Information processing system, information processing method, information processing program, information processing device, and data structure

Abstract

To provide an information processing system capable of searching for a route from a departure place to a destination in consideration of safety of a road in a specified time zone.SOLUTION: An information processing system generates road safety data including road link identification information, time zone information, and road safety information related to safety of a road, and associated with road network data for route search via the road link identification information, and acquires information relating to a specified time zone, a departure place, and a destination (S60), extracts a plurality of road links on the basis of the information relating to the departure place and the destination (S61), identifies the road safety information in the acquired specified time zone for each of the plurality of road links extracted (S62), and executes a route search from the departure place to the destination on the basis of a route cost for each of the plurality of road links extracted and the identified road safety information (S63).SELECTED DRAWING: Figure 23

Description

[Technical Field]

[0001] The present disclosure relates to an information processing system, an information processing method, an information processing program, an information processing device, and a data structure. [Background technology]

[0002] Patent Document 1 discloses a route search device capable of performing a route search according to route search conditions. In Patent Document 1, weighting coefficients are set for cost parameters (such as road width and the presence or absence of a median strip) for calculating the link cost of each road link according to the route search conditions, namely, safety priority or drivability priority. In this way, the link cost of each road link varies according to safety priority or drivability priority, making it possible to perform a desired route search according to the route search conditions. [Prior art documents] [Non-patent literature]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2003-21525 Summary of the Invention [Problem to be solved by the invention]

[0004] Incidentally, in the route search device disclosed in Patent Document 1, weighting coefficients for cost parameters are set according to route search conditions, but the values ​​of the cost parameters and weighting coefficients do not change according to the time of day. On the other hand, information related to road safety, such as the brightness of the road's surrounding environment and traffic volume, changes according to the time of day. In this way, when information related to road safety changes according to the time of day, it is desirable to be able to perform a route search that takes into account road safety according to the time of day. In particular, when a user travels from a departure point to a destination at night, it is desirable to be able to perform a route search that takes into account road safety at night.

[0005] In view of the above, the present disclosure aims to provide an information processing system, an information processing method, an information processing program, and an information processing device that are capable of searching for a route from a departure point to a destination taking into account road safety during a specified time period. The present disclosure also aims to provide a data structure that enables route searching that takes into account road safety during a specified time period. [Means for solving the problem]

[0006] An information processing system according to one aspect of the present disclosure performs a route search from a departure point to a destination that takes road safety into consideration. The information processing system generates road safety data that includes road link identification information, time period information, and road safety information related to road safety, and that is associated with road network data for route search via the road link identification information, acquires information related to a specified time period, a departure point, and a destination, extracts multiple road links based on the information related to the departure point and the destination, identifies the road safety information for the acquired specified time period for each of the extracted multiple road links, and performs a route search from the departure point to the destination based on the route costs of each of the extracted multiple road links and the identified road safety information.

[0007] An information processing method according to one aspect of the present disclosure performs a route search from a departure point to a destination based on road safety data including road link identification information, time period information, and road safety information related to road safety, and road network data for route search associated with the road safety data via the road link identification information. The information processing method is executed by a computer and includes the steps of acquiring information regarding a specified time period, a departure point, and a destination, extracting a plurality of road links based on the information regarding the departure point and the destination, specifying the road safety information for the acquired specified time period for each of the extracted road links, and performing a route search from the departure point to the destination based on the route costs of each of the extracted road links and the specified road safety information.

[0008] Also provided is an information processing program that causes a computer to execute the information processing method.

[0009] An information processing device according to one aspect of the present disclosure includes a processor and a memory that stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the information processing device performs the information processing method.

[0010] A data structure according to one aspect of the present disclosure includes road link identification information, time zone information, and road safety information related to road safety. The data structure is associated with road network data for route search via the road link identification information. The road safety information includes at least one of information indicating an evaluation value of the brightness of the surrounding environment of the road, information indicating the number of vehicles on the road, information indicating the number of open stores near the road, information indicating the number of pedestrians on the road, and information indicating the number of events occurring near the road. The number of events includes at least one of the number of crimes, traffic accidents, and suspicious person occurrences. [Effects of the Invention]

[0011] According to the present disclosure, it is possible to provide an information processing system, an information processing method, an information processing program, and an information processing device that can perform a route search from a departure point to a destination taking into account road safety during a specified time period. Also, according to the present disclosure, it is possible to provide a data structure that enables a route search taking into account road safety during a specified time period. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a diagram illustrating an example of an information processing system according to an embodiment of the present disclosure (hereinafter, the present embodiment). [Figure 2] FIG. 1 is a diagram illustrating an example of a configuration of a vehicle. [Figure 3] FIG. 2 is a diagram illustrating an example of a configuration of a mobile terminal. [Figure 4] FIG. 2 illustrates an example of a server configuration. [Figure 5] FIG. 2 is a diagram for explaining road network data. [Figure 6] FIG. 2 is a diagram for explaining a series of processes for generating road safety data according to the present embodiment. [Figure 7] 10 is a flowchart illustrating a series of processes for generating brightness evaluation value data according to the present embodiment. [Figure 8] (a) is a diagram showing an example of an image captured by a camera with a brightness evaluation value of 1. (b) is a diagram showing an example of an image captured by a camera with a brightness evaluation value of 3. (c) is a schematic diagram showing a brightness estimation model. [Figure 9] FIG. 10 is a diagram showing an example of intermediate data in which time information, position information, a road link ID, and information indicating a brightness evaluation value are associated with one another. [Figure 10] FIG. 10 is a diagram illustrating an example of brightness evaluation value data. [Figure 11] 4 is a flowchart illustrating a series of processes for generating vehicle count data according to the present embodiment. [Figure 12]FIG. 10 is a diagram showing an example of people flow data in which the user's means of transportation is a vehicle. [Figure 13] FIG. 10 is a diagram illustrating an example of vehicle number data. [Figure 14] 10 is a flowchart illustrating a series of processes for generating pedestrian count data according to the present embodiment. [Figure 15] FIG. 10 is a diagram illustrating an example of people flow data indicating that the user's means of transportation is walking. [Figure 16] FIG. 10 is a diagram illustrating an example of pedestrian count data. [Figure 17] 10 is a flowchart illustrating a series of processes for generating open store count data according to the present embodiment. [Figure 18] FIG. 10 is a diagram illustrating an example of intermediate data in which store name information, business hours information, and road link identification information are associated with each other. [Figure 19] FIG. 10 is a diagram illustrating an example of data on the number of stores in operation. [Figure 20] 10 is a flowchart illustrating a series of processes for generating event occurrence count data according to the present embodiment. [Figure 21] FIG. 10 is a diagram illustrating an example of event occurrence count data. [Figure 22] FIG. 10 is a diagram showing an example of road safety data. [Figure 23] 10 is a flowchart illustrating a process for executing a route search from a departure point to a destination according to the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0013] (Outline of this embodiment) The outline of this embodiment will be described below.

[0014] An information processing system according to one aspect of the present disclosure performs a route search from a departure point to a destination that takes road safety into consideration. The information processing system generates road safety data that includes road link identification information, time period information, and road safety information related to road safety, and that is associated with road network data for route search via the road link identification information, acquires information related to a specified time period, a departure point, and a destination, extracts multiple road links based on the information related to the departure point and the destination, identifies the road safety information for the acquired specified time period for each of the extracted multiple road links, and performs a route search from the departure point to the destination based on the route costs of each of the extracted multiple road links and the identified road safety information.

[0015] According to the above configuration, a route search from a departure point to a destination is performed based on the route costs of each of a plurality of road links and road safety information for a specified time period. In this way, a route search can be performed that takes into account the safety of roads for a specified time period. For example, when a user travels from a departure point to a destination on foot at night, a route search can be performed that takes into account the safety of the travel route at night. In this way, the user can travel to the destination safely on foot.

[0016] The road safety information may include at least one of information indicating an evaluation value of the brightness of the surrounding environment of the road, information indicating the number of vehicles on the road, information indicating the number of stores operating around the road, information indicating the number of pedestrians on the road, and information indicating the number of events occurring around the road. The number of events may include at least one of the number of crimes, the number of traffic accidents, and the number of suspicious person occurrences.

[0017] According to the above configuration, route search can be performed taking into consideration at least one of the following: the brightness of the surrounding environment of the road during the specified time period, the number of vehicles on the road, the number of open stores around the road, the number of pedestrians walking on the road, and the number of events occurring around the road.

[0018] The road safety information may also include information indicating an evaluation value of the brightness of the surrounding environment of the road, information indicating the number of vehicles on the road, information indicating the number of stores open near the road, information indicating the number of pedestrians on the road, and information indicating the number of events occurring near the road.

[0019] According to the above configuration, route search can be performed taking into consideration the brightness of the surrounding environment of the road during a specified time period, the number of vehicles traveling on the road, the number of businesses around the road, the number of pedestrians walking on the road, and the number of events occurring around the road. In particular, route search can be performed from three perspectives: information on the brightness of the surrounding environment at night, information on pedestrian traffic at night, and information on crimes, traffic accidents, and suspicious people.

[0020] The road safety information may include information indicating an evaluation value of brightness of an environment surrounding the road. The information processing system may acquire image data including an image captured by a camera mounted on a vehicle traveling on the road and a shooting time, acquire location data including a position of the vehicle and a time when the vehicle was located, determine the brightness evaluation value corresponding to the image using a brightness estimation model configured to estimate the brightness evaluation value from the image, determine identification information of the road link corresponding to the position of the vehicle, and generate brightness evaluation value data including the identification information of the road link, time period information, and information indicating the brightness evaluation value.

[0021] According to the above configuration, brightness evaluation value data including road link identification information, time period information, and information indicating a brightness evaluation value is generated based on an image captured by a camera mounted on a vehicle and the vehicle's position. In this way, the brightness evaluation value data can be used to perform route search taking into account the brightness of the surrounding environment of the road during a specified time period.

[0022] The road safety information may include information indicating the number of vehicles on the road. The information processing system may acquire, from a plurality of mobile devices, people flow data including the positions of the mobile devices, position acquisition times of the mobile devices, and means of transportation of users of the mobile devices, determine identification information of the road links corresponding to the positions of the mobile devices, extract people flow data indicating that the means of transportation is a vehicle from the people flow data acquired from the plurality of mobile devices, identify the number of vehicles for each predetermined time period on each road link based on the extracted people flow data, and generate vehicle count data including the identification information of the road link, time period information, and information indicating the number of vehicles.

[0023] According to the above configuration, vehicle count data including road link identification information, time period information, and information indicating the number of vehicles is generated based on people flow data including the location of the mobile device, the time of location acquisition of the mobile device, and the means of transportation of the user who owns the mobile device. In this way, the vehicle count data for the specified time period can be used to perform a route search that takes into account the number of vehicles on the road.

[0024] The road safety information may include information indicating the number of pedestrians on the road. The information processing system may acquire, from a plurality of mobile devices, people flow data including the positions of the mobile devices, position acquisition times of the mobile devices, and means of transportation of users of the mobile devices, determine identification information of the road links corresponding to the positions of the mobile devices, extract people flow data indicating that the means of transportation is walking from the people flow data acquired from the plurality of mobile devices, identify the number of pedestrians on each road link for each predetermined time period based on the extracted people flow data, and generate pedestrian number data including the identification information of the road link, time period information, and information indicating the number of pedestrians.

[0025] According to the above configuration, pedestrian count data including road link identification information, time period information, and information indicating the number of pedestrians is generated based on people flow data including the location of the mobile device, the time of location acquisition of the mobile device, and the means of transportation of the user who owns the mobile device. In this way, the pedestrian count data makes it possible to perform route search taking into account the number of pedestrians on roads in a specified time period.

[0026] The road safety information may include information indicating the number of open stores near the road. The information processing system may acquire store data indicating the business hours and location of each store, determine identification information of the road link corresponding to the store location, identify the number of open stores for each predetermined time period on each road link, and generate open store number data including the identification information of the road link, time period information, and information indicating the number of open stores.

[0027] According to the above configuration, the number of open stores data including the road link identification information, the time period information, and the information indicating the number of open stores is generated based on the store data indicating the business hours and location of each store. In this way, the number of open stores data can be used to perform a route search that takes into account the number of open stores during a specified time period.

[0028] The road safety information may include information indicating the number of events occurring around the road. The number of events may include at least one of the number of crimes, traffic accidents, and suspicious person occurrences. The information processing system may acquire event occurrence data indicating the time and location of an event, determine identification information of the road link corresponding to the location of the event, specify the number of events occurring for each predetermined time period on each road link, and generate event occurrence count data including the identification information of the road link, time period information, and information indicating the number of events occurring.

[0029] According to the above configuration, event occurrence count data including road link identification information, time period information, and information indicating the number of event occurrences is generated based on event occurrence data indicating the time and location of the event. In this way, the event occurrence count data can be used to perform route searches that take into account the number of events occurring around roads in a specified time period.

[0030] The information processing system may also generate brightness evaluation value data including identification information of the road link, time period information, and information indicating the brightness evaluation value, generate vehicle count data including identification information of the road link, time period information, and information indicating the number of vehicles, generate pedestrian count data including identification information of the road link, time period information, and information indicating the number of pedestrians, generate open store count data including identification information of the road link, time period information, and information indicating the number of open stores, generate event occurrence count data including identification information of the road link, time period information, and information indicating the number of event occurrences, and generate the road safety data by integrating the brightness evaluation value data, the vehicle number data, the pedestrian number data, the open store number data, and the event occurrence count data.

[0031] According to the above configuration, road safety data is generated by integrating the brightness evaluation value data, vehicle count data, pedestrian count data, business store count data, and event occurrence count data. In this way, route search can be performed taking into account the brightness of the surrounding environment of the road during a specified time period, the number of vehicles traveling on the road, the number of business stores around the road, the number of pedestrians walking on the road, and the number of events occurring around the road.

[0032] Further, the information processing system acquires image data including an image captured by a camera mounted on a vehicle traveling on the road and the time the image was captured, acquires position data including a position of the vehicle and a time the position was acquired of the vehicle, determines the brightness evaluation value corresponding to the image using a brightness estimation model configured to estimate the brightness evaluation value from the image, determines identification information of the road link corresponding to the position of the vehicle, generates the brightness evaluation value data, acquires people flow data including the positions of the mobile devices, the position acquisition time of the mobile devices, and the means of transportation of users who carry the mobile devices from a plurality of mobile devices, determines identification information of the road link corresponding to the position of the mobile devices, extracts people flow data indicating that the means of transportation is a vehicle from the people flow data acquired from the plurality of mobile devices, and generates the people flow data indicating that the means of transportation is a vehicle. the number of vehicles on each road link for each predetermined time period based on the data obtained from the plurality of mobile devices, and generate the vehicle number data; extracting people flow data indicating that the means of transportation is walking from the people flow data obtained from the plurality of mobile devices; identifying the number of pedestrians on each road link for each predetermined time period based on the people flow data indicating that the means of transportation is walking, and generating the pedestrian number data; obtaining store data indicating the opening hours and locations of each store; determining identification information of the road link corresponding to the store locations; identifying the number of stores open for each predetermined time period on each road link; generating the open store number data; obtaining event occurrence data indicating the time and location of an event; determining identification information of the road link corresponding to the location of the event;

[0033] According to the above configuration, brightness evaluation value data including road link identification information, time period information, and information indicating a brightness evaluation value is generated based on an image captured by a camera mounted on a vehicle and the position of the vehicle. Vehicle count data including road link identification information, time period information, and information indicating the number of vehicles is generated based on people flow data including the position of a mobile device, the time of location acquisition of the mobile device, and the means of transportation of a user carrying the mobile device. Pedestrian count data including road link identification information, time period information, and information indicating the number of pedestrians is generated based on people flow data including the position of a mobile device, the time of location acquisition of the mobile device, and the means of transportation of a user carrying the mobile device. Open store count data including road link identification information, time period information, and information indicating the number of open stores is generated based on store data indicating the business hours and location of each store. Event occurrence count data including road link identification information, time period information, and information indicating the number of event occurrences is generated based on event occurrence data indicating the time and location of an event.

[0034] An information processing method according to one aspect of the present disclosure is an information processing method for performing a route search from a departure point to a destination based on road safety data including road link identification information, time period information, and road safety information related to road safety, and road network data for route search associated with the road safety data via the road link identification information, the information processing method including the steps of: acquiring information regarding a specified time period, a departure point, and a destination; extracting a plurality of road links based on the information regarding the departure point and the destination; identifying the road safety information for the acquired specified time period for each of the extracted road links; and performing a route search from the departure point to the destination based on the route costs of each of the extracted road links and the identified road safety information.

[0035] According to the above method, a route search from a departure point to a destination is performed based on the route costs of each of a plurality of road links and road safety information for a specified time period. In this way, a route search can be performed that takes into account the safety of roads for a specified time period. For example, when a user travels from a departure point to a destination on foot at night, a route search can be performed that takes into account the safety of the travel route at night. In this way, the user can travel to the destination safely on foot.

[0036] The information processing method may further include the step of generating the road safety data.

[0037] According to the above method, it is possible to perform a route search that takes into consideration road safety through the generation of road safety data.

[0038] Furthermore, an information processing program for causing a computer to execute the information processing method may be provided.

[0039] An information processing device according to one aspect of the present disclosure includes a processor and a memory that stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the information processing device performs the information processing method.

[0040] A data structure according to one aspect of the present disclosure includes road link identification information, time zone information, and road safety information related to road safety. The data structure is associated with road network data for route search via the road link identification information. The road safety information includes at least one of information indicating an evaluation value of the brightness of the surrounding environment of the road, information indicating the number of vehicles on the road, information indicating the number of open stores near the road, information indicating the number of pedestrians on the road, and information indicating the number of events occurring near the road. The number of events includes at least one of the number of crimes, traffic accidents, and suspicious person occurrences.

[0041] According to the above data structure, it is possible to realize route search that takes into account at least one of the following: the brightness of the surrounding environment of the road during the specified time period, the number of vehicles on the road, the number of open stores around the road, the number of pedestrians walking on the road, and the number of events occurring around the road.

[0042] (Configuration of Information Processing System 1) An information processing system 1 according to this embodiment will be described below with reference to the drawings. FIG. 1 is a diagram showing an example of the configuration of the information processing system 1 according to this embodiment. As shown in FIG. 1, the information processing system 1 includes a vehicle 2, a server 3, a mobile terminal 4, and a user terminal 7. These are connected to a communication network 5. The vehicle 2, the mobile terminal 4, and the user terminal 7 are communicatively connected to the server 3 via the communication network 5. The communication network 5 is configured, for example, by the Internet or the like.

[0043] (Vehicle 2 configuration) Next, the hardware configuration of the vehicle 2 will be described below with reference to Fig. 2. Fig. 2 is a diagram showing an example of the configuration of the vehicle 2 according to this embodiment. As shown in Fig. 2, the vehicle 2 includes a vehicle control unit 20, a camera 21, a wireless communication unit 22, a GNSS (Global Navigation Satellite System) receiver 23, an HMI (Human Machine Interface) 24, a storage device 25, a drivetrain system 26, and a direction sensor 27. The GNSS receiver 23 may be, for example, a GPS (Global Positioning System) receiver.

[0044] The vehicle 2 may be a vehicle (for example, an autonomous vehicle) that can run in an autonomous driving mode. In this example, a four-wheeled vehicle is given as an example of a vehicle, but the number of wheels of the vehicle 2 is not particularly limited. The vehicle control unit 20 is configured to control various components provided in the vehicle 2, and is configured by, for example, at least one electronic control unit (ECU: Electronic Control Unit). The electronic control unit includes a computer system including one or more processors and one or more memories.

[0045] The camera 21 is configured to capture an image of the surrounding environment ahead of the vehicle 2. The camera 21 is disposed at a predetermined position on the vehicle 2 so as to capture an image of the surrounding environment ahead of the vehicle 2, for example, through the windshield of the vehicle 2. The images captured by the camera 21 may be still images or frames of a moving image. The frame rate of the moving image is not particularly limited. The camera 21 may also be detachably mounted on the vehicle 2. In this regard, the camera 21 may be portable by the driver or the like.

[0046] The wireless communication unit 22 is configured to connect the vehicle 2 to the communication network 5, and includes a transmitting / receiving antenna and a wireless transmitting / receiving circuit. The wireless communication unit 22 may be a wireless communication module compatible with short-range wireless communication standards such as Wi-Fi (registered trademark) or Bluetooth (registered trademark), or may be a wireless communication module compatible with a fourth-generation mobile communication system or a fifth-generation mobile communication system such as LTE.

[0047] The GNSS receiver 23 is configured to acquire information related to the current position of the vehicle 2. The HMI 24 is configured to include an input unit that accepts input operations from the driver and an output unit that outputs information related to the traveling of the vehicle 2 to the driver. The storage device 25 is an external storage device such as an HDD (Hard Disc Drive) or an SSD (Solid State Drive). Map data and vehicle control programs may be stored in the storage device 25. The drive system 26 is configured to control the traveling state of the vehicle 2. For example, the drive system 26 is configured to control the traveling of the vehicle 2 by controlling the accelerator, brake, and steering of the vehicle 2. The direction sensor 27 is configured to detect the traveling direction of the vehicle 2.

[0048] The vehicle 2 transmits image data and position data to the server 3 via the communication network 5. The image data includes multiple images (still images or frames of video) captured by a camera 21 mounted on the vehicle 2 traveling on a road, and the capture times of the multiple images. In the image data, each of the multiple images is associated with a corresponding one of the multiple capture times. The position data includes multiple positions (longitude and latitude) of the vehicle 2 and the acquisition times of the multiple positions of the vehicle 2. In the position data, each of the multiple positions is associated with a corresponding one of the multiple position acquisition times. Although FIG. 1 illustrates a single vehicle 2, multiple vehicles 2 may transmit image data and position data to the server 3.

[0049] (Configuration of mobile terminal 4) Next, the hardware configuration of the mobile terminal 4 will be described below with reference to Fig. 3. Fig. 3 is a diagram showing an example of the hardware configuration of the mobile terminal 4. As shown in Fig. 3, the mobile terminal 4 includes a control unit 40, a storage device 41, a GPS receiver 42, a communication unit 43, an input operation unit 44, a display unit 45, and a motion sensor 46. These components are connected to a communication bus 48.

[0050] The control unit 40 includes a memory and a processor. The storage device 41 is, for example, a flash memory or the like, and is configured to store programs and various data. The GPS receiver 42 is configured to acquire information related to the current location (latitude, longitude) of the mobile terminal 4. The communication unit 43 includes a wireless communication module for communicating with an external device connected to the communication network 5. The wireless communication module is configured to wirelessly communicate with external devices such as base stations and wireless LAN routers, and includes a transmitting / receiving antenna and a wireless transmitting / receiving circuit. The input operation unit 44 is, for example, a touch panel or the like arranged over the video display of the display unit 45. The display unit 45 is, for example, configured with a video display and a video display circuit that drives and controls the video display.

[0051] The motion sensor 46 is configured to detect the movement and attitude of the mobile terminal 4 (particularly, the acceleration, speed, attitude angle, etc. of the mobile terminal 4). The motion sensor 46 is, for example, an acceleration sensor or an angular velocity sensor. The control unit 40 may determine the movement speed of the mobile terminal 4 and the means of transportation (walking, vehicle, etc.) of the user who carries the mobile terminal 4 based on the detection signal detected by the motion sensor 46 and / or the signal detected by the GPS receiver 42. For example, the control unit 40 may determine the means of transportation of the user based on the movement speed of the mobile terminal 4.

[0052] The mobile terminal 4 transmits people flow data including the position information (position information consisting of latitude and longitude), speed information, means of transportation (type of transportation) information, and time information (time information consisting of date and time) of the mobile terminal 4 to the server 3 at predetermined time intervals via the communication network 5. In this way, the server 3 receives people flow data from multiple mobile terminals 4.

[0053] (Server 3 configuration) Next, the hardware configuration of the server 3 will be described below with reference to Fig. 4. Fig. 3 is a diagram showing an example of the configuration of the server 3. As shown in Fig. 3, the server 3 includes a control unit 30, a storage device 31, an input / output interface 32, a communication unit 33, an input operation unit 34, and a display unit 35. These elements are connected to a communication bus 36. The server 3 may be configured as a single server or may be configured as multiple servers. The server 3 may be constructed on-premise or may be a cloud server.

[0054] The control unit 30 includes a memory and a processor. The memory is configured to store computer-readable instructions (programs). For example, the memory may include a read-only memory (ROM) storing various programs and a random access memory (RAM) having multiple work areas for storing various programs executed by the processor. The processor may include at least one of a central processing unit (CPU), a microprocessing unit (MPU), and a graphics processing unit (GPU). The CPU may include multiple CPU cores. The GPU may include multiple GPU cores. The processor may be configured to load a program specified from various programs stored in the storage device 31 or the ROM onto the RAM and execute various processes in cooperation with the RAM. The memory may store an information processing program for causing the processor to execute a series of processes (information processing method) executed by the server 3.

[0055] The storage device 31 is, for example, a storage device (storage) such as an HDD or SSD, and is configured to store programs and various data. The storage device 31 stores map data, people flow data, brightness evaluation value data, vehicle count data, pedestrian count data, business store count data, event occurrence count data, road safety data, etc. The map data includes road network data for route search, background data, note data, address data, and store data.

[0056] As shown in FIG. 5, the road network data is composed of a plurality of road links (lines) and a plurality of road nodes (points). A road node is connected to a plurality of road links. Each road node is connected to other road nodes via one road link. Each road link is assigned unique identification information (ID). Each road link is associated with, for example, road attribute information and road regulation information (e.g., one-way streets, etc.). The road attribute information includes, for example, road type information, route cost information (e.g., distance cost information), number of lanes information, and road width information. Each road node is assigned unique identification information (ID). Each road node is associated with, for example, lane information, direction guidance information, traffic light information, and intersection name information. In this way, the road network data includes information related to road links and information related to road nodes. The server 3 can perform a route search from a departure point to a destination by using the road network data.

[0057] The background data includes illustration data relating to the visual background of the map (e.g., map shapes such as buildings, ocean, forests, roads, etc.). The annotation data includes text information to be displayed on the map (e.g., names of buildings and mountains, etc.). The address data includes address information associated with each building on the map. The store information data includes information about stores on the map (e.g., business hours, etc.). Data other than map data, such as brightness evaluation value data, will be described later.

[0058] Returning to FIG. 4, the input / output interface 32 is an interface that enables connection between an external device and the server 3, and includes an interface conforming to a predetermined communication standard such as the USB standard or the HDMI (registered trademark) standard. The communication unit 33 may include various wired communication modules for communicating with external terminals on the communication network 5. The input operation unit 34 is, for example, a touch panel, a mouse, and / or a keyboard, and is configured to accept input operations by an operator and to generate operation signals in response to the input operations by the operator. The display unit 35 is, for example, configured by a video display and a video display circuit.

[0059] Returning to FIG. 1, the user terminal 7 is a terminal operated by the user U. The user terminal 7 is communicatively connected to the server 3 via the communication network 5, and receives map information from the server 3. A map application or a web browser for displaying a map may be installed on the user terminal 7.

[0060] (Road safety data generation processing) Next, with reference to Fig. 6, a process for generating road safety data (an example of a data structure) according to this embodiment will be described below. Fig. 6 is a diagram for explaining a series of processes for generating road safety data according to this embodiment. An example of road safety data is shown in Fig. 22. As shown in Fig. 22, the road safety data includes identification information of a road link (hereinafter referred to as a road link ID), date information, time zone information, and road safety information related to road safety (such as information indicating a brightness evaluation value). The road link ID, date information, time zone information, and road safety information are associated with each other.

[0061] The road safety data is associated with road network data for route search via a road link ID. Each road link is associated with road safety information included in the road safety data and route cost information (particularly, distance cost information) included in the road network data. Therefore, as will be described later, the server 3 can perform a route search from a departure point to a destination based on the road safety data and road network data that are associated with each other via the road link ID. In particular, the server 3 can perform a route search that takes into account the safety of travel routes at night based on the road safety data and road network data.

[0062] As shown in FIG. 22, the road safety data includes, as road safety information, information indicating an evaluation value of the brightness of the road's surrounding environment, information indicating the number of vehicles on the road, information indicating the number of pedestrians around the road, information indicating the number of open stores around the road, and information indicating the number of events occurring around the road. These pieces of road safety information are associated with a road link ID, date information, and time period information. In this example, the road safety data records this road safety information (excluding event occurrence count information) for each road link every hour. Meanwhile, event occurrence count information is recorded for each road link every three hours. For example, the number of vehicles on road link ID: X0001 from 2:00 to 3:00 on January 1st is 100. This indicates that 100 vehicles were present on the road with road link ID: X0001 from 2:00 to 3:00 on January 1st.

[0063] Next, as shown in FIG. 6, in step S1, the server 3 (more specifically, the control unit 30 of the server 3) generates brightness evaluation value data (see FIG. 10) including a road link ID, date and time period information, and information indicating an evaluation value of the brightness of the surrounding environment of the road. In step S2, the server 3 generates vehicle count data (see FIG. 13) including a road link ID, date and time period information, and information indicating the number of vehicles (traffic volume) present on the road. In step S3, the server 3 generates pedestrian count data (see FIG. 16) including a road link ID, date and time period information, and information indicating the number of pedestrians (people flow volume) present around the road.

[0064] In step S4, the server 3 generates open store count data (see FIG. 19) including a road link ID, date and time period information, and information indicating the number of open stores located around the road. In step S5, the server 3 generates event occurrence count data (see FIG. 21) including a road link ID, date and time period information, and information indicating the number of events occurring around the road. Here, the event occurrence count includes at least one of the number of crimes, the number of traffic accidents, and the number of suspicious person occurrences. After executing the processes of steps S1 to S5, the server 3 generates road safety data shown in FIG. 22 by integrating the brightness evaluation value data, vehicle count data, pedestrian count data, open store count data, and event occurrence count data (step S6).

[0065] (Brightness evaluation data generation process) Next, the process of generating each piece of data generated in steps S1 to S5 will be described below. First, a series of processes for generating brightness evaluation value data will be described below with reference to FIGS. 7 to 10. FIG. 7 is a flowchart for explaining a series of processes for generating brightness evaluation value data according to this embodiment. FIG. 8(a) is a diagram showing an example of an image captured by the camera 21 with a brightness evaluation value of 1. FIG. 8(b) is a diagram showing an example of an image captured by the camera 21 with a brightness evaluation value of 3. FIG. 8(c) is a schematic diagram showing a brightness estimation model 120. FIG. 9 is a diagram showing an example of intermediate data in which time information (date and time information), location information, a road link ID, and information indicating a brightness evaluation value are associated with each other. FIG. 10 is a diagram showing an example of brightness evaluation value data.

[0066] As shown in Fig. 7, in step S10, the server 3 acquires image data including a plurality of images and a plurality of shooting times from the vehicle 2 via the communication network 5. In step S11, the server 3 acquires position data including a plurality of positions of the vehicle 2 and a plurality of position acquisition times from the vehicle 2 via the communication network 5. In step S12, the server 3 determines a brightness evaluation value corresponding to the image using a brightness estimation model 120 (see Fig. 8) configured to estimate a brightness evaluation value from the image.

[0067] The brightness estimation model 120 is a trained model constructed by machine learning. The brightness estimation model 120 is constructed using various training images that exhibit brightness evaluation values ​​1 to 5. The brightness estimation model 120 is stored in the storage device 31 of the server 3, and the control unit 30 can determine the brightness evaluation value of an image by using the brightness estimation model 120.

[0068] As the brightness evaluation value progresses from 1 to 5, the surrounding environment of the road becomes brighter. The brightness evaluation value of a learning image may be determined by evaluations by multiple people. In this case, the brightness evaluation value of an image estimated by the brightness estimation model 120 reflects the subjective opinions of multiple people. When the brightness evaluation value of the image shown in FIG. 8(a) is 1, the probability of a unit associated with a brightness evaluation value of 1 in the output layer of the brightness estimation model 120 is the highest. Similarly, when the brightness evaluation value of the image shown in FIG. 8(b) is 3, the probability of a unit associated with a brightness evaluation value of 3 in the output layer of the brightness estimation model 120 is the highest.

[0069] Returning to FIG. 7, in step S13, the server 3 determines a road link ID corresponding to the position of the vehicle 2. Each road link has a start point position, an end point position, and the positions of one or more constituent points between the start point and the end point. In this way, the server 3 identifies the road link ID that is most relevant to the position of the vehicle 2 from the position information associated with each road link. In particular, the server 3 identifies the road link having position information that matches the vehicle position or the road link having position information that is closest to the vehicle position, and then obtains the ID of the identified road link.

[0070] In step S14, the server 3 generates brightness evaluation value data (see FIG. 10) including a road link ID, date and time zone information, and information indicating an evaluation value of the brightness of the surrounding environment of the road. The server 3 first generates intermediate data (see FIG. 9) in which time information (date and time), position information (longitude and latitude) of the vehicle 2, a road link ID, and information indicating a brightness evaluation value are associated with each other. At this point, since the position data and image data have time information (shooting time and position acquisition time), the vehicle position and image are associated with each other via the time information. In addition, since the brightness evaluation value of each image and the road link ID linked to each vehicle position are determined, intermediate data can be generated in which the time information, vehicle position, road link ID, and brightness evaluation value are associated with each other, as shown in FIG. 9. Next, the server 3 determines a brightness evaluation value for each hour on each date for each road link based on the intermediate data. In this regard, if multiple brightness evaluation values ​​associated with the conditions of road link ID: X0001, date: January 1, and time period: 00:00-01:00 exist in the intermediate data, the average or median of the multiple brightness evaluation values ​​may be determined as the brightness evaluation value associated with the above conditions in the brightness evaluation value data. In this way, the server 3 can generate brightness evaluation value data in which road link IDs, dates, time periods, and brightness evaluation values ​​are associated with each other. As shown in FIG. 10, in the brightness evaluation value data, brightness evaluation values ​​are associated with road link IDs and date and time period information, so that brightness evaluation values ​​according to these conditions can be extracted.

[0071] (Generation of vehicle number data) Next, a series of processes for generating vehicle count data will be described below with reference to Figs. 11 to 13. Fig. 11 is a flowchart for explaining a series of processes for generating vehicle count data according to this embodiment. Fig. 12 is a diagram showing an example of people flow data in which the identification information (terminal ID) of the mobile terminal 4, the position acquisition time of the mobile terminal 4, the position of the mobile terminal 4, the speed of the mobile terminal 4, the means of transportation (vehicle only) of the user who owns the mobile terminal 4, and a road link ID are associated with each other. Fig. 13 is a diagram showing an example of vehicle count data.

[0072] As shown in FIG. 11 , in step S20, the server 3 acquires people flow data from multiple mobile devices 4 via the communication network 5. In the people flow data, the terminal ID of the mobile device 4, location information, speed information, mode of transportation information, and time information (date and time) are associated with each other. In step S21, the server 3 determines a road link ID corresponding to the location of the mobile device 4. The server 3 identifies the road link ID most relevant to the location of the mobile device 4 from the location information associated with each road link. In particular, the server 3 identifies the road link having location information that matches the location of the mobile device 4 or the road link having location information closest to the location, and then acquires the ID of the identified road link. Thereafter, the server 3 adds the road link to the people flow data acquired from the mobile device 4, thereby generating people flow data in which the location information of the mobile device 4, speed information, mode of transportation information, time information, and road link ID are associated with each other.

[0073] In step S22, the server 3 extracts people flow data indicating that the means of transportation is a vehicle from the people flow data acquired from the multiple mobile terminals 4. Fig. 12 shows people flow data including the terminal ID of the mobile terminal 4, location information, speed information, means of transportation information (limited to vehicles), time information, and road link IDs. In particular, Fig. 12 shows people flow data in which the means of transportation is a vehicle.

[0074] In step S23, the server 3 identifies the number of vehicles for each road link ID for each predetermined time period (each hour in this example) based on the people flow data extracted in step S22 (see FIG. 12). Next, in step S24, the server 3 generates vehicle count data including the road link ID, date and time period information, and information indicating the number of vehicles (traffic volume) (see FIG. 13). As shown in FIG. 13, in the vehicle count data, the number of vehicles (traffic volume) is associated with the road link ID and the date and time period information, so that the number of vehicles according to these conditions can be extracted.

[0075] (Generation and processing of pedestrian count data) Next, a series of processes for generating pedestrian count data will be described below with reference to Figs. 14 to 16. Fig. 14 is a flowchart for explaining a series of processes for generating pedestrian count data according to this embodiment. Fig. 15 is a diagram showing an example of people flow data in which the terminal ID of the mobile terminal 4, the position acquisition time of the mobile terminal 4, the position of the mobile terminal 4, the speed of the mobile terminal 4, the user's means of transportation (walking only), and a road link ID are associated with each other. Fig. 16 is a diagram showing an example of pedestrian count data.

[0076] As shown in FIG. 14, in step S30, the server 3 acquires people flow data from multiple mobile terminals 4 via the communication network 5. In the people flow data, the terminal ID of the mobile terminal 4, location information, speed information, mode of transportation information, and time information (date and time) are associated with each other. In step S31, the server 3 determines a road link ID corresponding to the location of the mobile terminal 4. The server 3 identifies the road link ID most relevant to the location of the mobile terminal 4 from the location information associated with each road link. Thereafter, the server 3 adds the road link to the people flow data acquired from the mobile terminal 4, thereby generating people flow data in which the location information, speed information, mode of transportation information, time information, and road link ID of the mobile terminal 4 are associated with each other.

[0077] In step S32, the server 3 extracts people flow data indicating that the mode of transportation is walking from the people flow data acquired from the multiple mobile terminals 4. Fig. 15 shows people flow data including the terminal ID of the mobile terminal 4, location information, speed information, mode of transportation information (limited to walking), time information, and road link ID. In particular, Fig. 15 shows people flow data in which the mode of transportation is walking.

[0078] In step S33, the server 3 identifies the number of pedestrians for each road link ID for each predetermined time period (each hour in this example) based on the people flow data extracted in step S32 (see FIG. 15). Next, in step S34, the server 3 generates pedestrian count data including the road link ID, date and time period information, and information indicating the number of pedestrians (people flow rate) (see FIG. 16). As shown in FIG. 16, in the pedestrian count data, the number of pedestrians (people flow rate) is associated with the road link ID and the date and time period information, so that the number of pedestrians according to these conditions can be extracted.

[0079] (Generation of data on the number of operating stores) Next, a series of processes for generating open store count data will be described below with reference to Figures 17 to 19. Figure 17 is a flowchart for explaining a series of processes for generating open store count data according to this embodiment. Figure 18 is a diagram showing an example of intermediate data in which store name information, business hours information, and road link IDs are associated with one another. Figure 19 is a diagram showing an example of open store count data.

[0080] As shown in FIG. 17, in step S40, the server 3 acquires store data included in the map data. The store data includes store name information, business hours information, and location information for each store (e.g., convenience store, restaurant, etc.). In step S41, the server 3 identifies a road link ID corresponding to the location of each store. In particular, the server 3 identifies a road link having location information that matches the store's location or a road link having location information closest to the store's location, and then acquires the ID of the identified road link. In this way, as shown in FIG. 18, intermediate data is generated in which store name information, business hours information (date, business hours), and road link IDs are associated with each other.

[0081] In step S42, the server 3 identifies the number of open stores for each predetermined time period (each hour in this example) for each road link ID based on the intermediate data shown in FIG. 18. For example, if there are five open stores under the conditions of road link ID: X00001, date: January 1, and time period: 00:00 to 01:00, the number of open stores associated with these conditions is five. In step S43, the server 3 generates open store count data including the road link ID, date and time period information, and information indicating the number of open stores (see FIG. 19). As shown in FIG. 19, in the open store count data, the number of open stores is associated with the road link ID and date and time period information, so that the number of open stores according to these conditions can be extracted.

[0082] (Event occurrence count data generation process) Next, a series of processes for generating event occurrence count data will be described below with reference to Figures 20 and 21. Figure 20 is a flowchart for explaining a series of processes for generating event occurrence count data according to this embodiment. Figure 21 is a diagram showing an example of event occurrence count data. Here, the event occurrence count includes at least one of the number of crimes, the number of traffic accidents, and the number of suspicious person occurrences.

[0083] As shown in FIG. 20 , in step S50, the server 3 acquires event occurrence data indicating at least the time and location of an event, such as a crime occurrence, a traffic accident, or the appearance of a suspicious person. The event occurrence data may be data made public by an administrative agency such as the National Police Agency. The event occurrence data may be acquired from an external server via the communication network 5, or may be imported into the server 3 via a storage medium or the like. In step S51, the server 3 determines a road link ID corresponding to the location of the event. In particular, the server 3 may identify a road link located near the location of the event occurrence or a road link having location information matching the location of the event occurrence, and then acquire the identified road link ID. Alternatively, the server 3 may identify multiple road links located within a predetermined distance from the location of the event occurrence, and then acquire the identified multiple road link IDs. In this case, one or more road link IDs corresponding to the location of the event are determined.

[0084] Next, in step S52, the server 3 identifies the number of event occurrences for each predetermined time period (every three hours in this example) on each road link. For example, if two events occur under the conditions of road link ID: X00001, date: January 1, and time period: 00:00 to 03:00, the number of event occurrences associated with the conditions is two. In step S53, the server 3 generates event occurrence count data including the road link ID, date and time period information, and information indicating the number of event occurrences (see FIG. 21). As shown in FIG. 21, in the event occurrence count data, the number of event occurrences is associated with the road link ID and date and time period information, so that the number of event occurrences according to these conditions can be extracted.

[0085] As shown in Fig. 6, brightness evaluation value data, vehicle count data, pedestrian count data, business store count data, and event occurrence count data are generated through the processing of steps S1 to S5, and these data are then integrated to generate road safety data (step S6). As shown in Fig. 22, the road safety data associates a road link ID, date and time period information, information indicating the number of pedestrians, information indicating the number of vehicles, brightness evaluation value information, information indicating the number of business stores, and information indicating the number of event occurrences. Therefore, it is possible to extract the number of pedestrians, number of vehicles, brightness evaluation value, number of business stores, and number of event occurrences as road safety information according to the road link ID and date and time period.

[0086] Here, the brightness evaluation value indicates the brightness of the surrounding environment of a road. Therefore, if the brightness evaluation value associated with a specific road link ID is high, it is estimated that the surrounding environment of the road linked to the specific road link ID is bright.

[0087] Furthermore, the greater the number of vehicles (traffic volume) at night, the brighter the area around the road is illuminated by the headlights of many vehicles, so the number of vehicles is an indicator of the brightness of the surrounding environment of the road. Therefore, if there are a large number of vehicles associated with a specific road link ID, it can be estimated that the surrounding environment of the road linked to the specific road link ID is bright.

[0088] In this way, the brightness evaluation value related to the brightness of the surrounding environment and the number of vehicles serve as an index for evaluating road safety at night.

[0089] The number of pedestrians is also an indicator of the number of people on a road. If the number of pedestrians associated with a specific road link ID is large, it is estimated that there is a lot of pedestrian traffic on the road linked to the specific road link ID. Therefore, the number of pedestrians associated with pedestrian traffic is one indicator for evaluating road safety at night.

[0090] Furthermore, the greater the number of stores that are open at night, the brighter the area around the road is lit by the lights of the stores and the more crowded the area is. Therefore, if there are a large number of stores that are open associated with a specific road link ID, it is estimated that the surrounding environment of the road linked to the specific road link ID is bright and there is a lot of foot traffic. In this way, the number of stores that are open, which is related to the brightness of the surrounding environment and the number of people, is one indicator for evaluating the safety of roads at night.

[0091] Furthermore, the greater the number of event occurrences, the more likely a traffic accident is to occur and / or the area is considered to be dangerous. Therefore, if the number of event occurrences associated with a specific road link ID is large, it is estimated that the surrounding environment of the road linked to the specific road link ID is dangerous. In this way, the number of event occurrences associated with a dangerous area is one index for evaluating road safety at night.

[0092] In this way, the road safety data according to this embodiment includes information that serves as various indices for evaluating road safety. Furthermore, since the road safety data is associated with the road network data via the road link ID, by utilizing both the road safety data and the road network data, it is possible to perform a route search from a departure point to a destination that takes into account road safety at night.

[0093] In this embodiment, the date information does not have to be included in the road safety data. In this case, the road safety information is associated with each of the time period information and the road link ID. In addition, in this embodiment, the road safety information (excluding the number of event occurrences) is recorded every hour, but the time width of each time period is not limited to one hour and may be any time width.

[0094] (Route search that takes into account the safety of the travel route) Next, the process of executing a route search from the departure point to the destination will be described below with reference to Fig. 23. Fig. 23 is a flowchart for explaining the process of executing a route search from the departure point to the destination according to this embodiment. In this example, the results of the route search process are displayed on a user terminal 7 that is communicatively connected to the server 3 via the communication network 5. The user terminal 7 is assumed to have a map display app and a browser installed.

[0095] 23, in step S60, in response to an input operation by the user U, the user terminal 7 transmits information indicating a designated time period, a departure point, and a destination to the server 3. The information indicating the designated time period is information related to a designated time period associated with the current date. For example, the information indicating the designated time period may be information indicating 01:00 to 02:00 on December 31st. Furthermore, if date information is not included in the road safety data, the information indicating the designated time period may be information indicating 01:00 to 02:00.

[0096] In step S61, the server 3 extracts a plurality of road links based on information about the departure point and the destination. In particular, the server 3 extracts a plurality of road links existing between the departure point and the destination by referring to road network data.

[0097] In step S62, the server 3 refers to the road safety data to identify road safety information (information indicating the number of pedestrians, the number of vehicles, the brightness rating, the number of open stores, and the number of event occurrences) for the specified time period for each of the extracted road links. In particular, the server 3 refers to the road safety data to identify road safety information linked to the specified time period (date and time period) and the extracted road link IDs. For example, as shown in Fig. 22, the road safety information linked to the specified time period: 01:00 to 02:00 on January 1st and road link ID: X00002 indicates that the number of pedestrians is 100, the number of vehicles is 10, the brightness rating is 3, the number of open stores is 5, and the number of event occurrences is 1.

[0098] In step S63, the server 3 performs a route search from the departure point to the destination based on the route costs (particularly, distance costs) of each of the extracted road links and the identified road safety information. In particular, the server 3 performs a route search from the departure point to the destination based on the route costs and road safety information associated with the extracted road link IDs.

[0099] For example, the server 3 may calculate the total link cost of each road link based on the route cost associated with each road link ID and road safety information (number of pedestrians, number of vehicles, brightness rating, number of open stores, number of events). For example, the server 3 may use a graph neural network (GNN) to calculate the total link cost of each road link based on the route cost associated with each road link ID and road safety information. Then, the server 3 may calculate a route from the departure point to the destination that minimizes the total total link cost based on the calculated total link cost of each road link. In this regard, the server 3 may determine the route from the departure point to the destination that minimizes the total link cost based on a route search algorithm such as Dijkstra's algorithm or A-Star algorithm.

[0100] The total link cost of a given road link may be calculated based on six factors: distance cost, number of pedestrians, number of vehicles, brightness evaluation value, number of open stores, and number of event occurrences. Weighting coefficients multiplied by each factor may be set appropriately.

[0101] The server 3 may further extract a plurality of road links that satisfy predetermined conditions based on six elements: the number of pedestrians, the number of vehicles, the brightness evaluation value, the number of open stores, and the number of event occurrences, and then determine a route from the departure point to the destination with the smallest total distance cost based on the extracted plurality of road links. For example, the server 3 may extract a plurality of road links with a brightness evaluation value of 3 or more and a number of event occurrences of 0 as predetermined conditions, and then calculate a route from the departure point to the destination with the smallest total distance cost based on the extracted plurality of road links. In this case, the calculated route consists only of a plurality of road links with a brightness evaluation value of 3 or more and a number of event occurrences of 0.

[0102] Thereafter, the server 3 transmits the route search result to the user terminal 7 via the communication network 5. Thereafter, the route search result is displayed on the display unit of the user terminal 7.

[0103] According to this embodiment, a route search from a departure point to a destination is performed based on the route costs of each of a plurality of road links and road safety information for a specified time period. In this way, a route search can be performed that takes into account the safety of the road during a specified time period. For example, when a user U travels from a departure point to a destination on foot at night, a route search can be performed that takes into account the safety of the travel route at night. More specifically, a route search can be performed that takes into account the brightness of the surrounding environment of the road during a specified time period, the number of vehicles traveling on the road, the number of open stores around the road, the number of pedestrians walking on the road, and the number of events, such as crimes, occurring around the road. In particular, a desired route search can be performed from three perspectives: information on the brightness of the surrounding environment at night, information on pedestrian traffic at night, and information on dangerous events, such as crimes, traffic accidents, and suspicious persons. In this way, the user U can travel safely to the destination on foot.

[0104] In this embodiment, the road safety data is generated by integrating the brightness evaluation value data, the vehicle count data, the pedestrian count data, the number of open stores data, and the event occurrence count data, but this embodiment is not limited to this. In this respect, the road safety data may be generated based on at least one of the brightness evaluation value data, the vehicle count data, the pedestrian count data, the number of open stores data, and the event occurrence count data. In other words, the road safety information included in the road safety data may be composed of at least one of information indicating the number of pedestrians, the number of vehicles, the brightness evaluation value, the number of open stores, and the event occurrence count.

[0105] For example, if the road safety information only includes information indicating a brightness evaluation value, it is possible to perform a route search that takes into account the brightness of the surrounding environment of the road during a time period such as nighttime. If the road safety information only includes information indicating the number of vehicles, it is possible to perform a route search that takes into account the traffic volume during a time period such as nighttime. If the road safety information only includes information indicating the number of pedestrians, it is possible to perform a route search that takes into account the number of people on the road during a time period such as nighttime. If the road safety information only includes information indicating the number of open stores, it is possible to perform a route search that takes into account the brightness and number of people in the surrounding environment during a time period such as nighttime. If the road safety information only includes information indicating the number of events occurring, it is possible to perform a route search that takes into account the public safety (dangerous areas) during a time period such as nighttime.

[0106] Furthermore, in this embodiment, road safety data is utilized for route search that takes road safety into consideration at night, but the use of road safety data is not limited to route search that takes road safety into consideration. For example, road safety data can be used to identify travel routes with high risk. Therefore, road safety data can be used to set optimal patrol routes for crime prevention patrols. Furthermore, road safety data can be effectively utilized in formulating urban plans and management plans. Furthermore, when a user who is not yet a junior high school student uses the user terminal 7 to search for a route at night, the road safety data can be utilized to present a travel route that avoids roads that meet specific conditions.

[0107] Furthermore, in this embodiment, even if the user U travels from the departure point to the destination by bicycle or electric kick scooter rather than on foot, the user U can similarly use the information processing system 1 to search for a route that takes into account road safety during specified time periods, such as at night.

[0108] Although the embodiments of the present invention have been described above, the technical scope of the present invention should not be construed as being limited by the description of the present embodiments. The present embodiments are merely examples, and it will be understood by those skilled in the art that various modifications of the embodiments are possible within the scope of the invention described in the claims. The technical scope of the present invention should be determined based on the scope of the invention described in the claims and its equivalents. [Explanation of symbols]

[0109] 1: Information processing system 2: Vehicle 3: Server 4: Mobile devices 5: Communication network 7: User terminal 20: Vehicle control unit 21: Camera 22: Wireless communication unit 23: GNSS receiver 25: Storage device 26: Drivetrain system 27: Orientation sensor 30: Control unit 31: Storage device 32: Input / output interface 33: Communications Department 34: Input operation section 35: Display section 36:Communication bus 40: Control unit 41:Storage device 42: GPS receiver 43: Communications Department 44: Input operation section 45: Display section 46: Motion sensor 48:Communication bus 120: Brightness estimation model U:User

Claims

1. An information processing system that performs a route search from a departure point to a destination during a specified time period based on road safety data and road network data for route search that is associated with the road safety data via identification information of road links, comprising: the road safety data includes identification information of the road link, time zone information, and road safety information related to road safety; In the road safety data, each piece of road safety information is associated with one piece of road link identification information among a plurality of pieces of road link identification information and one piece of time zone information among a plurality of pieces of time zone information; The information processing system includes: Obtain information about the time period, origin, and destination; extracting a plurality of road links based on information relating to the departure point and the destination; Identifying the road safety information for the acquired time period for each of the extracted road links; performing a route search from the departure point to the destination during the acquired time period based on the route costs of each of the extracted road links and the specified road safety information; Information processing system.

2. An information processing system for generating road safety data, comprising: The road safety data includes road link identification information, time zone information, and road safety information related to road safety; the road safety data is associated with road network data for route search via the identification information of the road link; the road safety information includes information indicating an evaluation value of brightness of the surrounding environment of the road, The information processing system includes: Acquire image data including an image captured by a camera mounted on a vehicle traveling on the road and the time of the image capture; acquiring location data including a location of the vehicle and a location acquisition time of the vehicle; determining a brightness estimate corresponding to the image using a brightness estimation model configured to estimate the brightness estimate from the image; determining an identification of the road link corresponding to the vehicle's location; generating brightness evaluation value data including identification information of the road link, time zone information, and information indicating the brightness evaluation value; generating the road safety data based at least on the brightness evaluation value data; Information processing system.

3. The road safety information includes: information indicating the number of vehicles present on the road; Information indicating the number of stores operating around the road; information indicating the number of pedestrians present on the road; Information indicating the number of events occurring around the road; and further comprising at least one of The number of occurrences of events includes at least one of the number of crimes, the number of traffic accidents, and the number of suspicious persons. The information processing system according to claim 2 .

4. The road safety information includes: information indicating the number of vehicles present on the road; Information indicating the number of stores that are open near the road; information indicating the number of pedestrians present on the road; Information indicating the number of events occurring around the road; further comprising: The information processing system according to claim 3 .

5. the road safety information includes information indicating the number of vehicles present on the road; The information processing system includes: Acquire people flow data from a plurality of mobile devices, the data including the positions of the mobile devices, the time when the positions of the mobile devices were acquired, and the means of transportation of users who possess the mobile devices; determining identification information of the road link corresponding to the location of the mobile terminal; extracting people flow data indicating that the means of transportation is a vehicle from the people flow data acquired from the plurality of mobile devices; Identifying the number of vehicles for each predetermined time period on each road link based on the extracted people flow data; generating vehicle count data including identification information of the road link, time zone information, and information indicating the number of vehicles; The information processing system according to any one of claims 2 to 4.

6. the road safety information includes information indicating the number of pedestrians present on the road; The information processing system includes: Acquire people flow data from a plurality of mobile devices, the data including the positions of the mobile devices, the time when the positions of the mobile devices were acquired, and the means of transportation of users who possess the mobile devices; determining identification information of the road link corresponding to the location of the mobile terminal; extracting people flow data indicating that the means of transportation is walking from the people flow data acquired from the plurality of mobile devices; Identifying the number of pedestrians for each predetermined time period on each road link based on the extracted people flow data; generating pedestrian count data including identification information of the road link, time period information, and information indicating the number of pedestrians; The information processing system according to any one of claims 2 to 4.

7. The road safety information includes information indicating the number of stores that are open near the road, The information processing system includes: Obtain store data indicating the opening hours and locations of each store; determining an identity of the road link corresponding to the store location; Identifying the number of stores open for each predetermined time period on each road link; generating business store number data including the road link identification information, time zone information, and information indicating the business store number; The information processing system according to any one of claims 2 to 4.

8. the road safety information includes information indicating the number of events occurring around the road, The number of occurrences of events includes at least one of the number of crimes, the number of traffic accidents, and the number of suspicious persons, The information processing system includes: obtaining event occurrence data indicating the time and location of the event; determining identification information of the road link corresponding to the location where the event occurred; Identifying the number of events occurring in each road link for each predetermined time period; generating event occurrence count data including identification information of the road link, time period information, and information indicating the number of event occurrences; The information processing system according to any one of claims 2 to 4.

9. The information processing system includes: generating vehicle count data including identification information of the road link, time zone information, and information indicating the number of vehicles; generating pedestrian count data including identification information of the road link, time period information, and information indicating the number of pedestrians; generating business store number data including the road link identification information, time zone information, and information indicating the business store number; generating event occurrence count data including identification information of the road link, time period information, and information indicating the number of event occurrences; generating the road safety data by integrating the brightness evaluation value data, the vehicle number data, the pedestrian number data, the business store number data, and the event occurrence number data; The information processing system according to claim 4 .

10. The information processing system includes: Acquire people flow data from a plurality of mobile devices, the data including the positions of the mobile devices, the time when the positions of the mobile devices were acquired, and the means of transportation of users who possess the mobile devices; determining identification information of the road link corresponding to the location of the mobile terminal; extracting people flow data indicating that the means of transportation is a vehicle from the people flow data acquired from the plurality of mobile devices; Identifying the number of vehicles for each predetermined time period on each road link based on people flow data indicating that the means of transportation is a vehicle; generating the vehicle count data; extracting people flow data indicating that the means of transportation is walking from the people flow data acquired from the plurality of mobile devices; Identifying the number of pedestrians in each road link for each predetermined time period based on the people flow data indicating that the means of transportation is walking; generating the pedestrian count data; Obtain store data indicating the opening hours and locations of each store; determining an identity of the road link corresponding to the store location; Identifying the number of stores open for each predetermined time period on each road link; generating the number of stores in operation; obtaining event occurrence data indicating the time and location of the event; determining identification information of the road link corresponding to the location where the event occurred; Identifying the number of events occurring in each road link for each predetermined time period; generating the event occurrence count data; The information processing system according to claim 9 .

11. An information processing method for performing a route search from a departure point to a destination in a specified time period based on road safety data and road network data for route search associated with the road safety data via identification information of road links, comprising: the road safety data includes identification information of the road link, time zone information, and road safety information related to road safety; In the road safety data, each piece of road safety information is associated with one piece of road link identification information among a plurality of pieces of road link identification information and one piece of time zone information among a plurality of pieces of time zone information; The information processing method includes: obtaining information regarding a time period, an origin, and a destination; extracting a plurality of road links based on information about the starting point and the destination; specifying the road safety information for the acquired time period for each of the extracted road links; performing a route search from the departure point to the destination during the acquired time period based on the route costs of each of the extracted road links and the identified road safety information; Including, A computer-implemented information processing method.

12. An information processing method for generating road safety data, comprising: The road safety data includes road link identification information, time zone information, and road safety information related to road safety; the road safety data is associated with road network data for route search via the identification information of the road link; the road safety information includes information indicating an evaluation value of brightness of the surrounding environment of the road, The information processing method includes: acquiring image data including an image taken by a camera mounted on a vehicle traveling on the road and the time of the image taking; acquiring location data including a location of the vehicle and a location acquisition time of the vehicle; determining the brightness estimate corresponding to the image using a brightness estimation model configured to estimate the brightness estimate from the image; determining an identity of the road link corresponding to the vehicle's location; generating brightness evaluation value data including identification information of the road link, time zone information, and information indicating the brightness evaluation value; generating the road safety data based at least on the brightness evaluation value data; Including, A computer-implemented information processing method.

13. An information processing program that causes a computer to execute the information processing method according to claim 11 or 12.

14. a processor; a memory for storing computer-readable instructions, When the computer-readable instructions are executed by the processor, the information processing device performs the information processing method according to claim 11 or 12. Information processing device.

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