routes in an application installed on a smartphone. This makes it possible to alleviate traffic congestion and reduce environmental pollution in urban areas, while also enabling highly accurate route suggestions in real time.

[0449] An "AI algorithm" is a calculation method that uses artificial intelligence technology to analyze data and make predictions.

[0450] "Traffic Data" means a collection of information including traffic conditions, such as latitude, longitude, time, and other traffic-related data.

[0451] A "means for optimizing traffic routes" is a method for calculating the most efficient travel route based on collected traffic data.

[0452] "Sustainable transportation" refers to transportation that can be operated over the long term while minimizing the burden on the environment. Examples include electric vehicles and carpooling.

[0453] A "traffic pattern" is a characteristic that indicates the traffic conditions and their fluctuations in a particular area or time period.

[0454] The "K-means clustering algorithm" is a machine learning algorithm for classifying data into K clusters, and is widely used in analyzing traffic data.

[0455] An "application installed on a smartphone" is software that runs on a smartphone device and provides functionality through a user interface.

[0456] A "data frame" is a data structure for managing a collection of data consisting of rows and columns, and is used for analysis and manipulation.

[0457] "Means for collecting traffic data in real time" refers to a method for instantly obtaining current traffic conditions.

[0458] "Means for displaying optimal route" means a method for visually showing the calculated optimal travel route to the user.

[0459] System program generation

[0460] The system for realizing the present invention is implemented through the following steps.

[0461] 1. Loading traffic data

[0462] The server collects traffic data from storage such as a database and converts it into a data frame using a data frame library such as Pandas. For example, PostgreSQL is used to query traffic data including latitude, longitude, and time, and read it as a data frame.

[0463] 2. Traffic pattern analysis

[0464] The server uses the K-means clustering algorithm to analyze the collected traffic data. It uses the scikit-learn library to classify data points into clusters that indicate specific traffic patterns.

[0465] 3. Calculating the cluster center (centroid)

[0466] The server calculates the average latitude and longitude of all data points within each cluster, which gives the center point (centroid) of each cluster, and then plans optimal traffic routes based on that.

[0467] 4. Promoting sustainable transportation

[0468] The server recommends a sustainable transportation mode for each cluster by randomly selecting it from the sustainable transportation mode list and recording it in a data frame.

[0469] 5. Display on smartphone applications

[0470] The application installed on the user's smartphone receives information on optimal routes and sustainable transportation methods sent from the server and displays it in real time. It is preferable to have a flat user interface, for example, to display the route on a map using Google Maps API.

[0471] Hardware and software used

[0472] Hardware: Servers, smartphones, network devices

[0473] Software: Pandas, scikit-learn, PostgreSQL, Flask, Google Maps API

[0474] Data processing and calculation

[0475] 1. Processing of traffic data

[0476] Querying from the database

[0477] Dataframe transformation with Pandas

[0478] 2. Traffic pattern calculation

[0479] Classification by K-means clustering

[0480] Calculate the centroid of each cluster

[0481] 3. Selecting sustainable transportation options

[0482] Update a dataframe with random selection

[0483] Specific examples

[0484] The operation of the system will be explained in detail through the following concrete example.

[0485] When a user searches for a route from "Shinjuku Station" to "Tokyo Tower," the system performs the following process.

[0486] 1. Loading traffic data

[0487] The server loads the traffic data for "Shinjuku-ku" and "Minato-ku" with the following query:

[0488] SELECT latitude, longitude, timestamp FROM traffic_data WHERE region IN ('Shinjuku', 'Minato');

[0489] 2. Traffic pattern analysis

[0490] Using the loaded data, apply K-means clustering as follows:

[0491] KMeans(n_clusters=5).fit(data_frame)

[0492] 3. Cluster centroid calculation

[0493] Calculate the center point of each cluster and get the optimal route as follows:

[0494] data_frame.groupby('cluster').mean()

[0495] 4. Promoting sustainable transportation

[0496] Randomly select electric vehicles or carpools for each cluster and add them to the dataframe as follows:

[0497] data_frame['sustainable_transport'] = np.random.choice(['electric_car', 'carpool'], len(data_frame))

[0498] 5. Display on smartphone applications

[0499] The calculated route and recommended transportation methods are displayed on a map using the Google Maps API, providing a visual representation to the user.

[0500] In this way, the system of the present invention can provide an efficient and sustainable means of transportation and contribute to improving traffic problems in urban areas.

[0501] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0502] Step 1: Loading traffic data

[0503] The server queries the traffic data from the database and converts it into a Pandas dataframe. This query retrieves traffic data for a specified area, including latitude, longitude, and time. For example, it executes the following SQL query:

[0504] SELECT latitude, longitude, timestamp FROM traffic_data WHERE region IN ('Shinjuku', 'Minato');

[0505] Input: Database

[0506] Output: Pandas dataframe

[0507] Step 2: Analyze traffic patterns

[0508] The server analyzes the loaded traffic data using the K-means clustering algorithm to classify data points into multiple clusters. It uses the scikit-learn library to perform the clustering. Specifically, it applies K-means clustering as follows:

[0509] from sklearn.cluster import KMeans

[0510] kmeans = KMeans(n_clusters=5).fit(data_frame)

[0511] data_frame['cluster'] = kmeans.labels_

[0512] Input: Pandas dataframe

[0513] Output: Data frame with cluster identifiers

[0514] Step 3: Calculate the cluster centers (centroids)

[0515] The server calculates the average latitude and longitude within each cluster to determine the centroid, which is the center point of the cluster and is used to set the optimal traffic route. Specifically, it calculates as follows:

[0516] centroids = data_frame.groupby('cluster').mean()

[0517] Input: Data frame with cluster identifiers

[0518] Output: Centroid data frame for each cluster

[0519] Step 4: Promote sustainable transportation

[0520] The server randomly selects a sustainable transportation mode for each cluster and records it in a data frame. Sustainable transportation modes include electric vehicles and carpooling. Specifically, the following settings are used:

[0521] import numpy as np

[0522] data_frame['sustainable_transport'] = np.random.choice(['electric_car', 'carpool'], len(data_frame))

[0523] Input: Centroid data frame of clusters

[0524] Output: A data frame containing sustainable transport modes

[0525] Step 5: Display on smartphone application

[0526] The smartphone device receives information on optimal routes and sustainable transportation methods sent from the server and displays it on a map in real time. It uses the Google Maps API to provide visual route information. Specifically, it sets markers on the map as follows:

[0527] map.addMarker(new google.maps.Marker({

[0528] position: {lat: 35.6895, lng: 139.6917}, / / Example coordinates

[0529] map: map,

[0530] title: 'Start Point'

[0531] }));

[0532] Input: A data frame containing sustainable transport modes

[0533] Output: A smartphone application that displays the optimal route and transportation options

[0534] Through the application, users can view the best routes and sustainable transportation options in real time, enabling them to travel eco-friendly.

[0535] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0536] The present invention combines a system for analyzing traffic data, generating optimal traffic routes, and recommending sustainable transportation modes with an emotion engine that recognizes user emotions. This system is implemented in the following configuration, including a server, a terminal, and a user.

[0537] System program processing contents

[0538] Loading traffic data

[0539] The server loads the traffic data and converts it into a Pandas data frame, which contains the latitude and longitude of each point, so that the system can access the latitude and longitude of each point within the city.

[0540] Traffic pattern analysis

[0541] The server applies the K-means clustering algorithm to the loaded traffic data, which classifies the data points into five clusters. Each data point is given a 'cluster' column with the cluster identifier to which it belongs.

[0542] Calculating cluster centroids

[0543] The server calculates the average latitude and longitude of all data points within each cluster, and determines the center point (centroid) of each cluster. This centroid becomes the base point for the optimal traffic route.

[0544] Promoting sustainable transportation

[0545] The server creates a list of predefined sustainable transport modes (bus, e-bike, tram, etc.), then randomly selects and recommends a sustainable transport mode based on the value of the 'cluster' column for each data point, and adds this recommendation to a new 'recommended_mode' column in the data frame.

[0546] User sentiment analysis

[0547] The emotion engine recognizes and analyzes the user's emotions. User emotion data is collected, for example, from the user's smartphone or other devices. This can be done using technologies such as facial recognition, voice analysis, and text analysis. This emotion analysis helps identify which transportation modes the user has positive or negative feelings about.

[0548] Emotion-based optimization of sustainable transportation

[0549] The server integrates the emotion data obtained from the emotion engine into the analysis results and further optimizes the recommendation of sustainable transportation modes based on the emotion data.For example, if a user has a strong preference for a particular transportation mode based on the user's emotion data, the server prioritizes the recommendation of that transportation mode.

[0550] For example, transportation data is collected from the latitude and longitude of five points in a city. This data is loaded into a server and the K-means clustering algorithm is applied to divide each data point into five clusters. Next, the average latitude and longitude of each cluster is calculated and the optimal transportation route is determined based on this. After that, the optimal sustainable transportation mode is recommended for each cluster and the information is recorded in a data frame.

[0551] Furthermore, when a user sends emotional data to the server via their smartphone, the emotion engine analyzes the data and, based on the analysis results, recommends a more appropriate mode of transportation that matches the emotional data.

[0552] In this way, the system of the present invention can build an efficient and eco-friendly public transportation system, contributing to reducing the carbon footprint of cities. Furthermore, by taking into account the user's emotions, it can provide an environment where users can travel without feeling stressed.

[0553] The processing flow will be explained below.

[0554] Step 1:

[0555] The server loads the traffic data and converts it into a Pandas data frame, which contains the latitude and longitude of each point, allowing the system to capture the location of each point within the city.

[0556] Step 2:

[0557] The server applies the K-means clustering algorithm to the loaded traffic data, which classifies the data points into five clusters. Each data point is given a 'cluster' column with the cluster identifier to which it belongs.

[0558] Step 3:

[0559] The server calculates the average latitude and longitude of all data points within each cluster, and determines the center point (centroid) of each cluster. This centroid becomes the base point for the optimal traffic route.

[0560] Step 4:

[0561] The server creates a list of sustainable transport modes (bus, e-bike, tram, etc.). It then randomly selects and recommends a sustainable transport mode based on the value of the 'cluster' column for each data point. This recommendation is added to a new 'recommended_mode' column in the data frame.

[0562] Step 5:

[0563] The device collects emotional data from the user. This emotional data is acquired using, for example, the smartphone's camera or microphone. The emotional data includes facial expressions, voice tone, text content, and so on.

[0564] Step 6:

[0565] The device transmits the collected emotion data to a server, which then analyzes the user's emotions using an emotion engine and obtains the analysis results.

[0566] Step 7:

[0567] The server integrates the emotion data obtained from the emotion engine with the transportation data. Based on the user's emotion data, the server further optimizes the recommendation of sustainable transportation modes. For example, if a user has a favorable emotion toward a particular transportation mode, the server prioritizes the recommendation of that mode.

[0568] Step 8:

[0569] The server records and stores information on optimized travel routes and sustainable transportation modes in a data frame, which is then provided to devices and users via APIs and dashboards as needed.

[0570] For example, traffic data is collected from the latitude and longitude of five points in a city. This data is loaded onto a server and the K-means clustering algorithm is applied to divide each data point into five clusters. Next, the average latitude and longitude of each cluster is calculated, and the optimal traffic route is determined based on this.

[0571] The device then collects the user's emotional data and sends it to the server. The server analyzes the emotional data and optimizes the recommendation of sustainable transportation methods based on the results of the emotional analysis. The recommended transportation methods based on the emotional data are recorded in a data frame and provided to the user as necessary information. This reduces the city's carbon footprint and provides an environment where users can travel without stress.

[0572] Example 2

[0573] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0574] There is a need to efficiently analyze transportation data, determine optimal transportation routes, and effectively recommend sustainable transportation modes. It is also necessary to provide a transportation system that is more convenient and satisfies users by optimizing transportation modes based on user emotions. However, while current systems are capable of analyzing transportation data and selecting sustainable transportation modes, they do not adequately optimize transportation modes based on user emotions. This leads to lower user satisfaction and efficiency, and hinders the promotion of sustainable transportation modes.

[0575] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0576] In this invention, the server includes means for collecting traffic data and converting it into a data frame format, means for applying a clustering algorithm to the converted traffic data to classify data points into clusters, means for calculating the center point (average latitude and longitude) of each cluster, means for randomly selecting sustainable transportation modes and recommending the transportation modes, means for analyzing user emotion data, and means for optimizing the recommended transportation modes based on the emotion data. This not only enables efficient analysis of traffic data and recommendation of sustainable transportation modes, but also enables optimization of transportation modes based on user emotion.

[0577] "Traffic data" refers to data that includes various information related to traffic conditions, such as latitude, longitude, traffic volume, speed, and time of day.

[0578] The "data frame format" is a two-dimensional data structure with rows and columns, and is a format for representing tabular data used in Python's Pandas library.

[0579] A "clustering algorithm" is an algorithm for classifying data points into groups (clusters) that share common characteristics.

[0580] A "cluster" is a collection of data points that share common characteristics, grouped together using a clustering algorithm.

[0581] A "centroid" is a point that represents the average location of all data points within a cluster.

[0582] "Sustainable transport" refers to eco-friendly transport methods (e.g. buses, electric bicycles, trams, etc.) that aim to reduce carbon footprints and protect the environment.

[0583] "Emotional data" is data that indicates a user's emotional state and is collected using technologies such as facial recognition, voice analysis, and text analysis.

[0584] An "emotion engine" is a computer program or algorithm that analyzes a user's emotional data and identifies their emotional state.

[0585] "Optimization" is the process of arranging resources and conditions most effectively to achieve a particular objective.

[0586] The present invention is a system that combines multiple pieces of hardware and software to analyze traffic data, generate optimal traffic routes, and recommend sustainable transportation modes. Specific embodiments are described below.

[0587] First, the server collects traffic data and converts it into a data frame using Python's Pandas library. This data includes the latitude and longitude of each location. Next, the server applies a clustering algorithm using the Python library Sci-kit Learn to classify the data points into clusters. During this process, each data point is assigned a cluster identifier.

[0588] The server calculates the average latitude and longitude of data points in each cluster and uses this as the centroid, which represents the cluster. The server then creates a list of sustainable transportation modes (e.g., bus, electric bicycle, tram, etc.) and randomly selects and recommends a sustainable transportation mode based on each cluster identifier. This recommendation information is added to a new column 'recommended_mode' in the data frame.

[0589] The server also collects emotion data from the user's device. This emotion data is collected using technologies such as facial recognition, voice analysis, and text analysis. For example, facial recognition is performed using technologies such as OpenCV and Google Cloud Vision API, and emotions are analyzed from the user's facial expressions. Based on the collected emotion data, the emotion engine analyzes the user's emotions and sends them to the server.

[0590] The server integrates the emotion data obtained from the emotion engine with the analysis results and optimizes the recommendation of sustainable transportation modes based on the emotion data. For example, if a user has a strong preference for a particular transportation mode, the recommendation of that mode will be prioritized, improving the accuracy of the recommendation.

[0591] For example, the latitude and longitude data of five points in a city are collected as transportation data and loaded onto a server. The K-means clustering algorithm is applied to this data to classify each data point into five clusters. Next, the average latitude and longitude values ​​of each cluster are calculated, and the optimal transportation route is determined based on this. After that, a sustainable transportation mode is randomly selected for each cluster, recommended, and the information is recorded in a data frame.

[0592] For example, a server receives emotional data sent by a user from a smartphone and performs an emotional analysis. Based on the analysis results, the system recommends the user's preferred mode of transportation. Through this process, the system of the present invention supports the creation of an efficient and eco-friendly public transportation system, contributing to reducing a city's carbon footprint and improving user satisfaction.

[0593] Example prompts for generative AI models:

[0594] "Using the following location data, generate code that generates optimal transportation routes and recommends sustainable modes of transportation. Additionally, include a section that analyzes user sentiment data to optimize recommended modes of transportation."

[0595] In this way, the embodiments of the present invention enable efficient analysis of traffic data, recommendation of sustainable transportation modes, and even optimization of transportation modes based on user sentiment data, thereby improving the efficiency of the overall transportation system and user satisfaction.

[0596] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0597] Step 1:

[0598] Traffic data collection and conversion

[0599] The server collects traffic data from the Internet or an internal database. For example, it uses an API to obtain traffic data and receives a dataset containing information such as latitude and longitude. It then converts this data into a data frame using the Python Pandas library. The input is raw traffic data, and the output is the traffic data converted into a data frame.

[0600] Specific actions

[0601] The server retrieves the traffic data from the API and then converts it into a data frame using Pandas, which contains the latitude and longitude of each point.

[0602] Step 2:

[0603] Traffic pattern analysis

[0604] The server applies Sci-Kit Learn's K-means clustering algorithm to the converted traffic data. The input is traffic data in a data frame format, and the output is the clustered data. This classifies each data point into a cluster and assigns a cluster identifier to each data point.

[0605] Specific actions

[0606] The server applies K-means clustering and assigns a cluster identifier to each data point, and the results are added as a new column in the data frame.

[0607] Step 3:

[0608] Calculating cluster centroids

[0609] The server calculates the average latitude and longitude within each cluster and defines this as the centroid. The input is traffic data with cluster identifiers, and the output is the centroid position of each cluster.

[0610] Specific actions

[0611] The server averages the latitude and longitude of all data points within each cluster and records this as the center point of each cluster in the data frame.

[0612] Step 4:

[0613] Promoting sustainable transportation

[0614] The server prepares a list of sustainable transport modes (e.g., buses, e-bikes, trams) and randomly selects one based on each cluster identifier. The input is the transport data with calculated centroids, and the output is the recommended sustainable transport mode.

[0615] Specific actions

[0616] The server randomly selects from the list of sustainable transportation options and adds the recommendations to a new column in the data frame.

[0617] Step 5:

[0618] User sentiment analysis

[0619] The emotion engine analyzes emotion data collected from the user's device. The input is the results of facial recognition, voice analysis, and text analysis sent from the user's device, and the output is the analyzed emotion data.

[0620] Specific actions

[0621] The user's device collects emotional data through a camera and microphone and sends it to a server, which then uses an emotion engine to analyze it and identify the user's emotional state.

[0622] Step 6:

[0623] Emotion-based optimization of sustainable transportation

[0624] The server integrates the emotion data obtained from the emotion engine with the analysis results to optimize the recommendation of sustainable transportation modes. The input is the emotion data from the emotion engine and existing recommendation information, and the output is the recommended transportation modes optimized based on the emotion data.

[0625] Specific actions

[0626] The server updates the recommendation list based on the user's preferred mode of transportation based on the emotion data. For example, if the user prefers electric bicycles, electric bicycles will be prioritized in the recommendation list.

[0627] (Application example 2)

[0628] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0629] Optimizing transportation routes and recommending sustainable modes of transportation are important for reducing a city's carbon footprint and providing efficient mobility. However, existing systems do not take into account the user's emotional state, which can lead to lower user satisfaction and stress levels. This reduces users' willingness to use the recommended modes of transportation, resulting in a problem of reduced effectiveness of the overall system.

[0630] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0631] In this invention, the server includes means for analyzing traffic data using an AI algorithm, means for optimizing a traffic route based on the analyzed traffic data, means for recommending a sustainable means of transportation based on the optimized traffic route, emotion recognition means for analyzing a user's emotion, and means for optimizing the recommended sustainable means of transportation based on the user's emotion data. This allows the server to recommend the optimal means of transportation according to the user's emotional state, thereby improving user satisfaction and system efficiency.

[0632] Key Word Definitions

[0633] An "AI algorithm" is a computational procedure that uses artificial intelligence technology to analyze data and discover specific patterns.

[0634] "Traffic data" is a collection of data containing information related to traffic, such as location information and traffic conditions.

[0635] "Means for optimizing traffic routes" refers to a method for calculating optimal routes based on analyzed traffic data.

[0636] "Sustainable transport" is transport designed to minimise environmental impact, including public transport and electric vehicles.

[0637] "Emotion recognition means" refers to technology for detecting and analyzing a user's emotions, and uses techniques such as facial recognition and voice analysis.

[0638] "User emotion data" is data that indicates the user's emotional state, and is acquired in the form of an image, sound, text, or the like.

[0639] The "means for optimizing sustainable transportation" is a method for adjusting recommendations for sustainable transportation based on user sentiment data.

[0640] A "data frame" is a two-dimensional data structure consisting of rows and columns, used for data processing and analysis.

[0641] "Location data" is information that indicates the latitude and longitude of a specific point.

[0642] MODE FOR CARRYING OUT THE INVENTION

[0643] This invention relates to a system that uses AI algorithms to analyze traffic data and recommend optimal routes and sustainable transportation methods based on user emotion data. This system is implemented with the following components, including a server, a terminal, and a user.

[0644] 1. Loading and analyzing traffic data

[0645] The server loads and analyzes traffic data, which includes information such as location and traffic conditions. The server manages the traffic data using a Pandas data frame and classifies the data points using the K-means clustering algorithm.

[0646] 2. Traffic route optimization

[0647] The server generates the optimal traffic route based on the analyzed traffic data. At this stage, it calculates the average value of the location data (latitude and longitude) of each cluster to determine the centroid. This allows the optimal traffic route to be designed.

[0648] 3. Promoting sustainable transportation

[0649] The server recommends sustainable transportation modes based on the optimized transportation route. The recommended transportation modes are randomly selected from a predefined list. The recommended transportation modes are recorded in a data frame.

[0650] 4. Emotion analysis

[0651] The server collects emotion data from users' smartphones and other devices and analyzes it using an emotion engine, which uses technologies such as facial recognition and voice analysis to recognize the user's emotions.

[0652] 5. Emotion-Based Optimization

[0653] The server optimizes the recommended sustainable transportation modes based on the sentiment data: if the user has a positive sentiment towards a particular transportation mode, it prioritizes the recommendation of that mode.

[0654] Specific examples

[0655] Transportation data is collected from multiple locations in a city using latitude and longitude data. This data is loaded onto a server and the K-means clustering algorithm is applied to separate the data points into clusters. The average latitude and longitude of each cluster is calculated to determine the centroid. The optimal sustainable transportation mode (e.g., electric bus or electric bicycle) is then recommended for each cluster and the information is recorded in a data frame.

[0656] Furthermore, when users send emotion data to the server via their smartphones, the emotion engine analyzes the data and re-recommends the optimal sustainable transportation mode based on the emotion data. In this way, the system can provide the optimal transportation mode that improves user satisfaction.

[0657] Prompt Sentence Examples

[0658] This includes examples of inputting prompts like the following into a generative AI model:

[0659] Create a system that suggests sustainable transportation options based on the user's emotional data ('happy', 'relaxed', etc.). For example:

[0660] If the user's emotion is "happy," suggest an electric bus.

[0661] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0662] System processing flow

[0663] Step 1:

[0664] The server loads the traffic data. The traffic data is read from a CSV file and converted into a Pandas data frame. The input at this stage is traffic data including location information and traffic conditions, and the output is traffic data in data frame format.

[0665] Step 2:

[0666] The server applies the K-means clustering algorithm to the loaded traffic data. This process classifies each data point into a specific cluster. The input is the data frame obtained in step 1, and the output is a data frame with the cluster identifiers added.

[0667] Step 3:

[0668] The server calculates the average latitude and longitude of each cluster, which determines the center point (centroid) of the cluster. The input is the data frame with cluster identifiers obtained in step 2, and the output is the centroid position data of each cluster.

[0669] Step 4:

[0670] The server recommends sustainable transportation modes based on the optimized transportation route. A predefined list of sustainable transportation modes (e.g., electric bus, electric bicycle, tram) is used in this stage. The input is the centroid location data for each cluster, and the output is a data frame with the recommended transportation modes added.

[0671] Step 5:

[0672] The device collects the user's emotional data. This emotional data is obtained from smartphones and other devices and uses technologies such as facial recognition and voice analysis. The input is the user's biometric data and voice data, and the output is analyzed emotional data.

[0673] Step 6:

[0674] The server analyzes the user's emotional data using an emotion engine, which identifies the user's current emotional state. The input is the emotional data obtained in step 5, and the output is the user's emotional state (e.g., happy, relaxed, etc.).

[0675] Step 7:

[0676] The server optimizes the recommended sustainable transportation modes based on the user's emotional data. For example, if the user is happy, it preferentially recommends electric buses. The input is the emotional state obtained in step 6 and the recommended transportation modes obtained in step 4, and the output is a data frame with the optimized transportation modes added based on the emotional state.

[0677] Specifically, the smartphone app captures the user's face with a camera and sends the image data to the server. The server then performs emotion recognition and analyzes the user's emotional state in real time. It then suggests an appropriate transportation method and sends a notification to the user's smartphone.

[0678] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0679] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0680] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0681] [Third embodiment]

[0682] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0683] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0684] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0685] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0686] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0687] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0688] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0689] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0690] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0691] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0692] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0693] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0694] The present invention provides a system for analyzing traffic data, generating optimal traffic routes, and recommending sustainable transportation modes. This system is implemented with the following configuration, centered around a server.

[0695] System program processing contents

[0696] 1. Loading traffic data

[0697] The server collects traffic data (including latitude and longitude) and converts it into a data frame, which reflects actual traffic conditions and contains location data for each point within a city.

[0698] 2. Traffic pattern analysis

[0699] The server analyzes the collected traffic data using the K-means clustering algorithm. This analysis classifies the traffic data into several clusters. Each cluster is a set of locations with similar traffic patterns. As a result of the analysis, each data point is assigned a cluster identifier to which it belongs.

[0700] 3. Calculating the cluster center (centroid)

[0701] The server calculates the average latitude and longitude of all data points within each cluster, which gives the center point (centroid) of each cluster, allowing for the generation of optimal travel routes that take into account distance and time efficiency.

[0702] 4. Promoting sustainable transportation

[0703] The server randomly recommends the best mode for each cluster based on a predefined list of sustainable modes (e.g., bus, e-bike, tram, etc.) and records the recommended mode as a new column in the data frame.

[0704] As a concrete example, the process is as follows: For example, transportation data is collected as latitude and longitude data for five points in a city. This data is loaded into a server and the K-means clustering algorithm is applied to divide each data point into five clusters. Next, the average latitude and longitude values ​​for each cluster are calculated and the optimal transportation route is determined based on this. After that, the optimal sustainable transportation mode is recommended for each cluster and the information is recorded in a data frame.

[0705] In this way, the system of the present invention can create an efficient and eco-friendly public transport system, contributing to reducing the carbon footprint of cities.

[0706] The processing flow will be explained below.

[0707] Step 1:

[0708] The server loads the traffic data and converts it into a Pandas dataframe, which contains the latitude and longitude of each point.

[0709] Step 2:

[0710] The server applies the K-means clustering algorithm to the loaded traffic data, which classifies the data points into five clusters. Each data point is given a 'cluster' column with the cluster identifier to which it belongs.

[0711] Step 3:

[0712] The server calculates the average latitude and longitude of each cluster and determines the center point (centroid) of each cluster. This centroid becomes the base point for the optimal transportation route.

[0713] Step 4:

[0714] The server creates a list of predefined sustainable transport modes (bus, e-bike, tram, etc.), then randomly selects and recommends a sustainable transport mode based on the value of the 'cluster' column for each data point, and adds this recommendation to a new 'recommended_mode' column in the data frame.

[0715] Step 5:

[0716] The server records and stores information on optimized travel routes and sustainable transportation modes in a data frame, which is then provided to devices and users via APIs and dashboards as needed.

[0717] Example 1

[0718] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0719] Traffic congestion and environmental impacts are major problems in modern urban transportation systems. To address these issues, there is a need for methods to improve traffic efficiency and reduce environmental impacts by effectively analyzing traffic data, generating optimal transportation routes, and recommending sustainable transportation modes.

[0720] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0721] In this invention, the server includes means for collecting traffic data and converting it into a data frame, means for analyzing the collected traffic data using a clustering algorithm, and means for calculating the average latitude and longitude of data points belonging to each cluster, thereby enabling effective analysis of the traffic data, generation of optimal traffic routes, and recommendation of sustainable transportation modes.

[0722] "Transportation Data" refers to location information, including latitude and longitude, of each point within a city and includes those data points.

[0723] "Data frame" refers to a tabular data structure that structures data in a matrix format to make it easier to manage.

[0724] A "clustering algorithm" is a computational method for dividing data points into groups based on similarity, and in the present invention primarily refers to K-means clustering.

[0725] A "cluster" refers to a group of data points that results from using a clustering algorithm.

[0726] "Average latitude and longitude" means the sum of the latitude and longitude values ​​of all data points belonging to a particular cluster, divided by the number of data points.

[0727] A "transportation route" refers to the optimal route for traveling from one point to another, and is determined taking into account the most efficient means of transportation and time efficiency.

[0728] "Sustainable transportation" refers to transportation that reduces environmental impact and minimizes energy consumption, and examples include buses, electric bicycles, and trams.

[0729] The present invention is a system that analyzes traffic data, generates optimal traffic routes, and recommends sustainable transportation modes. This system performs the following specific processes, centered on a server:

[0730] First, the server collects traffic data, including the latitude and longitude of each point in the city, collected via GPS devices and smartphone applications. The collected data is then converted into a data frame for easy handling. This conversion is performed using the Python Pandas library.

[0731] The server then applies a clustering algorithm to the collected traffic data to analyze traffic patterns. This analysis process uses the Scikit-learn library to run the K-means clustering algorithm, which divides data points into groups based on similarity. The analysis results can be classified into areas that are congested during rush hour and areas that are congested even during normal times.

[0732] The server then calculates the average latitude and longitude of the data points in each cluster, which gives the center point (centroid) of each cluster. The calculated centroid is recorded in a new data frame and used to generate optimal traffic routes.

[0733] The server then identifies the optimal mode for each cluster based on a predefined list of sustainable modes (e.g., bus, e-bike, tram, etc.). The selected mode is randomly assigned and finally recorded as a new column in the data frame.

[0734] For example, transportation data is collected from the latitude and longitude of five points in a city. This data is loaded onto a server and the K-means clustering algorithm is applied to divide each data point into five clusters. The average latitude and longitude values ​​of each cluster are then calculated to determine the optimal transportation route. Finally, the optimal sustainable transportation mode is recommended for each cluster and the information is recorded in a data frame.

[0735] For example, the following can be entered as a prompt to a generative AI model:

[0736] "Please describe the processing steps of a system that uses transportation data (latitude and longitude) from five locations in a city to generate optimal transportation routes using K-means clustering and recommend sustainable transportation modes. These steps should include loading transportation data, analyzing traffic patterns, calculating cluster centers, and recommending sustainable transportation modes. Also, please include the specific actions taken at each step."

[0737] In this way, the system of the present invention can create an efficient and eco-friendly public transport system, contributing to reducing the carbon footprint of cities.

[0738] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0739] Step 1:

[0740] Collecting and loading traffic data

[0741] The server collects traffic data, including the latitude and longitude of each point in the city, obtained from GPS devices and smartphone applications.

[0742] Input: Latitude and longitude data for each location

[0743] Output: Data frame

[0744] The server uses Python's Pandas library to convert the collected data into a data frame, which is then used in subsequent analysis steps.

[0745] Step 2:

[0746] Traffic pattern analysis

[0747] The server applies a clustering algorithm to the collected traffic data, in this case the K-means clustering algorithm.

[0748] Input: DataFrame

[0749] Output: Data frame with cluster identifiers for each data point

[0750] The server uses the Scikit-learn library to perform K-means clustering to classify data points into clusters based on their similarity.

[0751] Step 3:

[0752] Calculating cluster centroids

[0753] The server calculates the average latitude and longitude of the data points that belong to each cluster.

[0754] Input: Data frame with cluster identifiers

[0755] Output: A data frame representing the centroids of each cluster

[0756] The server uses the Pandas library to calculate the average latitude and longitude values ​​for each cluster and record them in a new data frame.

[0757] Step 4:

[0758] Generate optimal transportation routes

[0759] The server generates an optimal traffic route based on the calculated cluster centroid.

[0760] Input: A data frame representing the centroids of each cluster

[0761] Output: Data frame containing optimal transportation routes

[0762] The server uses an algorithm to determine the best route between centroids, taking into account distance and travel time efficiency.

[0763] Step 5:

[0764] Promoting sustainable transportation

[0765] The server recommends the best mode of transport for each cluster based on a predefined list of sustainable modes of transport (e.g., bus, e-bike, tram, etc.).

[0766] Input: A data frame containing optimal transit routes

[0767] Output: A data frame with a new column of sustainable transport recommendations

[0768] The server assigns the optimal transportation mode to each cluster randomly or based on specific conditions and records the information in a data frame.

[0769] In this way, the server analyzes traffic data and recommends optimal routes and sustainable transportation methods. This process improves urban traffic efficiency and creates an eco-friendly environment.

[0770] (Application example 1)

[0771] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0772] In recent years, traffic congestion and environmental pollution have become serious problems in urban areas. Furthermore, conventional methods often do not achieve sufficient accuracy when selecting optimal transportation routes or recommending sustainable transportation modes. Furthermore, there are challenges in analyzing traffic data in real time and proposing optimal routes using the results.

[0773] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0774] In this invention, the server includes means for analyzing traffic data using an AI algorithm, means for optimizing traffic routes based on the analyzed traffic data, means for recommending sustainable transportation modes based on the optimized traffic routes, means for analyzing traffic patterns using a K-means clustering algorithm, and means for displaying optimal routes in an application installed on a smartphone. This makes it possible to alleviate traffic congestion and reduce environmental pollution in urban areas, while also enabling highly accurate route suggestions in real time.

[0775] An "AI algorithm" is a calculation method that uses artificial intelligence technology to analyze data and make predictions.

[0776] "Traffic Data" means a collection of information including traffic conditions, such as latitude, longitude, time, and other traffic-related data.

[0777] A "means for optimizing traffic routes" is a method for calculating the most efficient travel route based on collected traffic data.

[0778] "Sustainable transportation" refers to transportation that can be operated over the long term while minimizing the burden on the environment. Examples include electric vehicles and carpooling.

[0779] A "traffic pattern" is a characteristic that indicates the traffic conditions and their fluctuations in a particular area or time period.

[0780] The "K-means clustering algorithm" is a machine learning algorithm for classifying data into K clusters, and is widely used in analyzing traffic data.

[0781] An "application installed on a smartphone" is software that runs on a smartphone device and provides functionality through a user interface.

[0782] A "data frame" is a data structure for managing a collection of data consisting of rows and columns, and is used for analysis and manipulation.

[0783] "Means for collecting traffic data in real time" refers to a method for instantly obtaining current traffic conditions.

[0784] "Means for displaying optimal route" means a method for visually showing the calculated optimal travel route to the user.

[0785] System program generation

[0786] The system for realizing the present invention is implemented through the following steps.

[0787] 1. Loading traffic data

[0788] The server collects traffic data from storage such as a database and converts it into a data frame using a data frame library such as Pandas. For example, PostgreSQL is used to query traffic data including latitude, longitude, and time, and read it as a data frame.

[0789] 2. Traffic pattern analysis

[0790] The server uses the K-means clustering algorithm to analyze the collected traffic data. It uses the scikit-learn library to classify data points into clusters that indicate specific traffic patterns.

[0791] 3. Calculating the cluster center (centroid)

[0792] The server calculates the average latitude and longitude of all data points within each cluster, which gives the center point (centroid) of each cluster, and then plans optimal traffic routes based on that.

[0793] 4. Promoting sustainable transportation

[0794] The server recommends a sustainable transportation mode for each cluster by randomly selecting it from the sustainable transportation mode list and recording it in a data frame.

[0795] 5. Display on smartphone applications

[0796] The application installed on the user's smartphone receives information on optimal routes and sustainable transportation methods sent from the server and displays it in real time. It is preferable to have a flat user interface, for example, to display the route on a map using Google Maps API.

[0797] Hardware and software used

[0798] Hardware: Servers, smartphones, network devices

[0799] Software: Pandas, scikit-learn, PostgreSQL, Flask, Google Maps API

[0800] Data processing and calculation

[0801] 1. Processing of traffic data

[0802] Querying from the database

[0803] Dataframe transformation with Pandas

[0804] 2. Traffic pattern calculation

[0805] Classification by K-means clustering

[0806] Calculate the centroid of each cluster

[0807] 3. Selecting sustainable transportation options

[0808] Update a dataframe with random selection

[0809] Specific examples

[0810] The operation of the system will be explained in detail through the following concrete example.

[0811] When a user searches for a route from "Shinjuku Station" to "Tokyo Tower," the system performs the following process.

[0812] 1. Loading traffic data

[0813] The server loads the traffic data for "Shinjuku-ku" and "Minato-ku" with the following query:

[0814] SELECT latitude, longitude, timestamp FROM traffic_data WHERE region IN ('Shinjuku', 'Minato');

[0815] 2. Traffic pattern analysis

[0816] Using the loaded data, apply K-means clustering as follows:

[0817] KMeans(n_clusters=5).fit(data_frame)

[0818] 3. Cluster centroid calculation

[0819] Calculate the center point of each cluster and get the optimal route as follows:

[0820] data_frame.groupby('cluster').mean()

[0821] 4. Promoting sustainable transportation

[0822] Randomly select electric vehicles or carpools for each cluster and add them to the dataframe as follows:

[0823] data_frame['sustainable_transport'] = np.random.choice(['electric_car', 'carpool'], len(data_frame))

[0824] 5. Display on smartphone applications

[0825] The calculated route and recommended transportation methods are displayed on a map using the Google Maps API, providing a visual representation to the user.

[0826] In this way, the system of the present invention can provide an efficient and sustainable means of transportation and contribute to improving traffic problems in urban areas.

[0827] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0828] Step 1: Loading traffic data

[0829] The server queries the traffic data from the database and converts it into a Pandas dataframe. This query retrieves traffic data for a specified area, including latitude, longitude, and time. For example, it executes the following SQL query:

[0830] SELECT latitude, longitude, timestamp FROM traffic_data WHERE region IN ('Shinjuku', 'Minato');

[0831] Input: Database

[0832] Output: Pandas dataframe

[0833] Step 2: Analyze traffic patterns

[0834] The server analyzes the loaded traffic data using the K-means clustering algorithm to classify data points into multiple clusters. It uses the scikit-learn library to perform the clustering. Specifically, it applies K-means clustering as follows:

[0835] from sklearn.cluster import KMeans

[0836] kmeans = KMeans(n_clusters=5).fit(data_frame)

[0837] data_frame['cluster'] = kmeans.labels_

[0838] Input: Pandas dataframe

[0839] Output: Data frame with cluster identifiers

[0840] Step 3: Calculate the cluster centers (centroids)

[0841] The server calculates the average latitude and longitude within each cluster to determine the centroid, which is the center point of the cluster and is used to set the optimal traffic route. Specifically, it calculates as follows:

[0842] centroids = data_frame.groupby('cluster').mean()

[0843] Input: Data frame with cluster identifiers

[0844] Output: Centroid data frame for each cluster

[0845] Step 4: Promote sustainable transportation

[0846] The server randomly selects a sustainable transportation mode for each cluster and records it in a data frame. Sustainable transportation modes include electric vehicles and carpooling. Specifically, the following settings are used:

[0847] import numpy as np

[0848] data_frame['sustainable_transport'] = np.random.choice(['electric_car', 'carpool'], len(data_frame))

[0849] Input: Centroid data frame of clusters

[0850] Output: A data frame containing sustainable transport modes

[0851] Step 5: Display on smartphone application

[0852] The smartphone device receives information on optimal routes and sustainable transportation methods sent from the server and displays it on a map in real time. It uses the Google Maps API to provide visual route information. Specifically, it sets markers on the map as follows:

[0853] map.addMarker(new google.maps.Marker({

[0854] position: {lat: 35.6895, lng: 139.6917}, / / Example coordinates

[0855] map: map,

[0856] title: 'Start Point'

[0857] }));

[0858] Input: A data frame containing sustainable transport modes

[0859] Output: A smartphone application that displays the optimal route and transportation options

[0860] Through the application, users can view the best routes and sustainable transportation options in real time, enabling them to travel eco-friendly.

[0861] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0862] The present invention combines a system for analyzing traffic data, generating optimal traffic routes, and recommending sustainable transportation modes with an emotion engine that recognizes user emotions. This system is implemented in the following configuration, including a server, a terminal, and a user.

[0863] System program processing contents

[0864] Loading traffic data

[0865] The server loads the traffic data and converts it into a Pandas data frame, which contains the latitude and longitude of each point, so that the system can access the latitude and longitude of each point within the city.

[0866] Traffic pattern analysis

[0867] The server applies the K-means clustering algorithm to the loaded traffic data, which classifies the data points into five clusters. Each data point is given a 'cluster' column with the cluster identifier to which it belongs.

[0868] Calculating cluster centroids

[0869] The server calculates the average latitude and longitude of all data points within each cluster, and determines the center point (centroid) of each cluster. This centroid becomes the base point for the optimal traffic route.

[0870] Promoting sustainable transportation

[0871] The server creates a list of predefined sustainable transport modes (bus, e-bike, tram, etc.), then randomly selects and recommends a sustainable transport mode based on the value of the 'cluster' column for each data point, and adds this recommendation to a new 'recommended_mode' column in the data frame.

[0872] User sentiment analysis

[0873] The emotion engine recognizes and analyzes the user's emotions. User emotion data is collected, for example, from the user's smartphone or other devices. This can be done using technologies such as facial recognition, voice analysis, and text analysis. This emotion analysis helps identify which transportation modes the user has positive or negative feelings about.

[0874] Emotion-based optimization of sustainable transportation

[0875] The server integrates the emotion data obtained from the emotion engine into the analysis results and further optimizes the recommendation of sustainable transportation modes based on the emotion data.For example, if a user has a strong preference for a particular transportation mode based on the user's emotion data, the server prioritizes the recommendation of that transportation mode.

[0876] For example, transportation data is collected from the latitude and longitude of five points in a city. This data is loaded into a server and the K-means clustering algorithm is applied to divide each data point into five clusters. Next, the average latitude and longitude of each cluster is calculated and the optimal transportation route is determined based on this. After that, the optimal sustainable transportation mode is recommended for each cluster and the information is recorded in a data frame.

[0877] Furthermore, when a user sends emotional data to the server via their smartphone, the emotion engine analyzes the data and, based on the analysis results, recommends a more appropriate mode of transportation that matches the emotional data.

[0878] In this way, the system of the present invention can build an efficient and eco-friendly public transportation system, contributing to reducing the carbon footprint of cities. Furthermore, by taking into account the user's emotions, it can provide an environment where users can travel without feeling stressed.

[0879] The processing flow will be explained below.

[0880] Step 1:

[0881] The server loads the traffic data and converts it into a Pandas data frame, which contains the latitude and longitude of each point, allowing the system to capture the location of each point within the city.

[0882] Step 2:

[0883] The server applies the K-means clustering algorithm to the loaded traffic data, which classifies the data points into five clusters. Each data point is given a 'cluster' column with the cluster identifier to which it belongs.

[0884] Step 3:

[0885] The server calculates the average latitude and longitude of all data points within each cluster, and determines the center point (centroid) of each cluster. This centroid becomes the base point for the optimal traffic route.

[0886] Step 4:

[0887] The server creates a list of sustainable transport modes (bus, e-bike, tram, etc.). It then randomly selects and recommends a sustainable transport mode based on the value of the 'cluster' column for each data point. This recommendation is added to a new 'recommended_mode' column in the data frame.

[0888] Step 5:

[0889] The device collects emotional data from the user. This emotional data is acquired using, for example, the smartphone's camera or microphone. The emotional data includes facial expressions, voice tone, text content, and so on.

[0890] Step 6:

[0891] The device transmits the collected emotion data to a server, which then analyzes the user's emotions using an emotion engine and obtains the analysis results.

[0892] Step 7:

[0893] The server integrates the emotion data obtained from the emotion engine with the transportation data. Based on the user's emotion data, the server further optimizes the recommendation of sustainable transportation modes. For example, if a user has a favorable emotion toward a particular transportation mode, the server prioritizes the recommendation of that mode.

[0894] Step 8:

[0895] The server records and stores information on optimized travel routes and sustainable transportation modes in a data frame, which is then provided to devices and users via APIs and dashboards as needed.

[0896] For example, traffic data is collected from the latitude and longitude of five points in a city. This data is loaded onto a server and the K-means clustering algorithm is applied to divide each data point into five clusters. Next, the average latitude and longitude of each cluster is calculated, and the optimal traffic route is determined based on this.

[0897] The device then collects the user's emotional data and sends it to the server. The server analyzes the emotional data and optimizes the recommendation of sustainable transportation methods based on the results of the emotional analysis. The recommended transportation methods based on the emotional data are recorded in a data frame and provided to the user as necessary information. This reduces the city's carbon footprint and provides an environment where users can travel without stress.

[0898] Example 2

[0899] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0900] There is a need to efficiently analyze transportation data, determine optimal transportation routes, and effectively recommend sustainable transportation modes. It is also necessary to provide a transportation system that is more convenient and satisfies users by optimizing transportation modes based on user emotions. However, while current systems are capable of analyzing transportation data and selecting sustainable transportation modes, they do not adequately optimize transportation modes based on user emotions. This leads to lower user satisfaction and efficiency, and hinders the promotion of sustainable transportation modes.

[0901] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0902] In this invention, the server includes means for collecting traffic data and converting it into a data frame format, means for applying a clustering algorithm to the converted traffic data to classify data points into clusters, means for calculating the center point (average latitude and longitude) of each cluster, means for randomly selecting sustainable transportation modes and recommending the transportation modes, means for analyzing user emotion data, and means for optimizing the recommended transportation modes based on the emotion data. This not only enables efficient analysis of traffic data and recommendation of sustainable transportation modes, but also enables optimization of transportation modes based on user emotion.

[0903] "Traffic data" refers to data that includes various information related to traffic conditions, such as latitude, longitude, traffic volume, speed, and time of day.

[0904] The "data frame format" is a two-dimensional data structure with rows and columns, and is a format for representing tabular data used in Python's Pandas library.

[0905] A "clustering algorithm" is an algorithm for classifying data points into groups (clusters) that share common characteristics.

[0906] A "cluster" is a collection of data points that share common characteristics, grouped together using a clustering algorithm.

[0907] A "centroid" is a point that represents the average location of all data points within a cluster.

[0908] "Sustainable transport" refers to eco-friendly transport methods (e.g. buses, electric bicycles, trams, etc.) that aim to reduce carbon footprints and protect the environment.

[0909] "Emotional data" is data that indicates a user's emotional state and is collected using technologies such as facial recognition, voice analysis, and text analysis.

[0910] An "emotion engine" is a computer program or algorithm that analyzes a user's emotional data and identifies their emotional state.

[0911] "Optimization" is the process of arranging resources and conditions most effectively to achieve a particular objective.

[0912] The present invention is a system that combines multiple pieces of hardware and software to analyze traffic data, generate optimal traffic routes, and recommend sustainable transportation modes. Specific embodiments are described below.

[0913] First, the server collects traffic data and converts it into a data frame using Python's Pandas library. This data includes the latitude and longitude of each location. Next, the server applies a clustering algorithm using the Python library Sci-kit Learn to classify the data points into clusters. During this process, each data point is assigned a cluster identifier.

[0914] The server calculates the average latitude and longitude of data points in each cluster and uses this as the centroid, which represents the cluster. The server then creates a list of sustainable transportation modes (e.g., bus, electric bicycle, tram, etc.) and randomly selects and recommends a sustainable transportation mode based on each cluster identifier. This recommendation information is added to a new column 'recommended_mode' in the data frame.

[0915] The server also collects emotion data from the user's device. This emotion data is collected using technologies such as facial recognition, voice analysis, and text analysis. For example, facial recognition is performed using technologies such as OpenCV and Google Cloud Vision API, and emotions are analyzed from the user's facial expressions. Based on the collected emotion data, the emotion engine analyzes the user's emotions and sends them to the server.

[0916] The server integrates the emotion data obtained from the emotion engine with the analysis results and optimizes the recommendation of sustainable transportation modes based on the emotion data. For example, if a user has a strong preference for a particular transportation mode, the recommendation of that mode will be prioritized, improving the accuracy of the recommendation.

[0917] For example, the latitude and longitude data of five points in a city are collected as transportation data and loaded onto a server. The K-means clustering algorithm is applied to this data to classify each data point into five clusters. Next, the average latitude and longitude values ​​of each cluster are calculated, and the optimal transportation route is determined based on this. After that, a sustainable transportation mode is randomly selected for each cluster, recommended, and the information is recorded in a data frame.

[0918] For example, a server receives emotional data sent by a user from a smartphone and performs an emotional analysis. Based on the analysis results, the system recommends the user's preferred mode of transportation. Through this process, the system of the present invention supports the creation of an efficient and eco-friendly public transportation system, contributing to reducing a city's carbon footprint and improving user satisfaction.

[0919] Example prompts for generative AI models:

[0920] "Using the following location data, generate code that generates optimal transportation routes and recommends sustainable modes of transportation. Additionally, include a section that analyzes user sentiment data to optimize recommended modes of transportation."

[0921] In this way, the embodiments of the present invention enable efficient analysis of traffic data, recommendation of sustainable transportation modes, and even optimization of transportation modes based on user sentiment data, thereby improving the efficiency of the overall transportation system and user satisfaction.

[0922] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0923] Step 1:

[0924] Traffic data collection and conversion

[0925] The server collects traffic data from the Internet or an internal database. For example, it uses an API to obtain traffic data and receives a dataset containing information such as latitude and longitude. It then converts this data into a data frame using the Python Pandas library. The input is raw traffic data, and the output is the traffic data converted into a data frame.

[0926] Specific actions

[0927] The server retrieves the traffic data from the API and then converts it into a data frame using Pandas, which contains the latitude and longitude of each point.

[0928] Step 2:

[0929] Traffic pattern analysis

[0930] The server applies Sci-Kit Learn's K-means clustering algorithm to the converted traffic data. The input is traffic data in a data frame format, and the output is the clustered data. This classifies each data point into a cluster and assigns a cluster identifier to each data point.

[0931] Specific actions

[0932] The server applies K-means clustering and assigns a cluster identifier to each data point, and the results are added as a new column in the data frame.

[0933] Step 3:

[0934] Calculating cluster centroids

[0935] The server calculates the average latitude and longitude within each cluster and defines this as the centroid. The input is traffic data with cluster identifiers, and the output is the centroid position of each cluster.

[0936] Specific actions

[0937] The server averages the latitude and longitude of all data points within each cluster and records this as the center point of each cluster in the data frame.

[0938] Step 4:

[0939] Promoting sustainable transportation

[0940] The server prepares a list of sustainable transport modes (e.g., buses, e-bikes, trams) and randomly selects one based on each cluster identifier. The input is the transport data with calculated centroids, and the output is the recommended sustainable transport mode.

[0941] Specific actions

[0942] The server randomly selects from the list of sustainable transportation options and adds the recommendations to a new column in the data frame.

[0943] Step 5:

[0944] User sentiment analysis

[0945] The emotion engine analyzes emotion data collected from the user's device. The input is the results of facial recognition, voice analysis, and text analysis sent from the user's device, and the output is the analyzed emotion data.

[0946] Specific actions

[0947] The user's device collects emotional data through a camera and microphone and sends it to a server, which then uses an emotion engine to analyze it and identify the user's emotional state.

[0948] Step 6:

[0949] Emotion-based optimization of sustainable transportation

[0950] The server integrates the emotion data obtained from the emotion engine with the analysis results to optimize the recommendation of sustainable transportation modes. The input is the emotion data from the emotion engine and existing recommendation information, and the output is the recommended transportation modes optimized based on the emotion data.

[0951] Specific actions

[0952] The server updates the recommendation list based on the user's preferred mode of transportation based on the emotion data. For example, if the user prefers electric bicycles, electric bicycles will be prioritized in the recommendation list.

[0953] (Application example 2)

[0954] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0955] Optimizing transportation routes and recommending sustainable modes of transportation are important for reducing a city's carbon footprint and providing efficient mobility. However, existing systems do not take into account the user's emotional state, which can lead to lower user satisfaction and stress levels. This reduces users' willingness to use the recommended modes of transportation, resulting in a problem of reduced effectiveness of the overall system.

[0956] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0957] In this invention, the server includes means for analyzing traffic data using an AI algorithm, means for optimizing a traffic route based on the analyzed traffic data, means for recommending a sustainable means of transportation based on the optimized traffic route, emotion recognition means for analyzing a user's emotion, and means for optimizing the recommended sustainable means of transportation based on the user's emotion data. This allows the server to recommend the optimal means of transportation according to the user's emotional state, thereby improving user satisfaction and system efficiency.

[0958] Key Word Definitions

[0959] An "AI algorithm" is a computational procedure that uses artificial intelligence technology to analyze data and discover specific patterns.

[0960] "Traffic data" is a collection of data containing information related to traffic, such as location information and traffic conditions.

[0961] "Means for optimizing traffic routes" refers to a method for calculating optimal routes based on analyzed traffic data.

[0962] "Sustainable transport" is transport designed to minimise environmental impact, including public transport and electric vehicles.

[0963] "Emotion recognition means" refers to technology for detecting and analyzing a user's emotions, and uses techniques such as facial recognition and voice analysis.

[0964] "User emotion data" is data that indicates the user's emotional state, and is acquired in the form of an image, sound, text, or the like.

[0965] The "means for optimizing sustainable transportation" is a method for adjusting recommendations for sustainable transportation based on user sentiment data.

[0966] A "data frame" is a two-dimensional data structure consisting of rows and columns, used for data processing and analysis.

[0967] "Location data" is information that indicates the latitude and longitude of a specific point.

[0968] MODE FOR CARRYING OUT THE INVENTION

[0969] This invention relates to a system that uses AI algorithms to analyze traffic data and recommend optimal routes and sustainable transportation methods based on user emotion data. This system is implemented with the following components, including a server, a terminal, and a user.

[0970] 1. Loading and analyzing traffic data

[0971] The server loads and analyzes traffic data, which includes information such as location and traffic conditions. The server manages the traffic data using a Pandas data frame and classifies the data points using the K-means clustering algorithm.

[0972] 2. Traffic route optimization

[0973] The server generates the optimal traffic route based on the analyzed traffic data. At this stage, it calculates the average value of the location data (latitude and longitude) of each cluster to determine the centroid. This allows the optimal traffic route to be designed.

[0974] 3. Promoting sustainable transportation

[0975] The server recommends sustainable transportation modes based on the optimized transportation route. The recommended transportation modes are randomly selected from a predefined list. The recommended transportation modes are recorded in a data frame.

[0976] 4. Emotion analysis

[0977] The server collects emotion data from users' smartphones and other devices and analyzes it using an emotion engine, which uses technologies such as facial recognition and voice analysis to recognize the user's emotions.

[0978] 5. Emotion-Based Optimization

[0979] The server optimizes the recommended sustainable transportation modes based on the sentiment data: if the user has a positive sentiment towards a particular transportation mode, it prioritizes the recommendation of that mode.

[0980] Specific examples

[0981] Transportation data is collected from multiple locations in a city using latitude and longitude data. This data is loaded onto a server and the K-means clustering algorithm is applied to separate the data points into clusters. The average latitude and longitude of each cluster is calculated to determine the centroid. The optimal sustainable transportation mode (e.g., electric bus or electric bicycle) is then recommended for each cluster and the information is recorded in a data frame.

[0982] Furthermore, when users send emotion data to the server via their smartphones, the emotion engine analyzes the data and re-recommends the optimal sustainable transportation mode based on the emotion data. In this way, the system can provide the optimal transportation mode that improves user satisfaction.

[0983] Prompt Sentence Examples

[0984] This includes examples of inputting prompts like the following into a generative AI model:

[0985] Create a system that suggests sustainable transportation options based on the user's emotional data ('happy', 'relaxed', etc.). For example:

[0986] If the user's emotion is "happy," suggest an electric bus.

[0987] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0988] System processing flow

[0989] Step 1:

[0990] The server loads the traffic data. The traffic data is read from a CSV file and converted into a Pandas data frame. The input at this stage is traffic data including location information and traffic conditions, and the output is traffic data in data frame format.

[0991] Step 2:

[0992] The server applies the K-means clustering algorithm to the loaded traffic data. This process classifies each data point into a specific cluster. The input is the data frame obtained in step 1, and the output is a data frame with the cluster identifiers added.

[0993] Step 3:

[0994] The server calculates the average latitude and longitude of each cluster, which determines the center point (centroid) of the cluster. The input is the data frame with cluster identifiers obtained in step 2, and the output is the centroid position data of each cluster.

[0995] Step 4:

[0996] The server recommends sustainable transportation modes based on the optimized transportation route. A predefined list of sustainable transportation modes (e.g., electric bus, electric bicycle, tram) is used in this stage. The input is the centroid location data for each cluster, and the output is a data frame with the recommended transportation modes added.

[0997] Step 5:

[0998] The device collects the user's emotional data. This emotional data is obtained from smartphones and other devices and uses technologies such as facial recognition and voice analysis. The input is the user's biometric data and voice data, and the output is analyzed emotional data.

[0999] Step 6:

[1000] The server analyzes the user's emotional data using an emotion engine, which identifies the user's current emotional state. The input is the emotional data obtained in step 5, and the output is the user's emotional state (e.g., happy, relaxed, etc.).

[1001] Step 7:

[1002] The server optimizes the recommended sustainable transportation modes based on the user's emotional data. For example, if the user is happy, it preferentially recommends electric buses. The input is the emotional state obtained in step 6 and the recommended transportation modes obtained in step 4, and the output is a data frame with the optimized transportation modes added based on the emotional state.

[1003] Specifically, the smartphone app captures the user's face with a camera and sends the image data to the server. The server then performs emotion recognition and analyzes the user's emotional state in real time. It then suggests an appropriate transportation method and sends a notification to the user's smartphone.

[1004] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1005] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1006] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1007] [Fourth embodiment]

[1008] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1009] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1010] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1011] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1012] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1013] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1014] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1015] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1016] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1017] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1018] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1019] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1020] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1021] The present invention provides a system for analyzing traffic data, generating optimal traffic routes, and recommending sustainable transportation modes. This system is implemented with the following configuration, centered around a server.

[1022] System program processing contents

[1023] 1. Loading traffic data

[1024] The server collects traffic data (including latitude and longitude) and converts it into a data frame, which reflects actual traffic conditions and contains location data for each point within a city.

[1025] 2. Traffic pattern analysis

[1026] The server analyzes the collected traffic data using the K-means clustering algorithm. This analysis classifies the traffic data into several clusters. Each cluster is a set of locations with similar traffic patterns. As a result of the analysis, each data point is assigned a cluster identifier to which it belongs.

[1027] 3. Calculating the cluster center (centroid)

[1028] The server calculates the average latitude and longitude of all data points within each cluster, which gives the center point (centroid) of each cluster, allowing for the generation of optimal travel routes that take into account distance and time efficiency.

[1029] 4. Promoting sustainable transportation

[1030] The server randomly recommends the best mode for each cluster based on a predefined list of sustainable modes (e.g., bus, e-bike, tram, etc.) and records the recommended mode as a new column in the data frame.

[1031] As a concrete example, the process is as follows: For example, transportation data is collected as latitude and longitude data for five points in a city. This data is loaded into a server and the K-means clustering algorithm is applied to divide each data point into five clusters. Next, the average latitude and longitude values ​​for each cluster are calculated and the optimal transportation route is determined based on this. After that, the optimal sustainable transportation mode is recommended for each cluster and the information is recorded in a data frame.

[1032] In this way, the system of the present invention can create an efficient and eco-friendly public transport system, contributing to reducing the carbon footprint of cities.

[1033] The processing flow will be explained below.

[1034] Step 1:

[1035] The server loads the traffic data and converts it into a Pandas dataframe, which contains the latitude and longitude of each point.

[1036] Step 2:

[1037] The server applies the K-means clustering algorithm to the loaded traffic data, which classifies the data points into five clusters. Each data point is given a 'cluster' column with the cluster identifier to which it belongs.

[1038] Step 3:

[1039] The server calculates the average latitude and longitude of each cluster and determines the center point (centroid) of each cluster. This centroid becomes the base point for the optimal transportation route.

[1040] Step 4:

[1041] The server creates a list of predefined sustainable transport modes (bus, e-bike, tram, etc.), then randomly selects and recommends a sustainable transport mode based on the value of the 'cluster' column for each data point, and adds this recommendation to a new 'recommended_mode' column in the data frame.

[1042] Step 5:

[1043] The server records and stores information on optimized travel routes and sustainable transportation modes in a data frame, which is then provided to devices and users via APIs and dashboards as needed.

[1044] Example 1

[1045] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1046] Traffic congestion and environmental impacts are major problems in modern urban transportation systems. To address these issues, there is a need for methods to improve traffic efficiency and reduce environmental impacts by effectively analyzing traffic data, generating optimal transportation routes, and recommending sustainable transportation modes.

[1047] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1048] In this invention, the server includes means for collecting traffic data and converting it into a data frame, means for analyzing the collected traffic data using a clustering algorithm, and means for calculating the average latitude and longitude of data points belonging to each cluster, thereby enabling effective analysis of the traffic data, generation of optimal traffic routes, and recommendation of sustainable transportation modes.

[1049] "Transportation Data" refers to location information, including latitude and longitude, of each point within a city and includes those data points.

[1050] "Data frame" refers to a tabular data structure that structures data in a matrix format to make it easier to manage.

[1051] A "clustering algorithm" is a computational method for dividing data points into groups based on similarity, and in the present invention primarily refers to K-means clustering.

[1052] A "cluster" refers to a group of data points that results from using a clustering algorithm.

[1053] "Average latitude and longitude" means the sum of the latitude and longitude values ​​of all data points belonging to a particular cluster, divided by the number of data points.

[1054] A "transportation route" refers to the optimal route for traveling from one point to another, and is determined taking into account the most efficient means of transportation and time efficiency.

[1055] "Sustainable transportation" refers to transportation that reduces environmental impact and minimizes energy consumption, and examples include buses, electric bicycles, and trams.

[1056] The present invention is a system that analyzes traffic data, generates optimal traffic routes, and recommends sustainable transportation modes. This system performs the following specific processes, centered on a server:

[1057] First, the server collects traffic data, including the latitude and longitude of each point in the city, collected via GPS devices and smartphone applications. The collected data is then converted into a data frame for easy handling. This conversion is performed using the Python Pandas library.

[1058] The server then applies a clustering algorithm to the collected traffic data to analyze traffic patterns. This analysis process uses the Scikit-learn library to run the K-means clustering algorithm, which divides data points into groups based on similarity. The analysis results can be classified into areas that are congested during rush hour and areas that are congested even during normal times.

[1059] The server then calculates the average latitude and longitude of the data points in each cluster, which gives the center point (centroid) of each cluster. The calculated centroid is recorded in a new data frame and used to generate optimal traffic routes.

[1060] The server then identifies the optimal mode for each cluster based on a predefined list of sustainable modes (e.g., bus, e-bike, tram, etc.). The selected mode is randomly assigned and finally recorded as a new column in the data frame.

[1061] For example, transportation data is collected from the latitude and longitude of five points in a city. This data is loaded onto a server and the K-means clustering algorithm is applied to divide each data point into five clusters. The average latitude and longitude values ​​of each cluster are then calculated to determine the optimal transportation route. Finally, the optimal sustainable transportation mode is recommended for each cluster and the information is recorded in a data frame.

[1062] For example, the following can be entered as a prompt to a generative AI model:

[1063] "Please describe the processing steps of a system that uses transportation data (latitude and longitude) from five locations in a city to generate optimal transportation routes using K-means clustering and recommend sustainable transportation modes. These steps should include loading transportation data, analyzing traffic patterns, calculating cluster centers, and recommending sustainable transportation modes. Also, please include the specific actions taken at each step."

[1064] In this way, the system of the present invention can create an efficient and eco-friendly public transport system, contributing to reducing the carbon footprint of cities.

[1065] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1066] Step 1:

[1067] Collecting and loading traffic data

[1068] The server collects traffic data, including the latitude and longitude of each point in the city, obtained from GPS devices and smartphone applications.

[1069] Input: Latitude and longitude data for each location

[1070] Output: Data frame

[1071] The server uses Python's Pandas library to convert the collected data into a data frame, which is then used in subsequent analysis steps.

[1072] Step 2:

[1073] Traffic pattern analysis

[1074] The server applies a clustering algorithm to the collected traffic data, in this case the K-means clustering algorithm.

[1075] Input: DataFrame

[1076] Output: Data frame with cluster identifiers for each data point

[1077] The server uses the Scikit-learn library to perform K-means clustering to classify data points into clusters based on their similarity.

[1078] Step 3:

[1079] Calculating cluster centroids

[1080] The server calculates the average latitude and longitude of the data points that belong to each cluster.

[1081] Input: Data frame with cluster identifiers

[1082] Output: A data frame representing the centroids of each cluster

[1083] The server uses the Pandas library to calculate the average latitude and longitude values ​​for each cluster and record them in a new data frame.

[1084] Step 4:

[1085] Generate optimal transportation routes

[1086] The server generates an optimal traffic route based on the calculated cluster centroid.

[1087] Input: A data frame representing the centroids of each cluster

[1088] Output: Data frame containing optimal transportation routes

[1089] The server uses an algorithm to determine the best route between centroids, taking into account distance and travel time efficiency.

[1090] Step 5:

[1091] Promoting sustainable transportation

[1092] The server recommends the best mode of transport for each cluster based on a predefined list of sustainable modes of transport (e.g., bus, e-bike, tram, etc.).

[1093] Input: A data frame containing optimal transit routes

[1094] Output: A data frame with a new column of sustainable transport recommendations

[1095] The server assigns the optimal transportation mode to each cluster randomly or based on specific conditions and records the information in a data frame.

[1096] In this way, the server analyzes traffic data and recommends optimal routes and sustainable transportation methods. This process improves urban traffic efficiency and creates an eco-friendly environment.

[1097] (Application example 1)

[1098] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1099] In recent years, traffic congestion and environmental pollution have become serious problems in urban areas. Furthermore, conventional methods often do not achieve sufficient accuracy when selecting optimal transportation routes or recommending sustainable transportation modes. Furthermore, there are challenges in analyzing traffic data in real time and proposing optimal routes using the results.

[1100] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1101] In this invention, the server includes means for analyzing traffic data using an AI algorithm, means for optimizing traffic routes based on the analyzed traffic data, means for recommending sustainable transportation modes based on the optimized traffic routes, means for analyzing traffic patterns using a K-means clustering algorithm, and means for displaying optimal routes in an application installed on a smartphone. This makes it possible to alleviate traffic congestion and reduce environmental pollution in urban areas, while also enabling highly accurate route suggestions in real time.

[1102] An "AI algorithm" is a calculation method that uses artificial intelligence technology to analyze data and make predictions.

[1103] "Traffic Data" means a collection of information including traffic conditions, such as latitude, longitude, time, and other traffic-related data.

[1104] A "means for optimizing traffic routes" is a method for calculating the most efficient travel route based on collected traffic data.

[1105] "Sustainable transportation" refers to transportation that can be operated over the long term while minimizing the burden on the environment. Examples include electric vehicles and carpooling.

[1106] A "traffic pattern" is a characteristic that indicates the traffic conditions and their fluctuations in a particular area or time period.

[1107] The "K-means clustering algorithm" is a machine learning algorithm for classifying data into K clusters, and is widely used in analyzing traffic data.

[1108] An "application installed on a smartphone" is software that runs on a smartphone device and provides functionality through a user interface.

[1109] A "data frame" is a data structure for managing a collection of data consisting of rows and columns, and is used for analysis and manipulation.

[1110] "Means for collecting traffic data in real time" refers to a method for instantly obtaining current traffic conditions.

[1111] "Means for displaying optimal route" means a method for visually showing the calculated optimal travel route to the user.

[1112] System program generation

[1113] The system for realizing the present invention is implemented through the following steps.

[1114] 1. Loading traffic data

[1115] The server collects traffic data from storage such as a database and converts it into a data frame using a data frame library such as Pandas. For example, PostgreSQL is used to query traffic data including latitude, longitude, and time, and read it as a data frame.

[1116] 2. Traffic pattern analysis

[1117] The server uses the K-means clustering algorithm to analyze the collected traffic data. It uses the scikit-learn library to classify data points into clusters that indicate specific traffic patterns.

[1118] 3. Calculating the cluster center (centroid)

[1119] The server calculates the average latitude and longitude of all data points within each cluster, which gives the center point (centroid) of each cluster, and then plans optimal traffic routes based on that.

[1120] 4. Promoting sustainable transportation

[1121] The server recommends a sustainable transportation mode for each cluster by randomly selecting it from the sustainable transportation mode list and recording it in a data frame.

[1122] 5. Display on smartphone applications

[1123] The application installed on the user's smartphone receives information on optimal routes and sustainable transportation methods sent from the server and displays it in real time. It is preferable to have a flat user interface, for example, to display the route on a map using Google Maps API.

[1124] Hardware and software used

[1125] Hardware: Servers, smartphones, network devices

[1126] Software: Pandas, scikit-learn, PostgreSQL, Flask, Google Maps API

[1127] Data processing and calculation

[1128] 1. Processing of traffic data

[1129] Querying from the database

[1130] Dataframe transformation with Pandas

[1131] 2. Traffic pattern calculation

[1132] Classification by K-means clustering

[1133] Calculate the centroid of each cluster

[1134] 3. Selecting sustainable transportation options

[1135] Update a dataframe with random selection

[1136] Specific examples

[1137] The operation of the system will be explained in detail through the following concrete example.

[1138] When a user searches for a route from "Shinjuku Station" to "Tokyo Tower," the system performs the following process.

[1139] 1. Loading traffic data

[1140] The server loads the traffic data for "Shinjuku-ku" and "Minato-ku" with the following query:

[1141] SELECT latitude, longitude, timestamp FROM traffic_data WHERE region IN ('Shinjuku', 'Minato');

[1142] 2. Traffic pattern analysis

[1143] Using the loaded data, apply K-means clustering as follows:

[1144] KMeans(n_clusters=5).fit(data_frame)

[1145] 3. Cluster centroid calculation

[1146] Calculate the center point of each cluster and get the optimal route as follows:

[1147] data_frame.groupby('cluster').mean()

[1148] 4. Promoting sustainable transportation

[1149] Randomly select electric vehicles or carpools for each cluster and add them to the dataframe as follows:

[1150] data_frame['sustainable_transport'] = np.random.choice(['electric_car', 'carpool'], len(data_frame))

[1151] 5. Display on smartphone applications

[1152] The calculated route and recommended transportation methods are displayed on a map using the Google Maps API, providing a visual representation to the user.

[1153] In this way, the system of the present invention can provide an efficient and sustainable means of transportation and contribute to improving traffic problems in urban areas.

[1154] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1155] Step 1: Loading traffic data

[1156] The server queries the traffic data from the database and converts it into a Pandas dataframe. This query retrieves traffic data for a specified area, including latitude, longitude, and time. For example, it executes the following SQL query:

[1157] SELECT latitude, longitude, timestamp FROM traffic_data WHERE region IN ('Shinjuku', 'Minato');

[1158] Input: Database

[1159] Output: Pandas dataframe

[1160] Step 2: Analyze traffic patterns

[1161] The server analyzes the loaded traffic data using the K-means clustering algorithm to classify data points into multiple clusters. It uses the scikit-learn library to perform the clustering. Specifically, it applies K-means clustering as follows:

[1162] from sklearn.cluster import KMeans

[1163] kmeans = KMeans(n_clusters=5).fit(data_frame)

[1164] data_frame['cluster'] = kmeans.labels_

[1165] Input: Pandas dataframe

[1166] Output: Data frame with cluster identifiers

[1167] Step 3: Calculate the cluster centers (centroids)

[1168] The server calculates the average latitude and longitude within each cluster to determine the centroid, which is the center point of the cluster and is used to set the optimal traffic route. Specifically, it calculates as follows:

[1169] centroids = data_frame.groupby('cluster').mean()

[1170] Input: Data frame with cluster identifiers

[1171] Output: Centroid data frame for each cluster

[1172] Step 4: Promote sustainable transportation

[1173] The server randomly selects a sustainable transportation mode for each cluster and records it in a data frame. Sustainable transportation modes include electric vehicles and carpooling. Specifically, the following settings are used:

[1174] import numpy as np

[1175] data_frame['sustainable_transport'] = np.random.choice(['electric_car', 'carpool'], len(data_frame))

[1176] Input: Centroid data frame of clusters

[1177] Output: A data frame containing sustainable transport modes

[1178] Step 5: Display on smartphone application

[1179] The smartphone device receives information on optimal routes and sustainable transportation methods sent from the server and displays it on a map in real time. It uses the Google Maps API to provide visual route information. Specifically, it sets markers on the map as follows:

[1180] map.addMarker(new google.maps.Marker({

[1181] position: {lat: 35.6895, lng: 139.6917}, / / Example coordinates

[1182] map: map,

[1183] title: 'Start Point'

[1184] }));

[1185] Input: A data frame containing sustainable transport modes

[1186] Output: A smartphone application that displays the optimal route and transportation options

[1187] Through the application, users can view the best routes and sustainable transportation options in real time, enabling them to travel eco-friendly.

[1188] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1189] The present invention combines a system for analyzing traffic data, generating optimal traffic routes, and recommending sustainable transportation modes with an emotion engine that recognizes user emotions. This system is implemented in the following configuration, including a server, a terminal, and a user.

[1190] System program processing contents

[1191] Loading traffic data

[1192] The server loads the traffic data and converts it into a Pandas data frame, which contains the latitude and longitude of each point, so that the system can access the latitude and longitude of each point within the city.

[1193] Traffic pattern analysis

[1194] The server applies the K-means clustering algorithm to the loaded traffic data, which classifies the data points into five clusters. Each data point is given a 'cluster' column with the cluster identifier to which it belongs.

[1195] Calculating cluster centroids

[1196] The server calculates the average latitude and longitude of all data points within each cluster, and determines the center point (centroid) of each cluster. This centroid becomes the base point for the optimal traffic route.

[1197] Promoting sustainable transportation

[1198] The server creates a list of predefined sustainable transport modes (bus, e-bike, tram, etc.), then randomly selects and recommends a sustainable transport mode based on the value of the 'cluster' column for each data point, and adds this recommendation to a new 'recommended_mode' column in the data frame.

[1199] User sentiment analysis

[1200] The emotion engine recognizes and analyzes the user's emotions. User emotion data is collected, for example, from the user's smartphone or other devices. This can be done using technologies such as facial recognition, voice analysis, and text analysis. This emotion analysis helps identify which transportation modes the user has positive or negative feelings about.

[1201] Emotion-based optimization of sustainable transportation

[1202] The server integrates the emotion data obtained from the emotion engine into the analysis results and further optimizes the recommendation of sustainable transportation modes based on the emotion data.For example, if a user has a strong preference for a particular transportation mode based on the user's emotion data, the server prioritizes the recommendation of that transportation mode.

[1203] For example, transportation data is collected from the latitude and longitude of five points in a city. This data is loaded into a server and the K-means clustering algorithm is applied to divide each data point into five clusters. Next, the average latitude and longitude of each cluster is calculated and the optimal transportation route is determined based on this. After that, the optimal sustainable transportation mode is recommended for each cluster and the information is recorded in a data frame.

[1204] Furthermore, when a user sends emotional data to the server via their smartphone, the emotion engine analyzes the data and, based on the analysis results, recommends a more appropriate mode of transportation that matches the emotional data.

[1205] In this way, the system of the present invention can build an efficient and eco-friendly public transportation system, contributing to reducing the carbon footprint of cities. Furthermore, by taking into account the user's emotions, it can provide an environment where users can travel without feeling stressed.

[1206] The processing flow will be explained below.

[1207] Step 1:

[1208] The server loads the traffic data and converts it into a Pandas data frame, which contains the latitude and longitude of each point, allowing the system to capture the location of each point within the city.

[1209] Step 2:

[1210] The server applies the K-means clustering algorithm to the loaded traffic data, which classifies the data points into five clusters. Each data point is given a 'cluster' column with the cluster identifier to which it belongs.

[1211] Step 3:

[1212] The server calculates the average latitude and longitude of all data points within each cluster, and determines the center point (centroid) of each cluster. This centroid becomes the base point for the optimal traffic route.

[1213] Step 4:

[1214] The server creates a list of sustainable transport modes (bus, e-bike, tram, etc.). It then randomly selects and recommends a sustainable transport mode based on the value of the 'cluster' column for each data point. This recommendation is added to a new 'recommended_mode' column in the data frame.

[1215] Step 5:

[1216] The device collects emotional data from the user. This emotional data is acquired using, for example, the smartphone's camera or microphone. The emotional data includes facial expressions, voice tone, text content, and so on.

[1217] Step 6:

[1218] The device transmits the collected emotion data to a server, which then analyzes the user's emotions using an emotion engine and obtains the analysis results.

[1219] Step 7:

[1220] The server integrates the emotion data obtained from the emotion engine with the transportation data. Based on the user's emotion data, the server further optimizes the recommendation of sustainable transportation modes. For example, if a user has a favorable emotion toward a particular transportation mode, the server prioritizes the recommendation of that mode.

[1221] Step 8:

[1222] The server records and stores information on optimized travel routes and sustainable transportation modes in a data frame, which is then provided to devices and users via APIs and dashboards as needed.

[1223] For example, traffic data is collected from the latitude and longitude of five points in a city. This data is loaded onto a server and the K-means clustering algorithm is applied to divide each data point into five clusters. Next, the average latitude and longitude of each cluster is calculated, and the optimal traffic route is determined based on this.

[1224] The device then collects the user's emotional data and sends it to the server. The server analyzes the emotional data and optimizes the recommendation of sustainable transportation methods based on the results of the emotional analysis. The recommended transportation methods based on the emotional data are recorded in a data frame and provided to the user as necessary information. This reduces the city's carbon footprint and provides an environment where users can travel without stress.

[1225] Example 2

[1226] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1227] There is a need to efficiently analyze transportation data, determine optimal transportation routes, and effectively recommend sustainable transportation modes. It is also necessary to provide a transportation system that is more convenient and satisfies users by optimizing transportation modes based on user emotions. However, while current systems are capable of analyzing transportation data and selecting sustainable transportation modes, they do not adequately optimize transportation modes based on user emotions. This leads to lower user satisfaction and efficiency, and hinders the promotion of sustainable transportation modes.

[1228] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1229] In this invention, the server includes means for collecting traffic data and converting it into a data frame format, means for applying a clustering algorithm to the converted traffic data to classify data points into clusters, means for calculating the center point (average latitude and longitude) of each cluster, means for randomly selecting sustainable transportation modes and recommending the transportation modes, means for analyzing user emotion data, and means for optimizing the recommended transportation modes based on the emotion data. This not only enables efficient analysis of traffic data and recommendation of sustainable transportation modes, but also enables optimization of transportation modes based on user emotion.

[1230] "Traffic data" refers to data that includes various information related to traffic conditions, such as latitude, longitude, traffic volume, speed, and time of day.

[1231] The "data frame format" is a two-dimensional data structure with rows and columns, and is a format for representing tabular data used in Python's Pandas library.

[1232] A "clustering algorithm" is an algorithm for classifying data points into groups (clusters) that share common characteristics.

[1233] A "cluster" is a collection of data points that share common characteristics, grouped together using a clustering algorithm.

[1234] A "centroid" is a point that represents the average location of all data points within a cluster.

[1235] "Sustainable transport" refers to eco-friendly transport methods (e.g. buses, electric bicycles, trams, etc.) that aim to reduce carbon footprints and protect the environment.

[1236] "Emotional data" is data that indicates a user's emotional state and is collected using technologies such as facial recognition, voice analysis, and text analysis.

[1237] An "emotion engine" is a computer program or algorithm that analyzes a user's emotional data and identifies their emotional state.

[1238] "Optimization" is the process of arranging resources and conditions most effectively to achieve a particular objective.

[1239] The present invention is a system that combines multiple pieces of hardware and software to analyze traffic data, generate optimal traffic routes, and recommend sustainable transportation modes. Specific embodiments are described below.

[1240] First, the server collects traffic data and converts it into a data frame using Python's Pandas library. This data includes the latitude and longitude of each location. Next, the server applies a clustering algorithm using the Python library Sci-kit Learn to classify the data points into clusters. During this process, each data point is assigned a cluster identifier.

[1241] The server calculates the average latitude and longitude of data points in each cluster and uses this as the centroid, which represents the cluster. The server then creates a list of sustainable transportation modes (e.g., bus, electric bicycle, tram, etc.) and randomly selects and recommends a sustainable transportation mode based on each cluster identifier. This recommendation information is added to a new column 'recommended_mode' in the data frame.

[1242] The server also collects emotion data from the user's device. This emotion data is collected using technologies such as facial recognition, voice analysis, and text analysis. For example, facial recognition is performed using technologies such as OpenCV and Google Cloud Vision API, and emotions are analyzed from the user's facial expressions. Based on the collected emotion data, the emotion engine analyzes the user's emotions and sends them to the server.

[1243] The server integrates the emotion data obtained from the emotion engine with the analysis results and optimizes the recommendation of sustainable transportation modes based on the emotion data. For example, if a user has a strong preference for a particular transportation mode, the recommendation of that mode will be prioritized, improving the accuracy of the recommendation.

[1244] For example, the latitude and longitude data of five points in a city are collected as transportation data and loaded onto a server. The K-means clustering algorithm is applied to this data to classify each data point into five clusters. Next, the average latitude and longitude values ​​of each cluster are calculated, and the optimal transportation route is determined based on this. After that, a sustainable transportation mode is randomly selected for each cluster, recommended, and the information is recorded in a data frame.

[1245] For example, a server receives emotional data sent by a user from a smartphone and performs an emotional analysis. Based on the analysis results, the system recommends the user's preferred mode of transportation. Through this process, the system of the present invention supports the creation of an efficient and eco-friendly public transportation system, contributing to reducing a city's carbon footprint and improving user satisfaction.

[1246] Example prompts for generative AI models:

[1247] "Using the following location data, generate code that generates optimal transportation routes and recommends sustainable modes of transportation. Additionally, include a section that analyzes user sentiment data to optimize recommended modes of transportation."

[1248] In this way, the embodiments of the present invention enable efficient analysis of traffic data, recommendation of sustainable transportation modes, and even optimization of transportation modes based on user sentiment data, thereby improving the efficiency of the overall transportation system and user satisfaction.

[1249] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1250] Step 1:

[1251] Traffic data collection and conversion

[1252] The server collects traffic data from the Internet or an internal database. For example, it uses an API to obtain traffic data and receives a dataset containing information such as latitude and longitude. It then converts this data into a data frame using the Python Pandas library. The input is raw traffic data, and the output is the traffic data converted into a data frame.

[1253] Specific actions

[1254] The server retrieves the traffic data from the API and then converts it into a data frame using Pandas, which contains the latitude and longitude of each point.

[1255] Step 2:

[1256] Traffic pattern analysis

[1257] The server applies Sci-Kit Learn's K-means clustering algorithm to the converted traffic data. The input is traffic data in a data frame format, and the output is the clustered data. This classifies each data point into a cluster and assigns a cluster identifier to each data point.

[1258] Specific actions

[1259] The server applies K-means clustering and assigns a cluster identifier to each data point, and the results are added as a new column in the data frame.

[1260] Step 3:

[1261] Calculating cluster centroids

[1262] The server calculates the average latitude and longitude within each cluster and defines this as the centroid. The input is traffic data with cluster identifiers, and the output is the centroid position of each cluster.

[1263] Specific actions

[1264] The server averages the latitude and longitude of all data points within each cluster and records this as the center point of each cluster in the data frame.

[1265] Step 4:

[1266] Promoting sustainable transportation

[1267] The server prepares a list of sustainable transport modes (e.g., buses, e-bikes, trams) and randomly selects one based on each cluster identifier. The input is the transport data with calculated centroids, and the output is the recommended sustainable transport mode.

[1268] Specific actions

[1269] The server randomly selects from the list of sustainable transportation options and adds the recommendations to a new column in the data frame.

[1270] Step 5:

[1271] User sentiment analysis

[1272] The emotion engine analyzes emotion data collected from the user's device. The input is the results of facial recognition, voice analysis, and text analysis sent from the user's device, and the output is the analyzed emotion data.

[1273] Specific actions

[1274] The user's device collects emotional data through a camera and microphone and sends it to a server, which then uses an emotion engine to analyze it and identify the user's emotional state.

[1275] Step 6:

[1276] Emotion-based optimization of sustainable transportation

[1277] The server integrates the emotion data obtained from the emotion engine with the analysis results to optimize the recommendation of sustainable transportation modes. The input is the emotion data from the emotion engine and existing recommendation information, and the output is the recommended transportation modes optimized based on the emotion data.

[1278] Specific actions

[1279] The server updates the recommendation list based on the user's preferred mode of transportation based on the emotion data. For example, if the user prefers electric bicycles, electric bicycles will be prioritized in the recommendation list.

[1280] (Application example 2)

[1281] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1282] Optimizing transportation routes and recommending sustainable modes of transportation are important for reducing a city's carbon footprint and providing efficient mobility. However, existing systems do not take into account the user's emotional state, which can lead to lower user satisfaction and stress levels. This reduces users' willingness to use the recommended modes of transportation, resulting in a problem of reduced effectiveness of the overall system.

[1283] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1284] In this invention, the server includes means for analyzing traffic data using an AI algorithm, means for optimizing a traffic route based on the analyzed traffic data, means for recommending a sustainable means of transportation based on the optimized traffic route, emotion recognition means for analyzing a user's emotion, and means for optimizing the recommended sustainable means of transportation based on the user's emotion data. This allows the server to recommend the optimal means of transportation according to the user's emotional state, thereby improving user satisfaction and system efficiency.

[1285] Key Word Definitions

[1286] An "AI algorithm" is a computational procedure that uses artificial intelligence technology to analyze data and discover specific patterns.

[1287] "Traffic data" is a collection of data containing information related to traffic, such as location information and traffic conditions.

[1288] "Means for optimizing traffic routes" refers to a method for calculating optimal routes based on analyzed traffic data.

[1289] "Sustainable transport" is transport designed to minimise environmental impact, including public transport and electric vehicles.

[1290] "Emotion recognition means" refers to technology for detecting and analyzing a user's emotions, and uses techniques such as facial recognition and voice analysis.

[1291] "User emotion data" is data that indicates the user's emotional state, and is acquired in the form of an image, sound, text, or the like.

[1292] The "means for optimizing sustainable transportation" is a method for adjusting recommendations for sustainable transportation based on user sentiment data.

[1293] A "data frame" is a two-dimensional data structure consisting of rows and columns, used for data processing and analysis.

[1294] "Location data" is information that indicates the latitude and longitude of a specific point.

[1295] MODE FOR CARRYING OUT THE INVENTION

[1296] This invention relates to a system that uses AI algorithms to analyze traffic data and recommend optimal routes and sustainable transportation methods based on user emotion data. This system is implemented with the following components, including a server, a terminal, and a user.

[1297] 1. Loading and analyzing traffic data

[1298] The server loads and analyzes traffic data, which includes information such as location and traffic conditions. The server manages the traffic data using a Pandas data frame and classifies the data points using the K-means clustering algorithm.

[1299] 2. Traffic route optimization

[1300] The server generates the optimal traffic route based on the analyzed traffic data. At this stage, it calculates the average value of the location data (latitude and longitude) of each cluster to determine the centroid. This allows the optimal traffic route to be designed.

[1301] 3. Promoting sustainable transportation

[1302] The server recommends sustainable transportation modes based on the optimized transportation route. The recommended transportation modes are randomly selected from a predefined list. The recommended transportation modes are recorded in a data frame.

[1303] 4. Emotion analysis

[1304] The server collects emotion data from users' smartphones and other devices and analyzes it using an emotion engine, which uses technologies such as facial recognition and voice analysis to recognize the user's emotions.

[1305] 5. Emotion-Based Optimization

[1306] The server optimizes the recommended sustainable transportation modes based on the sentiment data: if the user has a positive sentiment towards a particular transportation mode, it prioritizes the recommendation of that mode.

[1307] Specific examples

[1308] Transportation data is collected from multiple locations in a city using latitude and longitude data. This data is loaded onto a server and the K-means clustering algorithm is applied to separate the data points into clusters. The average latitude and longitude of each cluster is calculated to determine the centroid. The optimal sustainable transportation mode (e.g., electric bus or electric bicycle) is then recommended for each cluster and the information is recorded in a data frame.

[1309] Furthermore, when users send emotion data to the server via their smartphones, the emotion engine analyzes the data and re-recommends the optimal sustainable transportation mode based on the emotion data. In this way, the system can provide the optimal transportation mode that improves user satisfaction.

[1310] Prompt Sentence Examples

[1311] This includes examples of inputting prompts like the following into a generative AI model:

[1312] Create a system that suggests sustainable transportation options based on the user's emotional data ('happy', 'relaxed', etc.). For example:

[1313] If the user's emotion is "happy," suggest an electric bus.

[1314] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1315] System processing flow

[1316] Step 1:

[1317] The server loads the traffic data. The traffic data is read from a CSV file and converted into a Pandas data frame. The input at this stage is traffic data including location information and traffic conditions, and the output is traffic data in data frame format.

[1318] Step 2:

[1319] The server applies the K-means clustering algorithm to the loaded traffic data. This process classifies each data point into a specific cluster. The input is the data frame obtained in step 1, and the output is a data frame with the cluster identifiers added.

[1320] Step 3:

[1321] The server calculates the average latitude and longitude of each cluster, which determines the center point (centroid) of the cluster. The input is the data frame with cluster identifiers obtained in step 2, and the output is the centroid position data of each cluster.

[1322] Step 4:

[1323] The server recommends sustainable transportation modes based on the optimized transportation route. A predefined list of sustainable transportation modes (e.g., electric bus, electric bicycle, tram) is used in this stage. The input is the centroid location data for each cluster, and the output is a data frame with the recommended transportation modes added.

[1324] Step 5:

[1325] The device collects the user's emotional data. This emotional data is obtained from smartphones and other devices and uses technologies such as facial recognition and voice analysis. The input is the user's biometric data and voice data, and the output is analyzed emotional data.

[1326] Step 6:

[1327] The server analyzes the user's emotional data using an emotion engine, which identifies the user's current emotional state. The input is the emotional data obtained in step 5, and the output is the user's emotional state (e.g., happy, relaxed, etc.).

[1328] Step 7:

[1329] The server optimizes the recommended sustainable transportation modes based on the user's emotional data. For example, if the user is happy, it preferentially recommends electric buses. The input is the emotional state obtained in step 6 and the recommended transportation modes obtained in step 4, and the output is a data frame with the optimized transportation modes added based on the emotional state.

[1330] Specifically, the smartphone app captures the user's face with a camera and sends the image data to the server. The server then performs emotion recognition and analyzes the user's emotional state in real time. It then suggests an appropriate transportation method and sends a notification to the user's smartphone.

[1331] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1332] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1333] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1334] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1335] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1336] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1337] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1338] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1339] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1340] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1341] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1342] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1343] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1344] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1345] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1346] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1347] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1348] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1349] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1350] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1351] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1352] The following is further disclosed regarding the above embodiment.

[1353] (Claim 1)

[1354] A means of analyzing traffic data using AI algorithms;

[1355] a means for optimizing traffic routes based on the analyzed traffic data;

[1356] A means of recommending sustainable transport modes based on optimized transport routes;

[1357] A system including:

[1358] (Claim 2)

[1359] 10. The system of claim 1, further comprising: means for recording the recommended sustainable transportation mode in the data frame.

[1360] (Claim 3)

[1361] 10. The system of claim 1, further comprising means for calculating an average latitude and longitude for each cluster.

[1362] "Example 1"

[1363] (Claim 1)

[1364] a means for collecting traffic data and converting it into a data frame;

[1365] a means for analyzing the collected traffic data using a clustering algorithm;

[1366] means for calculating the average latitude and longitude of the data points belonging to each cluster;

[1367] a means for optimizing traffic routes based on the calculated cluster center points;

[1368] A means for recommending sustainable transportation modes based on optimized transportation routes;

[1369] A system including:

[1370] (Claim 2)

[1371] 10. The system of claim 1, further comprising: means for recording the recommended sustainable transportation mode in the data frame.

[1372] (Claim 3)

[1373] 10. The system of claim 1, further comprising means for calculating an average latitude and longitude for each cluster.

[1374] "Application Example 1"

[1375] (Claim 1)

[1376] A means of analyzing traffic data using AI algorithms;

[1377] a means for optimizing traffic routes based on the analyzed traffic data;

[1378] A means of recommending sustainable transport modes based on optimized transport routes;

[1379] A means for analyzing traffic patterns using a K-means clustering algorithm;

[1380] A means to display the best route through an application installed on a smartphone;

[1381] A system including:

[1382] (Claim 2)

[1383] 10. The system of claim 1, further comprising: means for recording the recommended sustainable transportation mode in the data frame.

[1384] (Claim 3)

[1385] 10. The system of claim 1, further comprising means for calculating an average latitude and longitude for each cluster.

[1386] (Claim 4)

[1387] 10. The system of claim 1, further comprising means for collecting and analyzing traffic data in real time.

[1388] (Claim 5)

[1389] 10. The system of claim 1, further comprising means for displaying and suggesting sustainable transportation options.

[1390] "Example 2: Combining Emotion Engines"

[1391] (Claim 1)

[1392] a means for collecting and converting traffic data into a data frame format;

[1393] means for applying a clustering algorithm to the transformed traffic data to classify the data points into clusters;

[1394] A means to calculate the center point (average latitude and longitude) of each cluster;

[1395] Random selection of sustainable transport modes and transport mode recommendations;

[1396] means for analyzing user emotion data;

[1397] a means for optimizing the recommended transportation options based on the sentiment data;

[1398] A system including:

[1399] (Claim 2)

[1400] 10. The system of claim 1, further comprising: means for recording the recommended sustainable transportation mode in the data frame.

[1401] (Claim 3)

[1402] 10. The system of claim 1, further comprising means for collecting emotion data from a user's terminal and analyzing the emotion data using an emotion engine.

[1403] "Application example 2 when combining emotion engines"

[1404] New Claims:

[1405] (Claim 1)

[1406] A means of analyzing traffic data using AI algorithms;

[1407] a means for optimizing traffic routes based on the analyzed traffic data;

[1408] A means of recommending sustainable transport modes based on optimized transport routes;

[1409] emotion recognition means for analyzing the emotion of a user;

[1410] A means for optimizing sustainable transportation recommendations based on user sentiment data;

[1411] A system including:

[1412] (Claim 2)

[1413] 10. The system of claim 1, further comprising: means for recording the recommended sustainable transportation mode in the data frame.

[1414] (Claim 3)

[1415] 10. The system of claim 1, further comprising means for calculating a mean value of the location data for each cluster. [Explanation of symbols]

[1416] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of analyzing traffic data using AI algorithms; a means for optimizing traffic routes based on the analyzed traffic data; A means of recommending sustainable transport modes based on optimized transport routes; A system including:

2. The system of claim 1 , further comprising means for recording the recommended sustainable transportation mode in a data frame.

3. The system of claim 1 further comprising means for calculating the average latitude and longitude of each cluster.

Images