System

Abstract

A system according to an embodiment is directed to automatically adjusting signal timing based on traffic data.SOLUTION: A system includes a collection unit, an analysis unit, and an adjustment unit. The collection unit collects traffic data. The analysis unit analyzes the data collected by the collection unit. The adjustment unit automatically adjusts the signal timing based on the analysis result obtained by the 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 do not adequately optimize signal timing based on traffic data, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically adjust signal timing based on traffic data. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and an adjustment unit. The collection unit collects traffic data. The analysis unit analyzes the data collected by the collection unit. The adjustment unit automatically adjusts signal timing based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] Embodiments of the system can automatically adjust signal timing based on traffic data. [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) A traffic control system according to an embodiment of the present invention collects traffic data, analyzes it using AI, and automatically adjusts traffic signal timing. The traffic control system collects information such as real-time traffic data, pedestrian counts, public transportation schedules, weather information, and large-scale events, analyzes it using AI, and automatically adjusts optimal traffic signal timing. This achieves optimized traffic conditions without congestion. For example, the traffic control system collects real-time traffic data using traffic volume sensors and cameras. Next, the traffic control system collects pedestrian counts using a pedestrian counting system. Furthermore, the traffic control system obtains bus and train schedules in real time. The traffic control system also obtains weather data and controls traffic according to weather conditions. Furthermore, the traffic control system obtains information on events such as concerts and sporting events. The collected information is analyzed using AI and automatically adjusts optimal traffic signal timing. For example, the system extends the green light duration during periods of heavy traffic and extends the red light duration during periods of light traffic. This allows for smoother traffic flow and reduced congestion. This allows the traffic control system to achieve optimized traffic conditions without congestion. For example, optimizing traffic light timing at major intersections can reduce vehicle waiting times and smooth traffic flow. Reducing congestion also contributes to environmental measures by reducing fuel consumption and exhaust emissions. For example, reducing vehicle idling time can reduce fuel consumption and exhaust emissions.

[0029] A traffic control system according to an embodiment includes a collection unit, an analysis unit, and an adjustment unit. The collection unit collects traffic data. The traffic data includes, for example, vehicle traffic volume, speed, and traffic accident information, but is not limited to these examples. The collection unit collects real-time traffic data using, for example, a traffic volume sensor or a camera. The collection unit can also collect the number of pedestrians using a pedestrian counting system. The collection unit can also acquire bus and train schedules in real time. The collection unit can also acquire weather data and perform traffic control according to weather conditions. The collection unit can also acquire information on events such as concerts and sporting events. For example, the collection unit measures vehicle traffic volume in real time using a traffic volume sensor. The collection unit can also measure vehicle speed using a camera. The collection unit can also measure the number of people passing through a specific area using a pedestrian counting system. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, a data analysis algorithm or a statistical method, but is not limited to these examples. For example, the analysis unit analyzes traffic data using a data analysis algorithm. The analysis unit can also analyze traffic data using a statistical method. The analysis unit can also analyze traffic data using a machine learning algorithm. The adjustment unit automatically adjusts traffic signal timing based on the analysis results obtained by the analysis unit. The traffic signal timing may, for example, adjust the green light time, red light time, cycle length, etc., but is not limited to these examples. For example, the adjustment unit extends the green light time to smooth traffic flow. The adjustment unit can also ensure pedestrian safety by extending the red light time. The adjustment unit can also optimize traffic flow by adjusting the cycle length of traffic signals. Thus, the traffic control system according to the embodiment can automatically collect and analyze traffic data and adjust traffic signal timing to achieve optimized traffic conditions without congestion.

[0030] The collection unit can collect real-time traffic data using a traffic volume sensor or a camera. Examples of traffic volume sensors include, but are not limited to, loop coil sensors and infrared sensors. Examples of cameras include, but are not limited to, CCTV cameras and IP cameras. The collection unit measures vehicle traffic volume in real time using, for example, a traffic volume sensor. The collection unit can also measure vehicle speeds using a camera. The collection unit can also collect more detailed traffic data by combining a traffic volume sensor and a camera. This allows for accurate understanding of current traffic conditions by collecting real-time traffic data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired by a traffic volume sensor or a camera into a generation AI and have the generation AI analyze the traffic data.

[0031] The collection unit can collect the number of pedestrians using a pedestrian counting system. Examples of pedestrian counting systems include, but are not limited to, infrared counters and video analysis systems. For example, the collection unit can count the number of people passing through a specific area using an infrared counter. The collection unit can also measure the number of pedestrians using a video analysis system. The collection unit can also use a pedestrian counting system to understand fluctuations in the number of pedestrians during a specific time period. By collecting the number of pedestrians, the flow of pedestrians can be understood and used to adjust traffic light timing. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input video data acquired by a video analysis system into a generation AI and have the generation AI analyze the number of pedestrians.

[0032] The collection unit can acquire bus and train operation schedules in real time. Acquisition of operation schedules includes, but is not limited to, real-time data acquisition via an API and periodically updated information. The collection unit, for example, acquires bus and train operation schedules in real time via an API. The collection unit can also obtain periodically updated information to grasp operation schedules. The collection unit can also grasp the operation status of public transportation in real time and reflect this in adjusting signal timing. This makes it possible to adjust signal timing according to the operation status of public transportation. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input operation schedule data acquired via the API into a generation AI and have the generation AI analyze the operation status.

[0033] The collection unit can acquire weather data and perform traffic control according to weather conditions. Examples of acquired weather data include, but are not limited to, data from the Japan Meteorological Agency and private weather services. The collection unit can, for example, acquire data from the Japan Meteorological Agency and perform traffic control according to weather conditions. The collection unit can also acquire data from private weather services and perform traffic control according to weather conditions. The collection unit can also acquire weather data in real time and adjust traffic signal timing to reduce traffic congestion during bad weather. This can reduce traffic congestion during bad weather. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input weather data to the generation AI and cause the generation AI to analyze traffic control according to weather conditions.

[0034] The collection unit can acquire information about concerts or sporting events. Examples of acquired event information include, but are not limited to, information provided by event organizers and online calendar data. For example, the collection unit acquires event information by receiving information provided by event organizers. The collection unit can also obtain event information by acquiring online calendar data. The collection unit can also acquire event information in real time and set traffic light timings that correspond to increases in traffic volume after the event ends. This makes it possible to set traffic light timings that correspond to increases in traffic volume after the event ends. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input online calendar data into the generation AI and have the generation AI analyze the event information.

[0035] The analysis unit can analyze the collected data and calculate signal timing. Examples of analysis include, but are not limited to, data mining techniques and machine learning algorithms. The analysis unit can analyze traffic data using, for example, data mining techniques. The analysis unit can also analyze traffic data using machine learning algorithms. The analysis unit can also analyze traffic data using statistical techniques. This allows for calculating optimal signal timing, thereby smoothing traffic flow. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected traffic data to a generation AI and have the generation AI calculate signal timing.

[0036] The adjustment unit can automatically adjust the signal timing based on the analysis results. Examples of automatic adjustment include, but are not limited to, a real-time control algorithm, a feedback control system, etc. The adjustment unit can adjust the signal timing using, for example, a real-time control algorithm. The adjustment unit can also adjust the signal timing using a feedback control system. The adjustment unit can also automatically adjust the signal timing using AI. This automatically adjusting the signal timing can smooth traffic flow and reduce congestion. Some or all of the above-mentioned processing in the adjustment unit can be performed using, for example, AI, or can be performed without using AI. For example, the adjustment unit can input the analysis results to a generation AI and have the generation AI adjust the signal timing.

[0037] The adjustment unit can extend the green time of a traffic light during times of heavy traffic and extend the red time of a traffic light during times of light traffic. Examples of times of heavy traffic include, but are not limited to, rush hour, specific events, and the like. Adjustment of the green time of a traffic light includes, but is not limited to, dynamic adjustment according to traffic volume and extension of a fixed time. For example, the adjustment unit extends the green time of a traffic light during rush hour. The adjustment unit can also extend the green time of a traffic light during specific events. The adjustment unit can also extend the red time of a traffic light during times of light traffic. This enables adjustment of traffic light timing according to traffic volume. Some or all of the above-described processing in the adjustment unit may be performed using, or without, AI. For example, the adjustment unit can input traffic volume data to the generation AI and cause the generation AI to adjust the traffic light timing.

[0038] The adjustment unit can adjust the signal timing at major intersections. Examples of major intersections include, but are not limited to, intersections with heavy traffic volume and intersections where accidents frequently occur. The adjustment unit can, for example, adjust the signal timing at intersections with heavy traffic volume. The adjustment unit can also adjust the signal timing at intersections where accidents frequently occur. The adjustment unit can also optimize the signal timing at major intersections. By optimizing the signal timing at major intersections, vehicle waiting times can be reduced and traffic flow can be made smoother. Some or all of the above-described processing in the adjustment unit can be performed using, for example, AI, or can be performed without using AI. For example, the adjustment unit can input traffic data for major intersections to the generation AI and cause the generation AI to adjust the signal timing.

[0039] The adjustment unit can reduce the idling time of the vehicle to reduce fuel consumption and exhaust gas emissions. Idling time includes, but is not limited to, for example, a time when the engine is not stopped or a specific waiting time. The adjustment unit can reduce the idling time of the vehicle by, for example, extending the green time of a traffic light. The adjustment unit can also reduce the idling time of the vehicle by shortening the red time of a traffic light. The adjustment unit can also reduce the idling time of the vehicle by adjusting the length of the traffic light cycle. This contributes to reducing fuel consumption and exhaust gas emissions and contributes to environmental measures. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input vehicle idling time data to the generation AI and cause the generation AI to adjust the traffic light timing.

[0040] When collecting traffic data, the collection unit can select a data collection method according to a specific event or season. Specific events or seasons include, but are not limited to, New Year's holidays, summer vacation, and specific festivals and events. For example, when a large-scale event is held, the collection unit can focus on collecting traffic data around the event venue. Furthermore, in winter, the collection unit can collect road condition data taking into account the effects of snow and ice. Furthermore, in summer, the collection unit can collect pedestrian count data taking into account the risk of heatstroke. This enables more accurate data collection by selecting a data collection method according to a specific event or season. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data on a specific event or season into the generation AI and have the generation AI select a data collection method.

[0041] The collection unit can collect traffic data by combining different data sources. Examples of different data sources include, but are not limited to, drones, satellite images, and ground sensors. For example, the collection unit collects traffic conditions from the air in real time using a drone. The collection unit can also use satellite images to grasp wide-area traffic conditions and collect data. The collection unit can also collect detailed traffic data by combining footage from a ground camera and a drone. This allows for more detailed traffic data to be collected by combining and collecting different data sources. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data from drones or satellite images into the generation AI and have the generation AI collect traffic data.

[0042] When collecting traffic data, the collection unit can improve the accuracy of the collection by referring to past traffic data. Past traffic data includes, for example, data obtained from a database, past statistical data, etc., but is not limited to these examples. For example, the collection unit predicts traffic patterns for a specific time period or day of the week based on the past traffic data and collects data. The collection unit can also detect abnormal traffic conditions by referring to the past traffic data and improve the accuracy of the collection. The collection unit can also analyze the past traffic data and determine the priority of the data to be collected. In this way, the accuracy of the collection can be improved by referring to the past traffic data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past traffic data to the generation AI and cause the generation AI to improve the accuracy of data collection.

[0043] When collecting traffic data, the collection unit can adjust the collection range taking into account geographical characteristics. Geographical characteristics include, but are not limited to, urban areas, suburban areas, and mountainous areas. For example, in mountainous areas, the collection unit can collect data taking into account the gradient of the road and the number of curves. In urban areas, the collection unit can also focus on collecting traffic data from intersections and major roads. In coastal areas, the collection unit can also collect data taking into account the effects of tides and wind. This allows for more accurate data collection by adjusting the collection range taking into account geographical characteristics. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input geographical characteristic data into the generation AI and have the generation AI adjust the collection range.

[0044] When collecting traffic data, the collection unit can analyze social media posts to understand real-time traffic conditions. Examples of social media post analysis include, but are not limited to, text mining and sentiment analysis. The collection unit can, for example, collect traffic congestion and accident information from social media posts. The collection unit can also analyze social media posts to understand the impact of specific events or rallies. The collection unit can also predict real-time traffic conditions based on social media posts. Thus, real-time traffic conditions can be understood by analyzing social media posts. Some or all of the above-described processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input social media post data into a generation AI and have the generation AI analyze traffic conditions.

[0045] When collecting traffic data, the collection unit can customize the collection method by reflecting the user's past feedback. The user's past feedback includes, but is not limited to, survey results and past opinions. The collection unit, for example, adjusts the type of data to be collected based on the user's past feedback. The collection unit can also adjust the frequency of data collection by reflecting the user's past feedback. The collection unit can also determine the priority of data to be collected by referring to the user's past feedback. This allows the collection method to be customized by reflecting the user's past feedback, enabling more appropriate data collection. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI customize the collection method.

[0046] During analysis, the analysis unit can combine different algorithms to improve the accuracy of the analysis. Examples of different algorithms include, but are not limited to, machine learning algorithms and statistical analysis algorithms. For example, the analysis unit can combine machine learning algorithms and statistical analysis to analyze traffic data. The analysis unit can also combine different AI models to detect traffic patterns. The analysis unit can also combine deep learning and conventional algorithms to improve the accuracy of the analysis. Thus, combining different algorithms can improve the accuracy of the analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input different algorithms into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0047] During analysis, the analysis unit can optimize the current analysis by referring to past analysis results. Past analysis results include, but are not limited to, results obtained from a database or past reports. The analysis unit, for example, predicts current traffic conditions based on past analysis results. The analysis unit can also detect abnormal traffic patterns by referring to past analysis results. The analysis unit can also adjust the analysis algorithm based on past analysis results. In this way, the current analysis can be optimized by referring to past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into a generation AI and have the generation AI optimize the current analysis.

[0048] During analysis, the analysis unit can apply a custom algorithm for detecting a specific traffic pattern. Examples of custom algorithms include, but are not limited to, algorithms specialized for a specific traffic pattern. For example, the analysis unit can apply a custom algorithm for detecting traffic congestion. The analysis unit can also apply a custom algorithm for predicting the risk of traffic accidents. The analysis unit can also apply a custom algorithm for predicting delays in public transportation. By applying a custom algorithm for detecting a specific traffic pattern, more accurate analysis is possible. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the custom algorithm into the generation AI and cause the generation AI to detect a specific traffic pattern.

[0049] The analysis unit can integrate different data sources during analysis. Examples of different data sources include, but are not limited to, weather data, event information, and traffic sensors. For example, the analysis unit can integrate weather data and traffic data to analyze traffic conditions during bad weather. The analysis unit can also integrate event information and traffic data to analyze traffic conditions during large-scale events. The analysis unit can also integrate public transportation schedules and traffic data to analyze the impact of service delays. Integrating different data sources enables more accurate analysis. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input different data sources into the generation AI and have the generation AI integrate and analyze the data.

[0050] During analysis, the analysis unit can improve safety by referring to traffic accident data. Traffic accident data includes, but is not limited to, police databases and insurance company data. For example, the analysis unit can identify locations where accidents frequently occur based on the traffic accident data. The analysis unit can also identify time periods with a high risk of accidents by referring to the traffic accident data. The analysis unit can also analyze the traffic accident data and propose measures to improve safety. In this way, safety can be improved by referring to the traffic accident data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input traffic accident data to a generation AI and have the generation AI perform an analysis to improve safety.

[0051] The analysis unit can perform analysis taking into account fluctuations in traffic data by time period. Examples of fluctuations by time period include, but are not limited to, peak hours and off-peak hours. For example, the analysis unit can analyze traffic data during morning rush hours and propose optimal signal timing. The analysis unit can also analyze data during low-traffic hours in the daytime and adjust signal timing. The analysis unit can also analyze traffic data at night and propose signal timing that takes safety into consideration. This enables more accurate analysis by taking into account fluctuations in traffic data by time period. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input traffic data by time period into a generation AI and have the generation AI analyze signal timing.

[0052] During adjustment, the adjustment unit can set custom signal timing according to a specific traffic pattern. Examples of custom signal timing include, but are not limited to, settings according to a specific traffic pattern. For example, the adjustment unit can improve traffic flow by extending the green time of a signal when traffic congestion occurs. The adjustment unit can also ensure pedestrian safety by extending the red time of a signal when there are many pedestrians. The adjustment unit can also adjust the green time of a signal when public transportation delays occur to ensure smooth operation of buses and trains. Thus, setting custom signal timing according to a specific traffic pattern can smooth traffic flow. Some or all of the above-described processing in the adjustment unit may be performed using, or without, AI. For example, the adjustment unit can input specific traffic pattern data to the generation AI and cause the generation AI to set the custom signal timing.

[0053] During adjustment, the adjustment unit can optimize the current adjustment by referring to past adjustment results. Past adjustment results include, but are not limited to, acquisition from a database or past reports. For example, the adjustment unit sets optimal signal timing for current traffic conditions based on past signal timing adjustment results. The adjustment unit can also detect abnormal traffic patterns and make adjustments by referring to past adjustment results. The adjustment unit can also optimize the signal timing adjustment algorithm based on past adjustment results. In this way, the current adjustment can be optimized by referring to past adjustment results. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input past adjustment result data to a generation AI and cause the generation AI to optimize the current adjustment.

[0054] During adjustment, the adjustment unit can simulate different traffic scenarios to determine optimal signal timing. Traffic scenarios include, but are not limited to, simulation software and scenario setting methods. For example, the adjustment unit can simulate a traffic congestion scenario and set optimal signal timing. The adjustment unit can also simulate a traffic volume increase scenario after a large-scale event and adjust the signal timing. The adjustment unit can also simulate a traffic scenario during bad weather and set signal timing that takes safety into consideration. By simulating different traffic scenarios, optimal signal timing can be determined. Some or all of the above-described processing in the adjustment unit can be performed using, for example, AI, or without AI. For example, the adjustment unit can input different traffic scenario data to the generation AI and have the generation AI perform the simulation and determine the signal timing.

[0055] During adjustment, the adjustment unit can change the signal timing in real time in response to fluctuations in traffic volume. Examples of methods for changing the signal timing in real time include, but are not limited to, real-time data acquisition methods and control algorithms. For example, when traffic volume suddenly increases, the adjustment unit extends the green time of the signal to smooth traffic flow. Furthermore, when traffic volume decreases, the adjustment unit can extend the red time of the signal to ensure pedestrian safety. Furthermore, the adjustment unit can adjust the signal timing in real time in response to fluctuations in traffic volume. This allows smooth traffic flow by changing the signal timing in real time in response to fluctuations in traffic volume. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input traffic volume data to the generation AI and cause the generation AI to perform real-time adjustment of the signal timing.

[0056] During adjustment, the adjustment unit can adjust the signal timing taking into account the operation status of the public transportation facility. Examples of the operation status of the public transportation facility include, but are not limited to, operation schedules and real-time operation information. For example, when a bus is delayed, the adjustment unit extends the green time of the signal to ensure smooth bus operation. The adjustment unit can also adjust the signal timing in accordance with the train operation schedule. The adjustment unit can also grasp the operation status of the public transportation facility in real time and adjust the signal timing. This allows the signal timing to be optimized by taking into account the operation status of the public transportation facility, thereby ensuring smooth operation of the public transportation facility. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input operation status data of the public transportation facility to the generation AI and cause the generation AI to adjust the signal timing.

[0057] During adjustment, the adjustment unit can set signal timing to reduce the risk of traffic accidents. Examples of the risk of traffic accidents include, but are not limited to, accident occurrence rates and risk assessments. For example, the adjustment unit can ensure safety by extending the red time of a signal at an intersection where traffic accidents frequently occur. The adjustment unit can also adjust signal timing to prevent accidents during times when the risk of traffic accidents is high. The adjustment unit can also improve safety by setting signal timing based on traffic accident data. Thus, safety can be improved by setting signal timing to reduce the risk of traffic accidents. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input traffic accident data into the generation AI and cause the generation AI to set the signal timing.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] When collecting traffic data, the collection unit can analyze drivers' driving styles and reflect the results in adjusting traffic signal timing. For example, if there is a lot of sudden braking or sudden acceleration, the collection unit can extend the green time of the traffic signal to promote smooth traffic flow. In addition, if the driving style is gentle, the collection unit can extend the red time of the traffic signal to ensure pedestrian safety. Furthermore, the collection unit can accumulate driving style data and use it to improve traffic control in the future. This makes it possible to adjust traffic signal timing according to the driver's driving style, thereby optimizing traffic flow.

[0060] When collecting traffic data, the collection unit can acquire vehicle fuel consumption data and reflect it in adjusting traffic signal timing. For example, if fuel consumption is high, the collection unit can extend the green time of the traffic signal to reduce idling time. In addition, if fuel consumption is low, the collection unit can extend the red time of the traffic signal to ensure pedestrian safety. Furthermore, the collection unit can accumulate fuel consumption data and use it to improve traffic control for environmental protection. This makes it possible to adjust traffic signal timing according to vehicle fuel consumption, thereby reducing environmental impact.

[0061] When collecting traffic data, the collection unit can recognize vehicle license plates and adjust traffic light timing for specific vehicles. For example, when an emergency vehicle is approaching, the green light time can be extended to allow quicker passage. The collection unit can also adjust traffic light timing to give priority to public transportation vehicles. Furthermore, the collection unit can accumulate traffic data for specific vehicles and use it to improve traffic control in the future. This makes it possible to adjust traffic light timing according to specific vehicles, thereby optimizing traffic flow.

[0062] The collection unit can acquire vehicle emission data when collecting traffic data and reflect it in adjusting traffic light timing. For example, if emissions are high, the green time of the traffic light can be extended to reduce idling time. The collection unit can also extend the red time of the traffic light when emissions are low to ensure pedestrian safety. Furthermore, the collection unit can accumulate emission data and use it to improve traffic control for environmental measures. This makes it possible to adjust traffic light timing according to vehicle emissions, thereby reducing environmental impact.

[0063] When collecting traffic data, the collection unit can predict vehicle operation routes and reflect the results in adjusting traffic signal timing. For example, if an increase in traffic volume is predicted on a specific route, the green time of the traffic signal can be extended to ensure smooth traffic flow. The collection unit can also extend the red time of the traffic signal based on the predicted operation route to ensure pedestrian safety. Furthermore, the collection unit can accumulate predicted data on operation routes and use it to improve traffic control in the future. This makes it possible to adjust traffic signal timing according to the vehicle operation route, thereby optimizing traffic flow.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects traffic data. Traffic data includes vehicle traffic volume, speed, traffic accident information, pedestrian counts, bus and train schedules, weather data, and information on concerts and sporting events. The collection unit collects real-time traffic data using traffic volume sensors, cameras, and pedestrian counting systems. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is carried out using data analysis algorithms, statistical methods, and machine learning algorithms. This allows for the identification of patterns and trends in traffic data, making it possible to predict traffic conditions and identify problems. Step 3: The adjustment unit automatically adjusts the signal timing based on the analysis results obtained by the analysis unit. The adjustment of signal timing includes the green and red light times, the length of the signal cycle, etc. For example, the green light time can be extended to smooth traffic flow, or the red light time can be extended to ensure pedestrian safety.

[0066] (Example 2) A traffic control system according to an embodiment of the present invention collects traffic data, analyzes it using AI, and automatically adjusts traffic signal timing. The traffic control system collects information such as real-time traffic data, pedestrian counts, public transportation schedules, weather information, and large-scale events, analyzes it using AI, and automatically adjusts optimal traffic signal timing. This achieves optimized traffic conditions without congestion. For example, the traffic control system collects real-time traffic data using traffic volume sensors and cameras. Next, the traffic control system collects pedestrian counts using a pedestrian counting system. Furthermore, the traffic control system obtains bus and train schedules in real time. The traffic control system also obtains weather data and controls traffic according to weather conditions. Furthermore, the traffic control system obtains information on events such as concerts and sporting events. The collected information is analyzed using AI and automatically adjusts optimal traffic signal timing. For example, the system extends the green light duration during periods of heavy traffic and extends the red light duration during periods of light traffic. This allows for smoother traffic flow and reduced congestion. This allows the traffic control system to achieve optimized traffic conditions without congestion. For example, optimizing traffic light timing at major intersections can reduce vehicle waiting times and smooth traffic flow. Reducing congestion also contributes to environmental measures by reducing fuel consumption and exhaust emissions. For example, reducing vehicle idling time can reduce fuel consumption and exhaust emissions.

[0067] A traffic control system according to an embodiment includes a collection unit, an analysis unit, and an adjustment unit. The collection unit collects traffic data. The traffic data includes, for example, vehicle traffic volume, speed, and traffic accident information, but is not limited to these examples. The collection unit collects real-time traffic data using, for example, a traffic volume sensor or a camera. The collection unit can also collect the number of pedestrians using a pedestrian counting system. The collection unit can also acquire bus and train schedules in real time. The collection unit can also acquire weather data and perform traffic control according to weather conditions. The collection unit can also acquire information on events such as concerts and sporting events. For example, the collection unit measures vehicle traffic volume in real time using a traffic volume sensor. The collection unit can also measure vehicle speed using a camera. The collection unit can also measure the number of people passing through a specific area using a pedestrian counting system. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, a data analysis algorithm or a statistical method, but is not limited to these examples. For example, the analysis unit analyzes traffic data using a data analysis algorithm. The analysis unit can also analyze traffic data using a statistical method. The analysis unit can also analyze traffic data using a machine learning algorithm. The adjustment unit automatically adjusts traffic signal timing based on the analysis results obtained by the analysis unit. The traffic signal timing may, for example, adjust the green light time, red light time, cycle length, etc., but is not limited to these examples. For example, the adjustment unit extends the green light time to smooth traffic flow. The adjustment unit can also ensure pedestrian safety by extending the red light time. The adjustment unit can also optimize traffic flow by adjusting the cycle length of traffic signals. Thus, the traffic control system according to the embodiment can automatically collect and analyze traffic data and adjust traffic signal timing to achieve optimized traffic conditions without congestion.

[0068] The collection unit can collect real-time traffic data using a traffic volume sensor or a camera. Examples of traffic volume sensors include, but are not limited to, loop coil sensors and infrared sensors. Examples of cameras include, but are not limited to, CCTV cameras and IP cameras. The collection unit measures vehicle traffic volume in real time using, for example, a traffic volume sensor. The collection unit can also measure vehicle speeds using a camera. The collection unit can also collect more detailed traffic data by combining a traffic volume sensor and a camera. This allows for accurate understanding of current traffic conditions by collecting real-time traffic data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired by a traffic volume sensor or a camera into a generation AI and have the generation AI analyze the traffic data.

[0069] The collection unit can collect the number of pedestrians using a pedestrian counting system. Examples of pedestrian counting systems include, but are not limited to, infrared counters and video analysis systems. For example, the collection unit can count the number of people passing through a specific area using an infrared counter. The collection unit can also measure the number of pedestrians using a video analysis system. The collection unit can also use a pedestrian counting system to understand fluctuations in the number of pedestrians during a specific time period. By collecting the number of pedestrians, the flow of pedestrians can be understood and used to adjust traffic light timing. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input video data acquired by a video analysis system into a generation AI and have the generation AI analyze the number of pedestrians.

[0070] The collection unit can acquire bus and train operation schedules in real time. Acquisition of operation schedules includes, but is not limited to, real-time data acquisition via an API and periodically updated information. The collection unit, for example, acquires bus and train operation schedules in real time via an API. The collection unit can also obtain periodically updated information to grasp operation schedules. The collection unit can also grasp the operation status of public transportation in real time and reflect this in adjusting signal timing. This makes it possible to adjust signal timing according to the operation status of public transportation. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input operation schedule data acquired via the API into a generation AI and have the generation AI analyze the operation status.

[0071] The collection unit can acquire weather data and perform traffic control according to weather conditions. Examples of acquired weather data include, but are not limited to, data from the Japan Meteorological Agency and private weather services. The collection unit can, for example, acquire data from the Japan Meteorological Agency and perform traffic control according to weather conditions. The collection unit can also acquire data from private weather services and perform traffic control according to weather conditions. The collection unit can also acquire weather data in real time and adjust traffic signal timing to reduce traffic congestion during bad weather. This can reduce traffic congestion during bad weather. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input weather data to the generation AI and cause the generation AI to analyze traffic control according to weather conditions.

[0072] The collection unit can acquire information about concerts or sporting events. Examples of acquired event information include, but are not limited to, information provided by event organizers and online calendar data. For example, the collection unit acquires event information by receiving information provided by event organizers. The collection unit can also obtain event information by acquiring online calendar data. The collection unit can also acquire event information in real time and set traffic light timings that correspond to increases in traffic volume after the event ends. This makes it possible to set traffic light timings that correspond to increases in traffic volume after the event ends. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input online calendar data into the generation AI and have the generation AI analyze the event information.

[0073] The analysis unit can analyze the collected data and calculate signal timing. Examples of analysis include, but are not limited to, data mining techniques and machine learning algorithms. The analysis unit can analyze traffic data using, for example, data mining techniques. The analysis unit can also analyze traffic data using machine learning algorithms. The analysis unit can also analyze traffic data using statistical techniques. This allows for calculating optimal signal timing, thereby smoothing traffic flow. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected traffic data to a generation AI and have the generation AI calculate signal timing.

[0074] The adjustment unit can automatically adjust the signal timing based on the analysis results. Examples of automatic adjustment include, but are not limited to, a real-time control algorithm, a feedback control system, etc. The adjustment unit can adjust the signal timing using, for example, a real-time control algorithm. The adjustment unit can also adjust the signal timing using a feedback control system. The adjustment unit can also automatically adjust the signal timing using AI. This automatically adjusting the signal timing can smooth traffic flow and reduce congestion. Some or all of the above-mentioned processing in the adjustment unit can be performed using, for example, AI, or can be performed without using AI. For example, the adjustment unit can input the analysis results to a generation AI and have the generation AI adjust the signal timing.

[0075] The adjustment unit can extend the green time of a traffic light during times of heavy traffic and extend the red time of a traffic light during times of light traffic. Examples of times of heavy traffic include, but are not limited to, rush hour, specific events, and the like. Adjustment of the green time of a traffic light includes, but is not limited to, dynamic adjustment according to traffic volume and extension of a fixed time. For example, the adjustment unit extends the green time of a traffic light during rush hour. The adjustment unit can also extend the green time of a traffic light during specific events. The adjustment unit can also extend the red time of a traffic light during times of light traffic. This enables adjustment of traffic light timing according to traffic volume. Some or all of the above-described processing in the adjustment unit may be performed using, or without, AI. For example, the adjustment unit can input traffic volume data to the generation AI and cause the generation AI to adjust the traffic light timing.

[0076] The adjustment unit can adjust the signal timing at major intersections. Examples of major intersections include, but are not limited to, intersections with heavy traffic volume and intersections where accidents frequently occur. The adjustment unit can, for example, adjust the signal timing at intersections with heavy traffic volume. The adjustment unit can also adjust the signal timing at intersections where accidents frequently occur. The adjustment unit can also optimize the signal timing at major intersections. By optimizing the signal timing at major intersections, vehicle waiting times can be reduced and traffic flow can be made smoother. Some or all of the above-described processing in the adjustment unit can be performed using, for example, AI, or can be performed without using AI. For example, the adjustment unit can input traffic data for major intersections to the generation AI and cause the generation AI to adjust the signal timing.

[0077] The adjustment unit can reduce the idling time of the vehicle to reduce fuel consumption and exhaust gas emissions. Idling time includes, but is not limited to, for example, a time when the engine is not stopped or a specific waiting time. The adjustment unit can reduce the idling time of the vehicle by, for example, extending the green time of a traffic light. The adjustment unit can also reduce the idling time of the vehicle by shortening the red time of a traffic light. The adjustment unit can also reduce the idling time of the vehicle by adjusting the length of the traffic light cycle. This contributes to reducing fuel consumption and exhaust gas emissions and contributes to environmental measures. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input vehicle idling time data to the generation AI and cause the generation AI to adjust the traffic light timing.

[0078] The collection unit can estimate the user's emotions and adjust the timing of traffic data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can increase the frequency of traffic data collection to obtain a more detailed understanding of real-time traffic conditions. Furthermore, if the user is relaxed, the collection unit can reduce the frequency of traffic data collection to reduce the load on the system. Furthermore, if the user is in a hurry, the collection unit can quickly collect traffic data and immediately adjust traffic signal timing. This enables more appropriate data collection by adjusting the timing of traffic data collection according to the user's emotions. The estimation of the user's emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of traffic data collection.

[0079] When collecting traffic data, the collection unit can select a data collection method according to a specific event or season. Specific events or seasons include, but are not limited to, New Year's holidays, summer vacation, and specific festivals and events. For example, when a large-scale event is held, the collection unit can focus on collecting traffic data around the event venue. Furthermore, in winter, the collection unit can collect road condition data taking into account the effects of snow and ice. Furthermore, in summer, the collection unit can collect pedestrian count data taking into account the risk of heatstroke. This enables more accurate data collection by selecting a data collection method according to a specific event or season. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data on a specific event or season into the generation AI and have the generation AI select a data collection method.

[0080] The collection unit can collect traffic data by combining different data sources. Examples of different data sources include, but are not limited to, drones, satellite images, and ground sensors. For example, the collection unit collects traffic conditions from the air in real time using a drone. The collection unit can also use satellite images to grasp wide-area traffic conditions and collect data. The collection unit can also collect detailed traffic data by combining footage from a ground camera and a drone. This allows for more detailed traffic data to be collected by combining and collecting different data sources. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data from drones or satellite images into the generation AI and have the generation AI collect traffic data.

[0081] When collecting traffic data, the collection unit can improve the accuracy of the collection by referring to past traffic data. Past traffic data includes, for example, data obtained from a database, past statistical data, etc., but is not limited to these examples. For example, the collection unit predicts traffic patterns for a specific time period or day of the week based on the past traffic data and collects data. The collection unit can also detect abnormal traffic conditions by referring to the past traffic data and improve the accuracy of the collection. The collection unit can also analyze the past traffic data and determine the priority of the data to be collected. In this way, the accuracy of the collection can be improved by referring to the past traffic data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past traffic data to the generation AI and cause the generation AI to improve the accuracy of data collection.

[0082] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting data related to traffic congestion. Furthermore, if the user is relaxed, the collection unit can prioritize collecting pedestrian count data. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting public transportation operation status data. This enables more appropriate data collection by determining the priority of data to be collected according to the user's emotions. The estimation of the user's emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of data to be collected.

[0083] When collecting traffic data, the collection unit can adjust the collection range taking into account geographical characteristics. Geographical characteristics include, but are not limited to, urban areas, suburban areas, and mountainous areas. For example, in mountainous areas, the collection unit can collect data taking into account the gradient of the road and the number of curves. In urban areas, the collection unit can also focus on collecting traffic data from intersections and major roads. In coastal areas, the collection unit can also collect data taking into account the effects of tides and wind. This allows for more accurate data collection by adjusting the collection range taking into account geographical characteristics. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input geographical characteristic data into the generation AI and have the generation AI adjust the collection range.

[0084] When collecting traffic data, the collection unit can analyze social media posts to understand real-time traffic conditions. Examples of social media post analysis include, but are not limited to, text mining and sentiment analysis. The collection unit can, for example, collect traffic congestion and accident information from social media posts. The collection unit can also analyze social media posts to understand the impact of specific events or rallies. The collection unit can also predict real-time traffic conditions based on social media posts. Thus, real-time traffic conditions can be understood by analyzing social media posts. Some or all of the above-described processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input social media post data into a generation AI and have the generation AI analyze traffic conditions.

[0085] When collecting traffic data, the collection unit can customize the collection method by reflecting the user's past feedback. The user's past feedback includes, but is not limited to, survey results and past opinions. The collection unit, for example, adjusts the type of data to be collected based on the user's past feedback. The collection unit can also adjust the frequency of data collection by reflecting the user's past feedback. The collection unit can also determine the priority of data to be collected by referring to the user's past feedback. This allows the collection method to be customized by reflecting the user's past feedback, enabling more appropriate data collection. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI customize the collection method.

[0086] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for more appropriate information provision by adjusting the display method of the analysis results according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0087] During analysis, the analysis unit can combine different algorithms to improve the accuracy of the analysis. Examples of different algorithms include, but are not limited to, machine learning algorithms and statistical analysis algorithms. For example, the analysis unit can combine machine learning algorithms and statistical analysis to analyze traffic data. The analysis unit can also combine different AI models to detect traffic patterns. The analysis unit can also combine deep learning and conventional algorithms to improve the accuracy of the analysis. Thus, combining different algorithms can improve the accuracy of the analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input different algorithms into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0088] During analysis, the analysis unit can optimize the current analysis by referring to past analysis results. Past analysis results include, but are not limited to, results obtained from a database or past reports. The analysis unit, for example, predicts current traffic conditions based on past analysis results. The analysis unit can also detect abnormal traffic patterns by referring to past analysis results. The analysis unit can also adjust the analysis algorithm based on past analysis results. In this way, the current analysis can be optimized by referring to past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into a generation AI and have the generation AI optimize the current analysis.

[0089] During analysis, the analysis unit can apply a custom algorithm for detecting a specific traffic pattern. Examples of custom algorithms include, but are not limited to, algorithms specialized for a specific traffic pattern. For example, the analysis unit can apply a custom algorithm for detecting traffic congestion. The analysis unit can also apply a custom algorithm for predicting the risk of traffic accidents. The analysis unit can also apply a custom algorithm for predicting delays in public transportation. By applying a custom algorithm for detecting a specific traffic pattern, more accurate analysis is possible. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the custom algorithm into the generation AI and cause the generation AI to detect a specific traffic pattern.

[0090] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize displaying analysis results related to traffic congestion. Furthermore, if the user is relaxed, the analysis unit can prioritize displaying analysis results related to the number of pedestrians. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying analysis results related to the operation status of public transportation. This enables more appropriate information to be provided by prioritizing the analysis results according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the analysis results.

[0091] The analysis unit can integrate different data sources during analysis. Examples of different data sources include, but are not limited to, weather data, event information, and traffic sensors. For example, the analysis unit can integrate weather data and traffic data to analyze traffic conditions during bad weather. The analysis unit can also integrate event information and traffic data to analyze traffic conditions during large-scale events. The analysis unit can also integrate public transportation schedules and traffic data to analyze the impact of service delays. Integrating different data sources enables more accurate analysis. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input different data sources into the generation AI and have the generation AI integrate and analyze the data.

[0092] During analysis, the analysis unit can improve safety by referring to traffic accident data. Traffic accident data includes, but is not limited to, police databases and insurance company data. For example, the analysis unit can identify locations where accidents frequently occur based on the traffic accident data. The analysis unit can also identify time periods with a high risk of accidents by referring to the traffic accident data. The analysis unit can also analyze the traffic accident data and propose measures to improve safety. In this way, safety can be improved by referring to the traffic accident data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input traffic accident data to a generation AI and have the generation AI perform an analysis to improve safety.

[0093] The analysis unit can perform analysis taking into account fluctuations in traffic data by time period. Examples of fluctuations by time period include, but are not limited to, peak hours and off-peak hours. For example, the analysis unit can analyze traffic data during morning rush hours and propose optimal signal timing. The analysis unit can also analyze data during low-traffic hours in the daytime and adjust signal timing. The analysis unit can also analyze traffic data at night and propose signal timing that takes safety into consideration. This enables more accurate analysis by taking into account fluctuations in traffic data by time period. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input traffic data by time period into a generation AI and have the generation AI analyze signal timing.

[0094] The adjustment unit can estimate the user's emotions and change the traffic light timing adjustment method based on the estimated user's emotions. For example, if the user is feeling stressed, the adjustment unit can extend the green time of the traffic light to smooth traffic flow. Furthermore, if the user is relaxed, the adjustment unit can also extend the red time of the traffic light to ensure pedestrian safety. Furthermore, if the user is in a hurry, the adjustment unit can shorten the green time of the traffic light to enable quick movement. This enables more appropriate traffic control by changing the traffic light timing adjustment method according to the user's emotions. The estimation of the user's emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the user's emotion data into the generation AI and cause the generation AI to change the traffic light timing adjustment method.

[0095] During adjustment, the adjustment unit can set custom signal timing according to a specific traffic pattern. Examples of custom signal timing include, but are not limited to, settings according to a specific traffic pattern. For example, the adjustment unit can improve traffic flow by extending the green time of a signal when traffic congestion occurs. The adjustment unit can also ensure pedestrian safety by extending the red time of a signal when there are many pedestrians. The adjustment unit can also adjust the green time of a signal when public transportation delays occur to ensure smooth operation of buses and trains. Thus, setting custom signal timing according to a specific traffic pattern can smooth traffic flow. Some or all of the above-described processing in the adjustment unit may be performed using, or without, AI. For example, the adjustment unit can input specific traffic pattern data to the generation AI and cause the generation AI to set the custom signal timing.

[0096] During adjustment, the adjustment unit can optimize the current adjustment by referring to past adjustment results. Past adjustment results include, but are not limited to, acquisition from a database or past reports. For example, the adjustment unit sets optimal signal timing for current traffic conditions based on past signal timing adjustment results. The adjustment unit can also detect abnormal traffic patterns and make adjustments by referring to past adjustment results. The adjustment unit can also optimize the signal timing adjustment algorithm based on past adjustment results. In this way, the current adjustment can be optimized by referring to past adjustment results. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input past adjustment result data to a generation AI and cause the generation AI to optimize the current adjustment.

[0097] During adjustment, the adjustment unit can simulate different traffic scenarios to determine optimal signal timing. Traffic scenarios include, but are not limited to, simulation software and scenario setting methods. For example, the adjustment unit can simulate a traffic congestion scenario and set optimal signal timing. The adjustment unit can also simulate a traffic volume increase scenario after a large-scale event and adjust the signal timing. The adjustment unit can also simulate a traffic scenario during bad weather and set signal timing that takes safety into consideration. By simulating different traffic scenarios, optimal signal timing can be determined. Some or all of the above-described processing in the adjustment unit can be performed using, for example, AI, or without AI. For example, the adjustment unit can input different traffic scenario data to the generation AI and have the generation AI perform the simulation and determine the signal timing.

[0098] The adjustment unit can estimate the user's emotions and prioritize traffic light timings based on the estimated user's emotions. For example, if the user is feeling stressed, the adjustment unit can prioritize traffic light timings at major intersections. Furthermore, if the user is relaxed, the adjustment unit can prioritize traffic light timings in areas with a high number of pedestrians. Furthermore, if the user is in a hurry, the adjustment unit can prioritize traffic light timings along public transportation routes. This enables more appropriate traffic control by prioritizing traffic light timings according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the adjustment unit can be performed using, for example, an AI, or without an AI. For example, the adjustment unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of traffic light timings.

[0099] During adjustment, the adjustment unit can change the signal timing in real time in response to fluctuations in traffic volume. Examples of methods for changing the signal timing in real time include, but are not limited to, real-time data acquisition methods and control algorithms. For example, when traffic volume suddenly increases, the adjustment unit extends the green time of the signal to smooth traffic flow. Furthermore, when traffic volume decreases, the adjustment unit can extend the red time of the signal to ensure pedestrian safety. Furthermore, the adjustment unit can adjust the signal timing in real time in response to fluctuations in traffic volume. This allows smooth traffic flow by changing the signal timing in real time in response to fluctuations in traffic volume. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input traffic volume data to the generation AI and cause the generation AI to perform real-time adjustment of the signal timing.

[0100] During adjustment, the adjustment unit can adjust the signal timing taking into account the operation status of the public transportation facility. Examples of the operation status of the public transportation facility include, but are not limited to, operation schedules and real-time operation information. For example, when a bus is delayed, the adjustment unit extends the green time of the signal to ensure smooth bus operation. The adjustment unit can also adjust the signal timing in accordance with the train operation schedule. The adjustment unit can also grasp the operation status of the public transportation facility in real time and adjust the signal timing. This allows the signal timing to be optimized by taking into account the operation status of the public transportation facility, thereby ensuring smooth operation of the public transportation facility. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input operation status data of the public transportation facility to the generation AI and cause the generation AI to adjust the signal timing.

[0101] During adjustment, the adjustment unit can set signal timing to reduce the risk of traffic accidents. Examples of the risk of traffic accidents include, but are not limited to, accident occurrence rates and risk assessments. For example, the adjustment unit can ensure safety by extending the red time of a signal at an intersection where traffic accidents frequently occur. The adjustment unit can also adjust signal timing to prevent accidents during times when the risk of traffic accidents is high. The adjustment unit can also improve safety by setting signal timing based on traffic accident data. Thus, safety can be improved by setting signal timing to reduce the risk of traffic accidents. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input traffic accident data into the generation AI and cause the generation AI to set the signal timing. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and adjustment unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects traffic data using the camera 42 or traffic volume sensor of the smart device 14, and analyzes the collected data by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and provides information necessary for adjusting signal timing. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically adjusts signal timing based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and adjustment unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects traffic data using the camera 42 or traffic volume sensor of the smart glasses 214, and analyzes the collected data by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and provides information necessary for adjusting signal timing. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically adjusts signal timing based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and adjustment unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects traffic data using the camera 42 or traffic volume sensor of the headset type terminal 314, and analyzes the collected data by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and provides information necessary for adjusting signal timing. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically adjusts signal timing based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and adjustment unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects traffic data using the camera 42 or traffic volume sensor of the robot 414, and analyzes the collected data by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and provides information necessary for adjusting signal timing. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically adjusts signal timing based on the analysis results.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] When collecting traffic data, the collection unit can analyze drivers' driving styles and reflect the results in adjusting traffic signal timing. For example, if there is a lot of sudden braking or sudden acceleration, the collection unit can extend the green time of the traffic signal to promote smooth traffic flow. In addition, if the driving style is gentle, the collection unit can extend the red time of the traffic signal to ensure pedestrian safety. Furthermore, the collection unit can accumulate driving style data and use it to improve traffic control in the future. This makes it possible to adjust traffic signal timing according to the driver's driving style, thereby optimizing traffic flow.

[0104] When collecting traffic data, the collection unit can acquire vehicle fuel consumption data and reflect it in adjusting traffic signal timing. For example, if fuel consumption is high, the collection unit can extend the green time of the traffic signal to reduce idling time. In addition, if fuel consumption is low, the collection unit can extend the red time of the traffic signal to ensure pedestrian safety. Furthermore, the collection unit can accumulate fuel consumption data and use it to improve traffic control for environmental protection. This makes it possible to adjust traffic signal timing according to vehicle fuel consumption, thereby reducing environmental impact.

[0105] When collecting traffic data, the collection unit can recognize vehicle license plates and adjust traffic light timing for specific vehicles. For example, when an emergency vehicle is approaching, the green light time can be extended to allow quicker passage. The collection unit can also adjust traffic light timing to give priority to public transportation vehicles. Furthermore, the collection unit can accumulate traffic data for specific vehicles and use it to improve traffic control in the future. This makes it possible to adjust traffic light timing according to specific vehicles, thereby optimizing traffic flow.

[0106] The collection unit can acquire vehicle emission data when collecting traffic data and reflect it in adjusting traffic light timing. For example, if emissions are high, the green time of the traffic light can be extended to reduce idling time. The collection unit can also extend the red time of the traffic light when emissions are low to ensure pedestrian safety. Furthermore, the collection unit can accumulate emission data and use it to improve traffic control for environmental measures. This makes it possible to adjust traffic light timing according to vehicle emissions, thereby reducing environmental impact.

[0107] When collecting traffic data, the collection unit can predict vehicle operation routes and reflect the results in adjusting traffic signal timing. For example, if an increase in traffic volume is predicted on a specific route, the green time of the traffic signal can be extended to ensure smooth traffic flow. The collection unit can also extend the red time of the traffic signal based on the predicted operation route to ensure pedestrian safety. Furthermore, the collection unit can accumulate predicted data on operation routes and use it to improve traffic control in the future. This makes it possible to adjust traffic signal timing according to the vehicle operation route, thereby optimizing traffic flow.

[0108] The collection unit can estimate the user's emotions and change the traffic data collection method based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can increase the frequency of traffic data collection to understand the real-time traffic situation in more detail. The collection unit can also reduce the frequency of traffic data collection to reduce the load on the system if the user is relaxed. Furthermore, if the user is in a hurry, the collection unit can quickly collect traffic data and immediately adjust traffic signal timing. This allows for more appropriate data collection by changing the traffic data collection method according to the user's emotions.

[0109] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method is provided. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method of the analysis results according to the user's emotions, it is possible to provide more appropriate information.

[0110] The adjustment unit can estimate the user's emotions and change the method for adjusting traffic signal timing based on the estimated user's emotions. For example, if the user is feeling stressed, the adjustment unit can extend the green time of the traffic signal to ensure smooth traffic flow. If the user is relaxed, the adjustment unit can also extend the red time of the traffic signal to ensure the safety of pedestrians. Furthermore, if the user is in a hurry, the adjustment unit can shorten the green time of the traffic signal to enable quick movement. This allows for more appropriate traffic control by changing the method for adjusting traffic signal timing according to the user's emotions.

[0111] The adjustment unit can estimate the user's emotions and prioritize signal timings based on the estimated user's emotions. For example, if the user is feeling stressed, the adjustment unit can prioritize adjusting signal timings at major intersections. In addition, if the user is relaxed, the adjustment unit can prioritize adjusting signal timings in areas with a high number of pedestrians. Furthermore, if the user is in a hurry, the adjustment unit can prioritize adjusting signal timings for public transportation routes. This enables more appropriate traffic control by prioritizing signal timings according to the user's emotions.

[0112] The adjustment unit can estimate the user's emotions and adjust the traffic light timing based on the estimated user's emotions. For example, if the user is feeling stressed, the adjustment unit can extend the green time of the traffic light to ensure smooth traffic flow. If the user is relaxed, the adjustment unit can also extend the red time of the traffic light to ensure the safety of pedestrians. Furthermore, if the user is in a hurry, the adjustment unit can shorten the green time of the traffic light to enable quick movement. This allows for more appropriate traffic control by adjusting the traffic light timing according to the user's emotions.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The collection unit collects traffic data. Traffic data includes vehicle traffic volume, speed, traffic accident information, pedestrian counts, bus and train schedules, weather data, and information on concerts and sporting events. The collection unit collects real-time traffic data using traffic volume sensors, cameras, and pedestrian counting systems. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is carried out using data analysis algorithms, statistical methods, and machine learning algorithms. This allows for the identification of patterns and trends in traffic data, making it possible to predict traffic conditions and identify problems. Step 3: The adjustment unit automatically adjusts the signal timing based on the analysis results obtained by the analysis unit. The adjustment of signal timing includes the green and red light times, the length of the signal cycle, etc. For example, the green light time can be extended to smooth traffic flow, or the red light time can be extended to ensure pedestrian safety.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0143] 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.

[0144] 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.

[0145] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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, in order to avoid confusion and to 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.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 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 collection unit that collects traffic data; an analysis unit that analyzes the data collected by the collection unit; an adjustment unit that automatically adjusts signal timing based on the analysis result obtained by the analysis unit; A system characterized by:

2. The collecting unit Collect real-time traffic data using traffic sensors or cameras 2. The system of claim 1.

3. The collecting unit Collecting the number of people passing by using a pedestrian counting system 2. The system of claim 1.

4. The collecting unit Get real-time bus and train schedules 2. The system of claim 1.

5. The collecting unit Obtaining weather data and controlling traffic according to weather conditions 2. The system of claim 1.

6. The collecting unit Get information about upcoming concerts or sporting events 2. The system of claim 1.

7. The analysis unit Analyze collected data and calculate signal timing 2. The system of claim 1.

8. The adjustment unit Automatically adjust signal timing based on analysis results 2. The system of claim 1.

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