System

Abstract

An object of a system according to an embodiment is to analyze a traffic situation in a city and provide optimal route guidance or an operation plan of public transportation.SOLUTION: A system according to an embodiment includes a traffic data collection unit, a traffic situation analysis unit, a route guidance unit, an operation planning unit, and a congestion information provision unit. The traffic data collection unit collects traffic data in a city. The traffic situation analysis unit analyzes the traffic data collected by the traffic data collection unit. The route guidance unit guides an optimum route based on the traffic situation analyzed by the traffic situation analysis unit. The operation planning unit makes an operation plan of public transportation on the basis of the data analyzed by the traffic situation analysis unit. The congestion information providing unit provides congestion information based on the data analyzed by the traffic situation analysis unit.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] Conventional technologies have had the problem of making it difficult to efficiently alleviate traffic congestion in cities and crowding on public transportation.

[0005] The system according to the embodiment aims to analyze traffic conditions within a city and provide optimal route guidance and operation plans for public transportation. [Means for solving the problem]

[0006] The system according to the embodiment includes a traffic data collection unit, a traffic situation analysis unit, a route guidance unit, an operation planning unit, and a congestion information provision unit. The traffic data collection unit collects traffic data within a city. The traffic situation analysis unit analyzes the traffic data collected by the traffic data collection unit. The route guidance unit provides guidance on an optimal route based on the traffic situation analyzed by the traffic situation analysis unit. The operation planning unit creates an operation plan for public transportation based on the data analyzed by the traffic situation analysis unit. The congestion information provision unit provides congestion information based on the data analyzed by the traffic situation analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze traffic conditions within a city and provide optimal route guidance and public transportation operation plans. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The urban transportation optimization system according to an embodiment of the present invention collects and analyzes urban transportation data in real time, and provides optimal route guidance, public transportation operation plans, and congestion information. As a result, the urban transportation optimization system can eliminate traffic congestion and realize efficient travel.

[0029] An urban transportation optimization system according to an embodiment includes a traffic data collection unit, a traffic situation analysis unit, a route guidance unit, an operation planning unit, and a congestion information provision unit. The traffic data collection unit collects traffic data within a city. For example, the traffic data collection unit collects traffic volume data, vehicle location information, traffic light status, and the like, obtained from cameras and sensors on roads. The traffic data collection unit can also collect GPS data and smartphone location information. For example, the traffic data collection unit monitors traffic volume in real time using cameras and sensors and collects data. The GPS data is used to obtain vehicle location information in real time and understand traffic conditions. The smartphone location information is used to analyze commuter movement patterns. The traffic situation analysis unit analyzes the traffic data collected by the traffic data collection unit. For example, the generation AI analyzes the traffic data to understand current traffic conditions. The generation AI can also learn from past traffic data and predict future traffic congestion. The generation AI can also analyze weather data and event information to improve the accuracy of traffic situation predictions. For example, the generation AI identifies and predicts traffic congestion locations based on the traffic data. By studying past traffic data, the system analyzes traffic congestion patterns that occur on specific days and times of the week. By analyzing weather data and event information, the system predicts traffic conditions during rainy weather or when an event is held. The route guidance unit provides optimal route guidance based on the traffic conditions analyzed by the traffic condition analysis unit. For example, when a commuter heads to their destination, the generation AI considers current traffic conditions to suggest the most efficient route. The generation AI can also consider fuel-efficient routes from an eco-driving perspective. The generation AI can also include routes that take road safety and accident risk into account. For example, the generation AI proposes routes with the shortest distance and time based on traffic conditions. From an eco-driving perspective, it prioritizes fuel-efficient roads. It considers road safety and accident risk to prioritize roads with a low accident risk. The operation planning unit creates public transportation operation plans based on the data analyzed by the traffic condition analysis unit. For example, the generation AI analyzes bus and train schedules and generates operation plans tailored to times when there are many users.The generation AI can also learn from past usage data and perform demand forecasts. The generation AI can also refer to successful cases in other cities. For example, the generation AI analyzes bus and train schedules and generates operation plans tailored to busy times. It learns from past usage data and predicts demand for specific days of the week and time periods. It refers to successful cases in other cities and identifies areas for improvement in the operation plans. The congestion information provision unit provides congestion information based on data analyzed by the traffic situation analysis unit. For example, the generation AI analyzes the congestion level of trains and buses and provides congestion information to users. The generation AI can also perform congestion predictions in real time. The generation AI can also analyze congestion patterns based on past congestion data. For example, the generation AI analyzes the congestion level of trains and buses and provides congestion information to users. It performs real-time congestion predictions based on current traffic volume and vehicle location information. It analyzes congestion patterns for specific days of the week and time periods based on past congestion data. As a result, the urban transportation optimization system according to the embodiment can eliminate traffic congestion and achieve efficient travel. For example, the output unit provides users with optimal route guidance and congestion information through web applications or mobile applications. Public transportation operation plans are implemented through operation management systems. Congestion information is provided through electronic bulletin boards and digital signage.

[0030] The traffic condition analysis unit analyzes weather data and event information in addition to traffic data, thereby improving the accuracy of traffic condition predictions. In the traffic condition analysis unit, for example, the generation AI integrates traffic data and weather data to predict traffic conditions on rainy or snowy days. For example, areas where traffic congestion is likely to occur on rainy days are identified and reflected in the prediction. In addition, the generation AI analyzes traffic data and event information to predict traffic conditions when large-scale events are held. For example, it predicts traffic congestion around venues where concerts or sporting events are held. In addition, the generation AI combines and analyzes traffic data, weather data, and event information to predict the occurrence of traffic congestion due to multiple factors. For example, it predicts traffic conditions when an event is held in rainy weather. This improves the accuracy of traffic condition predictions.

[0031] The traffic situation analysis unit learns past traffic patterns and can predict future traffic congestion. For example, the generation AI in the traffic situation analysis unit learns past traffic data and analyzes traffic congestion patterns that occur on specific days of the week and at specific times of the day. For example, it predicts traffic conditions during rush hour on weekdays in the morning and evening. The generation AI in the traffic situation analysis unit also learns traffic congestion patterns associated with specific events and seasons based on past traffic data. For example, it predicts traffic congestion during the New Year holidays and long weekends. The generation AI in the traffic situation analysis unit also analyzes past traffic data and learns traffic congestion patterns that are likely to occur on specific roads and intersections. For example, it predicts waiting times at traffic lights at specific intersections. This makes it possible to predict future traffic congestion.

[0032] The traffic situation analysis unit also analyzes the movements of bicycles and pedestrians within a city, making it possible to grasp the overall traffic situation. For example, the generation AI in the traffic situation analysis unit analyzes bicycle location information to grasp the congestion status of bicycle lanes. For example, it identifies areas where bicycles are concentrated during specific times of the day. The generation AI in the traffic situation analysis unit also analyzes pedestrian movements to grasp the congestion status of pedestrian-only roads and intersections. For example, it identifies areas with a high number of pedestrians during rush hour. The generation AI in the traffic situation analysis unit also analyzes the movements of bicycles and pedestrians to grasp the overall traffic situation. For example, it analyzes the impact of bicycle and pedestrian movements on traffic congestion. This makes it possible to grasp the overall traffic situation.

[0033] The traffic situation analysis unit can link the analyzed traffic data with other systems in the smart city. For example, the traffic situation analysis unit links the traffic data analyzed by the generation AI with an energy management system to optimize energy consumption according to traffic volume. For example, it increases energy supply during times of heavy traffic. The traffic situation analysis unit also links the traffic data analyzed by the generation AI with the smart city's lighting system to adjust lighting according to traffic conditions. For example, it increases lighting on roads with heavy traffic. The traffic situation analysis unit also links the traffic data analyzed by the generation AI with the smart city's garbage collection system to optimize garbage collection routes according to traffic conditions. For example, it suggests garbage collection routes that avoid traffic congestion. This allows for linkage with other systems in the smart city.

[0034] The route guidance unit can take into account fuel-efficient routes from the perspective of eco-driving. For example, the route guidance unit prioritizes fuel-efficient roads for routes proposed by the generation AI. For example, it proposes roads with fewer traffic lights and roads with appropriate speed limits. The route guidance unit also considers routes that avoid steep slopes and traffic jams from the perspective of eco-driving for routes proposed by the generation AI. For example, it proposes flat roads and roads with little traffic. The route guidance unit also proposes routes that take into account fuel-efficient driving methods for routes proposed by the generation AI. For example, it proposes roads that can be driven at a constant speed and routes recommended for eco-driving. This makes it possible to take into account fuel-efficient routes.

[0035] The route guidance unit can include routes that take into consideration road safety and accident risk. For example, the route guidance unit prioritizes roads with a low accident risk in the routes proposed by the generation AI. For example, it proposes safe routes based on past accident data. The route guidance unit also includes routes that take into consideration road safety in the routes proposed by the generation AI. For example, it proposes roads with good lighting and roads with good visibility. The route guidance unit also considers routes that reduce the risk of traffic accidents in the routes proposed by the generation AI. For example, it proposes roads with few intersections and roads with many traffic lights. This makes it possible to include routes that take safety and accident risk into consideration.

[0036] The route guidance unit can combine information on tourist spots, restaurants, etc. to provide route guidance for tourists. For example, the route guidance unit combines information on tourist spots and restaurants with the route proposed by the generation AI to provide route guidance for tourists. For example, it proposes a route that passes through tourist spots and popular restaurants. The route guidance unit also combines information for tourists with the route proposed by the generation AI to introduce the highlights of tourist destinations. For example, it provides information on the history and culture of the tourist destination. The route guidance unit also combines information on tourist spots and restaurants with the route proposed by the generation AI to propose routes that tourists can enjoy. For example, it proposes routes with beautiful scenery or routes where you can enjoy local specialties. This makes it possible to provide route guidance for tourists.

[0037] The route guidance unit can include a hybrid route that recommends the use of public transportation. For example, the route guidance unit includes a hybrid route that recommends the use of public transportation in the route proposed by the generation AI. For example, it proposes a route by driving to a station and then taking a train from there. The route guidance unit also includes a hybrid route that combines the use of public transportation and bicycles in the route proposed by the generation AI. For example, it proposes a route by bicycle from a station to a destination. The route guidance unit also includes a hybrid route that combines the use of public transportation and walking in the route proposed by the generation AI. For example, it proposes a route by walking from a bus stop to a destination. In this way, it is possible to include a hybrid route that recommends the use of public transportation.

[0038] The operation planning unit can learn from past usage data and perform demand forecasting. In the operation planning unit, for example, the generation AI learns from past usage data and predicts demand for public transportation on specific days of the week and time periods. For example, it predicts demand during rush hours in the morning and evening on weekdays. In addition, the operation planning unit uses the generation AI to predict demand for public transportation related to specific events or seasons based on past usage data. For example, it predicts demand during the New Year holidays and long weekends. In addition, the operation planning unit uses the generation AI to analyze past usage data and predict demand for specific routes and stations. For example, it predicts the number of passengers boarding and alighting at specific stations. This makes it possible to perform demand forecasting.

[0039] The operation planning unit can refer to successful cases from other cities. For example, the generation AI in the operation planning unit analyzes the operation plans of public transportation systems in other cities and uses these successful cases as reference. For example, it learns the operation plans of cities with less traffic congestion and reflects them in the operation plan of its own city. The generation AI in the operation planning unit also identifies areas for improvement in the operation plan based on successful cases from other cities. For example, it optimizes bus operation frequency and route settings. The generation AI in the operation planning unit also refers to the operation plans of other cities and makes suggestions to improve user convenience. For example, it proposes an operation schedule that makes transfers more convenient. This allows the operation planning unit to refer to successful cases from other cities.

[0040] The operation planning unit can work with shared ride and car sharing services. For example, the generation AI in the operation planning unit works with shared ride services to optimize public transportation operation plans. For example, it adjusts bus operation frequency to match demand for shared rides. The generation AI in the operation planning unit also works with car sharing services to optimize public transportation operation plans. For example, it adjusts bus and train operation schedules according to car sharing usage. The generation AI in the operation planning unit also works with shared ride and car sharing services to propose operation plans to increase user convenience. For example, it links shared ride pickup points with public transportation transfer points. This allows it to work with shared ride and car sharing services.

[0041] The operation planning unit can take into account the city's event schedule. In the operation planning unit, for example, the generation AI analyzes the city's event schedule and optimizes the public transportation operation plan when an event is held. For example, it increases the number of special services when a large-scale event is held. In addition, the operation planning unit predicts user travel demand based on the event schedule and adjusts the operation plan. For example, it proposes an operation schedule to accommodate the rush hour for people returning home after the event ends. In addition, the operation planning unit considers the city's event schedule and proposes an operation plan to increase user convenience. For example, it proposes an operation route that takes into account access to the event venue. This makes it possible to take into account the city's event schedule.

[0042] The congestion information providing unit can make congestion predictions in real time. In the congestion information providing unit, for example, the generation AI analyzes traffic data in real time and makes congestion predictions. For example, congestion predictions are made based on current traffic volume and vehicle location information. In addition, the congestion information providing unit analyzes the usage status of public transportation in real time and makes congestion predictions. For example, congestion predictions are made based on the occupancy rates of trains and buses. In addition, the congestion information providing unit analyzes weather data and event information in real time and makes congestion predictions. For example, congestion predictions are made on rainy days or when events are held. This makes it possible to make congestion predictions in real time.

[0043] The congestion information providing unit can analyze congestion patterns based on past congestion data. In the congestion information providing unit, for example, the generation AI analyzes past congestion data and identifies congestion patterns on specific days of the week and time periods. For example, it analyzes congestion patterns during weekday morning and evening rush hours. In addition, the congestion information providing unit analyzes congestion patterns related to specific events or seasons based on past congestion data. For example, it analyzes congestion patterns during the New Year holidays and long weekends. In addition, the congestion information providing unit analyzes past congestion data and identifies congestion patterns on specific lines and stations. For example, it analyzes fluctuations in the number of passengers boarding and alighting at specific stations. This makes it possible to analyze congestion patterns based on past congestion data.

[0044] The congestion information provision unit can suggest alternative routes or alternative means to avoid congestion. For example, the generation AI suggests alternative routes to avoid congestion based on congestion information. For example, it recommends using roads with less traffic or public transportation. The congestion information provision unit also suggests alternative means to avoid congestion based on congestion information. For example, it recommends traveling by bicycle or on foot. The congestion information provision unit also suggests multiple alternative routes or alternative means to avoid congestion based on congestion information. For example, it suggests a hybrid route that combines car and public transportation. This makes it possible to suggest alternative routes or alternative means to avoid congestion.

[0045] The congestion information providing unit can provide an interface that visually displays the congestion status. For example, the generation AI provides an interface that visually displays congestion information, allowing the user to intuitively understand the congestion status. For example, the congestion level is displayed in color on a map. The congestion information providing unit also provides an interface in which the generation AI updates and visually displays congestion information in real time. For example, it provides a map that reflects changes in the congestion status in real time. The congestion information providing unit also provides an interface in which the generation AI visually displays routes that the user can take to avoid congestion based on the congestion information. For example, it highlights routes with less congestion. This makes it possible to provide an interface that visually displays the congestion status.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The urban transportation optimization system can further include an energy consumption optimization unit. The energy consumption optimization unit optimizes energy consumption within the city based on data obtained from the transportation data collection unit. For example, it can adjust energy supply to suit times of heavy traffic. The energy consumption optimization unit can also optimize energy consumption based on public transportation schedules. For example, it can adjust energy supply to suit the operation of buses and trains. Furthermore, the energy consumption optimization unit can analyze energy consumption patterns within the city and improve energy efficiency. For example, it can identify areas where energy consumption is concentrated during specific times of the day and optimize energy supply.

[0048] The urban transportation optimization system can further include a health promotion unit. The health promotion unit promotes health within the city based on data obtained from the transportation data collection unit. For example, it can analyze the movements of pedestrians and cyclists and recommend healthy means of transportation. The health promotion unit can also analyze air quality data within the city and propose health-conscious routes. For example, it can propose routes that pass through areas with good air quality. Furthermore, the health promotion unit can identify areas within the city that promote exercise and support a healthy lifestyle. For example, it can propose routes that take into account access to parks and exercise facilities.

[0049] The urban traffic optimization system can further include an emergency response unit. The emergency response unit responds to emergencies based on data obtained from the traffic data collection unit. For example, it can propose optimal evacuation routes in the event of a traffic accident or natural disaster. The emergency response unit can also provide routes to prioritize emergency vehicles. For example, it can propose routes that allow ambulances and fire engines to reach the scene quickly. Furthermore, the emergency response unit can monitor traffic conditions in real time when an emergency occurs in the city and take appropriate measures. For example, it can dynamically adjust routes to avoid traffic congestion.

[0050] The urban traffic optimization system may further include an environmental monitoring unit. The environmental monitoring unit monitors the environmental conditions within the city based on data obtained from the traffic data collection unit. For example, it can analyze the level of air pollution according to traffic volume and propose environmentally friendly routes. The environmental monitoring unit can also analyze noise levels within the city and propose quieter routes. For example, it can propose routes that pass through areas with less noise. Furthermore, the environmental monitoring unit can analyze the usage of green spaces and parks within the city and propose routes that contribute to environmental protection. For example, it can preferentially propose routes that pass through green spaces.

[0051] The urban traffic optimization system may further include a commercial promotion unit. The commercial promotion unit promotes commercial activity within the city based on data obtained from the traffic data collection unit. For example, it can analyze the usage status of commercial facilities in areas with high traffic volume and optimize commercial activity. The commercial promotion unit can also analyze the congestion status of commercial facilities during specific time periods and propose optimal shopping routes to users. For example, it can recommend shopping during less crowded times. Furthermore, the commercial promotion unit can analyze event information within the city and make proposals to stimulate commercial activity. For example, it can propose routes that take into account access to commercial facilities when events are held.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The traffic data collection unit collects traffic data within the city. For example, it collects traffic volume data obtained from cameras and sensors on the road, vehicle location information, traffic light status, etc. It can also collect GPS data and smartphone location information. This allows it to monitor traffic volume and collect data in real time. Step 2: The traffic situation analysis unit analyzes the traffic data collected by the traffic data collection unit. For example, it uses generative AI to understand the current traffic situation and learns from past traffic data to predict future traffic congestion. It also analyzes weather data and event information to improve the accuracy of traffic situation predictions. Step 3: The route guidance section provides optimal route guidance based on the traffic conditions analyzed by the traffic analysis section. For example, when a commuter heads to their destination, the generation AI can suggest the most efficient route taking into account the current traffic conditions. It can also include fuel-efficient routes from an eco-driving perspective, as well as routes that take into account road safety and accident risk. Step 4: The operation planning unit creates a public transportation operation plan based on the data analyzed by the traffic situation analysis unit. For example, the generation AI analyzes bus and train schedules and generates an operation plan tailored to the time periods when there are many users. It can also study past usage data and perform demand forecasts. Step 5: The congestion information provision unit provides congestion information based on the data analyzed by the traffic situation analysis unit. For example, the generation AI analyzes the congestion level of trains and buses and provides congestion information to the user. It can also perform real-time congestion predictions and analyze congestion patterns based on past congestion data.

[0054] (Example 2) The urban transportation optimization system according to an embodiment of the present invention collects and analyzes urban transportation data in real time, and provides optimal route guidance, public transportation operation plans, and congestion information. As a result, the urban transportation optimization system can eliminate traffic congestion and realize efficient travel.

[0055] An urban transportation optimization system according to an embodiment includes a traffic data collection unit, a traffic situation analysis unit, a route guidance unit, an operation planning unit, and a congestion information provision unit. The traffic data collection unit collects traffic data within a city. For example, the traffic data collection unit collects traffic volume data, vehicle location information, traffic light status, and the like, obtained from cameras and sensors on roads. The traffic data collection unit can also collect GPS data and smartphone location information. For example, the traffic data collection unit monitors traffic volume in real time using cameras and sensors and collects data. The GPS data is used to obtain vehicle location information in real time and understand traffic conditions. The smartphone location information is used to analyze commuter movement patterns. The traffic situation analysis unit analyzes the traffic data collected by the traffic data collection unit. For example, the generation AI analyzes the traffic data to understand current traffic conditions. The generation AI can also learn from past traffic data and predict future traffic congestion. The generation AI can also analyze weather data and event information to improve the accuracy of traffic situation predictions. For example, the generation AI identifies and predicts traffic congestion locations based on the traffic data. By studying past traffic data, the system analyzes traffic congestion patterns that occur on specific days and times of the week. By analyzing weather data and event information, the system predicts traffic conditions during rainy weather or when an event is held. The route guidance unit provides optimal route guidance based on the traffic conditions analyzed by the traffic condition analysis unit. For example, when a commuter heads to their destination, the generation AI considers current traffic conditions to suggest the most efficient route. The generation AI can also consider fuel-efficient routes from an eco-driving perspective. The generation AI can also include routes that take road safety and accident risk into account. For example, the generation AI proposes routes with the shortest distance and time based on traffic conditions. From an eco-driving perspective, it prioritizes fuel-efficient roads. It considers road safety and accident risk to prioritize roads with a low accident risk. The operation planning unit creates public transportation operation plans based on the data analyzed by the traffic condition analysis unit. For example, the generation AI analyzes bus and train schedules and generates operation plans tailored to times when there are many users.The generation AI can also learn from past usage data and perform demand forecasts. The generation AI can also refer to successful cases in other cities. For example, the generation AI analyzes bus and train schedules and generates operation plans tailored to busy times. It learns from past usage data and predicts demand for specific days of the week and time periods. It refers to successful cases in other cities and identifies areas for improvement in the operation plans. The congestion information provision unit provides congestion information based on data analyzed by the traffic situation analysis unit. For example, the generation AI analyzes the congestion level of trains and buses and provides congestion information to users. The generation AI can also perform congestion predictions in real time. The generation AI can also analyze congestion patterns based on past congestion data. For example, the generation AI analyzes the congestion level of trains and buses and provides congestion information to users. It performs real-time congestion predictions based on current traffic volume and vehicle location information. It analyzes congestion patterns for specific days of the week and time periods based on past congestion data. As a result, the urban transportation optimization system according to the embodiment can eliminate traffic congestion and achieve efficient travel. For example, the output unit provides users with optimal route guidance and congestion information through web applications or mobile applications. Public transportation operation plans are implemented through operation management systems. Congestion information is provided through electronic bulletin boards and digital signage.

[0056] The traffic condition analysis unit analyzes weather data and event information in addition to traffic data, thereby improving the accuracy of traffic condition predictions. In the traffic condition analysis unit, for example, the generation AI integrates traffic data and weather data to predict traffic conditions on rainy or snowy days. For example, areas where traffic congestion is likely to occur on rainy days are identified and reflected in the prediction. In addition, the generation AI analyzes traffic data and event information to predict traffic conditions when large-scale events are held. For example, it predicts traffic congestion around venues where concerts or sporting events are held. In addition, the generation AI combines and analyzes traffic data, weather data, and event information to predict the occurrence of traffic congestion due to multiple factors. For example, it predicts traffic conditions when an event is held in rainy weather. This improves the accuracy of traffic condition predictions.

[0057] The traffic situation analysis unit learns past traffic patterns and can predict future traffic congestion. For example, the generation AI in the traffic situation analysis unit learns past traffic data and analyzes traffic congestion patterns that occur on specific days of the week and at specific times of the day. For example, it predicts traffic conditions during rush hour on weekdays in the morning and evening. The generation AI in the traffic situation analysis unit also learns traffic congestion patterns associated with specific events and seasons based on past traffic data. For example, it predicts traffic congestion during the New Year holidays and long weekends. The generation AI in the traffic situation analysis unit also analyzes past traffic data and learns traffic congestion patterns that are likely to occur on specific roads and intersections. For example, it predicts waiting times at traffic lights at specific intersections. This makes it possible to predict future traffic congestion.

[0058] The traffic situation analysis unit can use the emotion estimation function to collect emotional data of drivers and commuters and suggest less stressful routes. For example, the traffic situation analysis unit can use the emotion estimation function to analyze the stress level from the driver's facial expressions and voice and suggest less stressful routes. For example, it can preferentially suggest routes that avoid traffic jams. The traffic situation analysis unit can also use the emotion estimation function to collect emotional data of commuters and recommend the use of less stressful public transportation. For example, it can suggest trains or buses during less crowded times. The traffic situation analysis unit can also use the emotion estimation function to analyze the emotional data of drivers and commuters in real time and dynamically adjust less stressful routes. For example, it can re-suggest routes depending on changes in traffic conditions. This makes it possible to suggest less stressful routes.

[0059] The traffic situation analysis unit also analyzes the movements of bicycles and pedestrians within a city, making it possible to grasp the overall traffic situation. For example, the generation AI in the traffic situation analysis unit analyzes bicycle location information to grasp the congestion status of bicycle lanes. For example, it identifies areas where bicycles are concentrated during specific times of the day. The generation AI in the traffic situation analysis unit also analyzes pedestrian movements to grasp the congestion status of pedestrian-only roads and intersections. For example, it identifies areas with a high number of pedestrians during rush hour. The generation AI in the traffic situation analysis unit also analyzes the movements of bicycles and pedestrians to grasp the overall traffic situation. For example, it analyzes the impact of bicycle and pedestrian movements on traffic congestion. This makes it possible to grasp the overall traffic situation.

[0060] The traffic situation analysis unit can link the analyzed traffic data with other systems in the smart city. For example, the traffic situation analysis unit links the traffic data analyzed by the generation AI with an energy management system to optimize energy consumption according to traffic volume. For example, it increases energy supply during times of heavy traffic. The traffic situation analysis unit also links the traffic data analyzed by the generation AI with the smart city's lighting system to adjust lighting according to traffic conditions. For example, it increases lighting on roads with heavy traffic. The traffic situation analysis unit also links the traffic data analyzed by the generation AI with the smart city's garbage collection system to optimize garbage collection routes according to traffic conditions. For example, it suggests garbage collection routes that avoid traffic congestion. This allows for linkage with other systems in the smart city.

[0061] The traffic situation analysis unit can use the emotion estimation function to collect citizens' emotional reactions to traffic conditions and use the collected data to improve transportation policies. The traffic situation analysis unit, for example, uses the emotion estimation function to collect citizens' emotional reactions to traffic conditions and use the collected data to improve transportation policies. For example, it analyzes citizens' stress levels due to traffic congestion. The traffic situation analysis unit also uses the emotion estimation function to evaluate the effectiveness of transportation policies based on citizens' emotional data. For example, it analyzes the impact of new traffic regulations on citizens' emotions. The traffic situation analysis unit also uses the emotion estimation function to identify areas for improvement in transportation policies based on citizens' emotional reactions. For example, it proposes transportation policies to reduce citizens' stress. This helps improve transportation policies.

[0062] The route guidance unit can take into account fuel-efficient routes from the perspective of eco-driving. For example, the route guidance unit prioritizes fuel-efficient roads for routes proposed by the generation AI. For example, it proposes roads with fewer traffic lights and roads with appropriate speed limits. The route guidance unit also considers routes that avoid steep slopes and traffic jams from the perspective of eco-driving for routes proposed by the generation AI. For example, it proposes flat roads and roads with little traffic. The route guidance unit also proposes routes that take into account fuel-efficient driving methods for routes proposed by the generation AI. For example, it proposes roads that can be driven at a constant speed and routes recommended for eco-driving. This makes it possible to take into account fuel-efficient routes.

[0063] The route guidance unit can include routes that take into consideration road safety and accident risk. For example, the route guidance unit prioritizes roads with a low accident risk in the routes proposed by the generation AI. For example, it proposes safe routes based on past accident data. The route guidance unit also includes routes that take into consideration road safety in the routes proposed by the generation AI. For example, it proposes roads with good lighting and roads with good visibility. The route guidance unit also considers routes that reduce the risk of traffic accidents in the routes proposed by the generation AI. For example, it proposes roads with few intersections and roads with many traffic lights. This makes it possible to include routes that take safety and accident risk into consideration.

[0064] The route guidance unit can use the emotion estimation function to suggest a route for reducing the user's stress level. For example, the route guidance unit uses the emotion estimation function to analyze the user's stress level and suggest a less stressful route. For example, it suggests roads with less traffic or roads with beautiful scenery. The route guidance unit also uses the emotion estimation function to suggest a route for reducing stress based on the user's emotion data. For example, it suggests a road with fewer traffic lights or a route that avoids traffic jams. The route guidance unit also uses the emotion estimation function to analyze the user's stress level in real time and dynamically adjust a less stressful route. For example, it re-suggests a route depending on changes in traffic conditions. In this way, it is possible to suggest a route for reducing the user's stress level.

[0065] The route guidance unit can combine information on tourist spots, restaurants, etc. to provide route guidance for tourists. For example, the route guidance unit combines information on tourist spots and restaurants with the route proposed by the generation AI to provide route guidance for tourists. For example, it proposes a route that passes through tourist spots and popular restaurants. The route guidance unit also combines information for tourists with the route proposed by the generation AI to introduce the highlights of tourist destinations. For example, it provides information on the history and culture of the tourist destination. The route guidance unit also combines information on tourist spots and restaurants with the route proposed by the generation AI to propose routes that tourists can enjoy. For example, it proposes routes with beautiful scenery or routes where you can enjoy local specialties. This makes it possible to provide route guidance for tourists.

[0066] The route guidance unit can include a hybrid route that recommends the use of public transportation. For example, the route guidance unit includes a hybrid route that recommends the use of public transportation in the route proposed by the generation AI. For example, it proposes a route by driving to a station and then taking a train from there. The route guidance unit also includes a hybrid route that combines the use of public transportation and bicycles in the route proposed by the generation AI. For example, it proposes a route by bicycle from a station to a destination. The route guidance unit also includes a hybrid route that combines the use of public transportation and walking in the route proposed by the generation AI. For example, it proposes a route by walking from a bus stop to a destination. In this way, it is possible to include a hybrid route that recommends the use of public transportation.

[0067] The route guidance unit can use the emotion estimation function to propose a route that suits the user's preferences. For example, the route guidance unit uses the emotion estimation function to propose a route that suits the user's preferences. For example, it proposes a scenic route or a route that passes through tourist attractions. The route guidance unit also uses the emotion estimation function to propose a route that suits the user's preferences based on the user's emotion data. For example, it proposes a route on a quiet road or with lots of nature. The route guidance unit also uses the emotion estimation function to adjust the route that suits the user's preferences in real time. For example, it re-proposes a scenic route in response to changes in traffic conditions. This makes it possible to propose a route that suits the user's preferences.

[0068] The operation planning unit can learn from past usage data and perform demand forecasting. In the operation planning unit, for example, the generation AI learns from past usage data and predicts demand for public transportation on specific days of the week and time periods. For example, it predicts demand during rush hours in the morning and evening on weekdays. In addition, the operation planning unit uses the generation AI to predict demand for public transportation related to specific events or seasons based on past usage data. For example, it predicts demand during the New Year holidays and long weekends. In addition, the operation planning unit uses the generation AI to analyze past usage data and predict demand for specific routes and stations. For example, it predicts the number of passengers boarding and alighting at specific stations. This makes it possible to perform demand forecasting.

[0069] The operation planning unit can refer to successful cases from other cities. For example, the generation AI in the operation planning unit analyzes the operation plans of public transportation systems in other cities and uses these successful cases as reference. For example, it learns the operation plans of cities with less traffic congestion and reflects them in the operation plan of its own city. The generation AI in the operation planning unit also identifies areas for improvement in the operation plan based on successful cases from other cities. For example, it optimizes bus operation frequency and route settings. The generation AI in the operation planning unit also refers to the operation plans of other cities and makes suggestions to improve user convenience. For example, it proposes an operation schedule that makes transfers more convenient. This allows the operation planning unit to refer to successful cases from other cities.

[0070] The operation planning unit can use the emotion estimation function to propose an operation plan to increase user satisfaction. The operation planning unit, for example, uses the emotion estimation function to collect user emotion data and propose an operation plan to increase satisfaction. For example, the operation planning unit proposes an operation schedule to avoid congestion. The operation planning unit also uses the emotion estimation function to identify improvements to the operation plan based on the user emotion data. For example, the operation planning unit proposes an operation route to reduce user stress. The operation planning unit also uses the emotion estimation function to adjust the operation plan in real time to increase user satisfaction. For example, the operation schedule is re-proposed in response to changes in the user's emotion. This makes it possible to propose an operation plan to increase user satisfaction.

[0071] The operation planning unit can work with shared ride and car sharing services. For example, the generation AI in the operation planning unit works with shared ride services to optimize public transportation operation plans. For example, it adjusts bus operation frequency to match demand for shared rides. The generation AI in the operation planning unit also works with car sharing services to optimize public transportation operation plans. For example, it adjusts bus and train operation schedules according to car sharing usage. The generation AI in the operation planning unit also works with shared ride and car sharing services to propose operation plans to increase user convenience. For example, it links shared ride pickup points with public transportation transfer points. This allows it to work with shared ride and car sharing services.

[0072] The operation planning unit can take into account the city's event schedule. In the operation planning unit, for example, the generation AI analyzes the city's event schedule and optimizes the public transportation operation plan when an event is held. For example, it increases the number of special services when a large-scale event is held. In addition, the operation planning unit predicts user travel demand based on the event schedule and adjusts the operation plan. For example, it proposes an operation schedule to accommodate the rush hour for people returning home after the event ends. In addition, the operation planning unit considers the city's event schedule and proposes an operation plan to increase user convenience. For example, it proposes an operation route that takes into account access to the event venue. This makes it possible to take into account the city's event schedule.

[0073] The operation planning unit can use the emotion estimation function to propose an operation plan to avoid congestion based on the user's emotion data. The operation planning unit, for example, uses the emotion estimation function to collect the user's emotion data and propose an operation plan to avoid congestion. For example, the operation planning unit proposes an operation schedule to reduce stress during congestion. The operation planning unit also uses the emotion estimation function to propose an operation route to avoid congestion based on the user's emotion data. For example, it prioritizes proposing time periods and routes with less congestion. The operation planning unit also uses the emotion estimation function to analyze the user's emotion data in real time and dynamically adjust the operation plan to avoid congestion. For example, it re-proposes an operation schedule according to changes in the user's emotion. This makes it possible to propose an operation plan to avoid congestion.

[0074] The congestion information providing unit can make congestion predictions in real time. In the congestion information providing unit, for example, the generation AI analyzes traffic data in real time and makes congestion predictions. For example, congestion predictions are made based on current traffic volume and vehicle location information. In addition, the congestion information providing unit analyzes the usage status of public transportation in real time and makes congestion predictions. For example, congestion predictions are made based on the occupancy rates of trains and buses. In addition, the congestion information providing unit analyzes weather data and event information in real time and makes congestion predictions. For example, congestion predictions are made on rainy days or when events are held. This makes it possible to make congestion predictions in real time.

[0075] The congestion information providing unit can analyze congestion patterns based on past congestion data. In the congestion information providing unit, for example, the generation AI analyzes past congestion data and identifies congestion patterns on specific days of the week and time periods. For example, it analyzes congestion patterns during weekday morning and evening rush hours. In addition, the congestion information providing unit analyzes congestion patterns related to specific events or seasons based on past congestion data. For example, it analyzes congestion patterns during the New Year holidays and long weekends. In addition, the congestion information providing unit analyzes past congestion data and identifies congestion patterns on specific lines and stations. For example, it analyzes fluctuations in the number of passengers boarding and alighting at specific stations. This makes it possible to analyze congestion patterns based on past congestion data.

[0076] The congestion information providing unit can use the emotion estimation function to provide congestion information for reducing the user's stress level. The congestion information providing unit, for example, uses the emotion estimation function to analyze the user's stress level and provide congestion information for reducing stress. For example, it suggests less crowded time periods and routes. The congestion information providing unit also uses the emotion estimation function to provide congestion information for reducing stress based on the user's emotion data. For example, it recommends using less crowded public transportation. The congestion information providing unit also uses the emotion estimation function to analyze the user's stress level in real time and dynamically provide congestion information for reducing stress. For example, it updates the congestion information according to changes in traffic conditions. This makes it possible to provide congestion information for reducing the user's stress level.

[0077] The congestion information provision unit can suggest alternative routes or alternative means to avoid congestion. For example, the generation AI suggests alternative routes to avoid congestion based on congestion information. For example, it recommends using roads with less traffic or public transportation. The congestion information provision unit also suggests alternative means to avoid congestion based on congestion information. For example, it recommends traveling by bicycle or on foot. The congestion information provision unit also suggests multiple alternative routes or alternative means to avoid congestion based on congestion information. For example, it suggests a hybrid route that combines car and public transportation. This makes it possible to suggest alternative routes or alternative means to avoid congestion.

[0078] The congestion information providing unit can provide an interface that visually displays the congestion status. For example, the generation AI provides an interface that visually displays congestion information, allowing the user to intuitively understand the congestion status. For example, the congestion level is displayed in color on a map. The congestion information providing unit also provides an interface in which the generation AI updates and visually displays congestion information in real time. For example, it provides a map that reflects changes in the congestion status in real time. The congestion information providing unit also provides an interface in which the generation AI visually displays routes that the user can take to avoid congestion based on the congestion information. For example, it highlights routes with less congestion. This makes it possible to provide an interface that visually displays the congestion status.

[0079] The congestion information providing unit can use the emotion estimation function to suggest an optimal travel time to avoid congestion based on the user's emotion data. The congestion information providing unit, for example, uses the emotion estimation function to analyze the user's emotion data and suggest an optimal travel time to avoid congestion. For example, it suggests a time period with less stress. The congestion information providing unit also uses the emotion estimation function to suggest an optimal travel time based on the user's emotion data. For example, it suggests a time period or route with less congestion. The congestion information providing unit also uses the emotion estimation function to analyze the user's emotion data in real time and dynamically suggest an optimal travel time. For example, it re-suggests a travel time according to changes in traffic conditions. This makes it possible to suggest an optimal travel time to avoid congestion.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The urban transportation optimization system can further include an energy consumption optimization unit. The energy consumption optimization unit optimizes energy consumption within the city based on data obtained from the transportation data collection unit. For example, it can adjust energy supply to suit times of heavy traffic. The energy consumption optimization unit can also optimize energy consumption based on public transportation schedules. For example, it can adjust energy supply to suit the operation of buses and trains. Furthermore, the energy consumption optimization unit can analyze energy consumption patterns within the city and improve energy efficiency. For example, it can identify areas where energy consumption is concentrated during specific times of the day and optimize energy supply.

[0082] The urban transportation optimization system can further include a health promotion unit. The health promotion unit promotes health within the city based on data obtained from the transportation data collection unit. For example, it can analyze the movements of pedestrians and cyclists and recommend healthy means of transportation. The health promotion unit can also analyze air quality data within the city and propose health-conscious routes. For example, it can propose routes that pass through areas with good air quality. Furthermore, the health promotion unit can identify areas within the city that promote exercise and support a healthy lifestyle. For example, it can propose routes that take into account access to parks and exercise facilities.

[0083] The urban traffic optimization system can further include an emergency response unit. The emergency response unit responds to emergencies based on data obtained from the traffic data collection unit. For example, it can propose optimal evacuation routes in the event of a traffic accident or natural disaster. The emergency response unit can also provide routes to prioritize emergency vehicles. For example, it can propose routes that allow ambulances and fire engines to reach the scene quickly. Furthermore, the emergency response unit can monitor traffic conditions in real time when an emergency occurs in the city and take appropriate measures. For example, it can dynamically adjust routes to avoid traffic congestion.

[0084] The urban traffic optimization system may further include an environmental monitoring unit. The environmental monitoring unit monitors the environmental conditions within the city based on data obtained from the traffic data collection unit. For example, it can analyze the level of air pollution according to traffic volume and propose environmentally friendly routes. The environmental monitoring unit can also analyze noise levels within the city and propose quieter routes. For example, it can propose routes that pass through areas with less noise. Furthermore, the environmental monitoring unit can analyze the usage of green spaces and parks within the city and propose routes that contribute to environmental protection. For example, it can preferentially propose routes that pass through green spaces.

[0085] The urban traffic optimization system may further include a commercial promotion unit. The commercial promotion unit promotes commercial activity within the city based on data obtained from the traffic data collection unit. For example, it can analyze the usage status of commercial facilities in areas with high traffic volume and optimize commercial activity. The commercial promotion unit can also analyze the congestion status of commercial facilities during specific time periods and propose optimal shopping routes to users. For example, it can recommend shopping during less crowded times. Furthermore, the commercial promotion unit can analyze event information within the city and make proposals to stimulate commercial activity. For example, it can propose routes that take into account access to commercial facilities when events are held.

[0086] The urban traffic optimization system can further use an emotion estimation function to provide driving assistance based on the driver's emotional data. For example, the emotion estimation function can be used to analyze the driver's stress level and provide driving advice to reduce stress, such as by suggesting relaxing music. The emotion estimation function can also be used to provide advice to improve attention while driving based on the driver's emotional data, such as by recommending a break if attention is declining. Furthermore, the emotion estimation function can be used to analyze the driver's emotional data in real time and provide advice to improve safety while driving, such as by adjusting the driving style according to changes in emotion.

[0087] The urban transportation optimization system can further use an emotion estimation function to improve services based on the emotional data of public transportation users. For example, the emotion estimation function can be used to analyze a user's stress level and provide services to reduce stress, such as guiding the user to a less crowded vehicle. The emotion estimation function can also be used to provide advice to provide a comfortable travel environment based on the user's emotional data, such as suggesting seat arrangements and in-car temperature adjustments. Furthermore, the emotion estimation function can be used to analyze the user's emotional data in real time and make suggestions to improve the quality of services, such as adjusting services according to changes in the user's emotions.

[0088] The urban transportation optimization system can further use an emotion estimation function to improve the commuting environment based on commuters' emotional data. For example, the emotion estimation function can be used to analyze commuters' stress levels and suggest commuting routes that reduce stress. For example, it can suggest less crowded times and routes. The emotion estimation function can also be used to provide advice to provide a comfortable commuting environment based on commuters' emotional data. For example, it can suggest ways to relax during the commute. Furthermore, the emotion estimation function can be used to analyze commuters' emotional data in real time and dynamically adjust the commuting environment. For example, it can re-suggest commuting routes in response to changes in traffic conditions.

[0089] The urban transportation optimization system can further use an emotion estimation function to suggest sightseeing routes based on tourists' emotion data. For example, the emotion estimation function can be used to analyze tourists' emotion data and suggest routes for enjoying sightseeing. For example, it can suggest scenic routes or routes that pass through popular tourist spots. The emotion estimation function can also be used to provide advice to provide a comfortable sightseeing experience based on tourists' emotion data. For example, it can suggest ways to relax while sightseeing. Furthermore, the emotion estimation function can be used to analyze tourists' emotion data in real time and dynamically adjust sightseeing routes. For example, it can re-suggest sightseeing routes depending on the congestion situation.

[0090] The urban transportation optimization system can further use an emotion estimation function to optimize access to an event venue based on the emotional data of event participants. For example, the emotion estimation function can be used to analyze the emotional data of event participants and suggest access routes that reduce stress. For example, it can suggest routes with less congestion or routes that use public transportation. The emotion estimation function can also be used to provide advice to provide a comfortable travel environment based on the emotional data of event participants. For example, it can suggest ways to relax while accessing the event venue. Furthermore, the emotion estimation function can be used to analyze the emotional data of event participants in real time and dynamically adjust access routes. For example, it can re-suggest access routes in response to changes in traffic conditions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The traffic data collection unit collects traffic data within the city. For example, it collects traffic volume data obtained from cameras and sensors on the road, vehicle location information, traffic light status, etc. It can also collect GPS data and smartphone location information. This allows it to monitor traffic volume and collect data in real time. Step 2: The traffic situation analysis unit analyzes the traffic data collected by the traffic data collection unit. For example, it uses generative AI to understand the current traffic situation and learns from past traffic data to predict future traffic congestion. It also analyzes weather data and event information to improve the accuracy of traffic situation predictions. Step 3: The route guidance section provides optimal route guidance based on the traffic conditions analyzed by the traffic analysis section. For example, when a commuter heads to their destination, the generation AI can suggest the most efficient route taking into account the current traffic conditions. It can also include fuel-efficient routes from an eco-driving perspective, as well as routes that take into account road safety and accident risk. Step 4: The operation planning unit creates a public transportation operation plan based on the data analyzed by the traffic situation analysis unit. For example, the generation AI analyzes bus and train schedules and generates an operation plan tailored to the time periods when there are many users. It can also study past usage data and perform demand forecasts. Step 5: The congestion information provision unit provides congestion information based on the data analyzed by the traffic situation analysis unit. For example, the generation AI analyzes the congestion level of trains and buses and provides congestion information to the user. It can also perform real-time congestion predictions and analyze congestion patterns based on past congestion data.

[0093] 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.

[0094] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0099] 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.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0107] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0114] 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.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, the 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.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0129] 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.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] 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.

[0133] The control object 443 includes a display device, LEDs in the eyes, and motors that drive 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.

[0134] 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.

[0135] 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.

[0136] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] 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.

[0143] FIG. 9 illustrates 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 behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions 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.

[0144] 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.

[0145] 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).

[0146] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] 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."

[0148] 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.

[0149] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, 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. A processor also includes 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.

[0154] The hardware resource that executes the specific process 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 process may be a single processor.

[0155] 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.

[0156] 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.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] 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.

[0159] 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. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a traffic data collection unit that collects traffic data within the city; a traffic situation analysis unit that analyzes the traffic data collected by the traffic data collection unit; a route guidance unit that provides guidance on an optimal route based on the traffic conditions analyzed by the traffic condition analysis unit; an operation planning unit that creates an operation plan for public transportation based on the data analyzed by the traffic situation analysis unit; a congestion information providing unit that provides congestion information based on the data analyzed by the traffic situation analyzing unit. A system characterized by:

2. The traffic situation analysis unit In addition to the traffic data, weather data and event information will be analyzed to improve the accuracy of traffic situation predictions.

2. The system of claim 1.

3. The traffic situation analysis unit Learn from past traffic patterns and predict future traffic congestion 2. The system of claim 1.

4. The traffic situation analysis unit Collects emotional data from drivers and commuters to suggest routes with less stress 2. The system of claim 1.

5. The traffic situation analysis unit The movement of bicycles and pedestrians within the city is also analyzed to grasp the overall traffic situation.

2. The system of claim 1.

6. The traffic situation analysis unit The analyzed traffic data will be linked to other smart city systems.

2. The system of claim 1.

Images