JP7578519B2 - Information processing system, information processing method, and computer program - Google Patents

Description

The present disclosure relates to data processing technology, and in particular to an information processing system, an information processing method, and a computer program.

There is a demand for transportation services for elderly people who have surrendered their driver's licenses, and in the past, local governments operated community buses on fixed routes at set times. However, community buses have the problem of being inefficient, and in order to further increase convenience for residents, there is a growing movement to introduce demand transportation (also called on-demand transportation or demand-type transportation) that allows passengers to get on and off at any location (see, for example, Patent Document 1).

JP 2019-215798 A

Even in areas where demand-responsive transportation has been introduced, its utilization rate is not necessarily high, and in many areas, demand-responsive transportation is in the red. To date, technologies have been proposed for visualizing the current situation in areas where the introduction of demand-responsive transportation is being considered, and for improving the business viability of demand-responsive transportation already in operation (e.g., increasing occupancy rates), but the inventors believe that sufficient technology has not been proposed to support the design of demand-responsive transportation services themselves.

This disclosure was made based on the inventor's recognition of the above problems, and one of its objectives is to provide technology that supports the design of demand-responsive transportation services.

In order to solve the above problem, an information processing system according to one aspect of the present disclosure includes a trip storage unit that stores multiple trip data related to the movement of multiple private cars, the trip data including the departure point, arrival point, and travel distance related to the movement of residents' private cars; a trip extraction unit that extracts multiple trip data from the multiple trip data stored in the trip storage unit, the multiple trip data having both the departure point and the arrival point included in the operation range of the demand transportation; a total distance derivation unit that derives the total travel distance of users that should be achieved by the demand transportation based on the travel distance of the multiple trip data extracted by the trip extraction unit; and a vehicle number derivation unit that derives the number of vehicles that should be operated in the demand transportation by dividing the total travel distance of users by the travel speed of the vehicles operated in the demand transportation.

Another aspect of the present disclosure is an information processing method. In this method, a computer that can access a storage unit that stores a plurality of trip data related to the movement of a plurality of private cars of residents, the trip data including a departure point, an arrival point, and a travel distance, executes the steps of: extracting, from the plurality of trip data stored in the storage unit, a plurality of trip data whose departure point and arrival point are both included in the operation range of the demand transportation; deriving the total travel distance of users that the demand transportation should achieve based on the travel distance of the plurality of trip data extracted in the extracting step; and deriving the number of vehicles that should be operated in the demand transportation by dividing the total travel distance of users by the travel speed of the vehicles operated in the demand transportation.

In addition, any combination of the above components, or any conversion of the expressions of this disclosure between devices, computer programs, recording media storing computer programs, etc., are also valid aspects of this disclosure.

This disclosure can assist in the design of demand-responsive transportation services.

FIG. 1 is a diagram showing a configuration of a demand transportation support system according to an embodiment. FIG. 2 is a block diagram showing functional blocks included in the design support system of FIG. 1 . FIG. 1 is a diagram showing a process of processing by a design support system. FIG. 13 is a diagram illustrating an example of a grid. FIG. 4 is a diagram showing the process of processing by the design support system, following FIG. 3; FIG. 6 is a diagram showing the process of processing by the design support system, following FIG. 5 . FIG. 13 is a diagram illustrating an example of design support information.

The subject of the device or method of the present disclosure includes a computer. The computer executes a computer program to realize the functions of the subject of the device or method of the present disclosure. The computer includes, as its main hardware configuration, a processor that operates according to a computer program. The type of processor is not important as long as it can realize the functions by executing a computer program. The processor is composed of one or more electronic circuits including a semiconductor integrated circuit (IC, LSI, etc.). The computer program is recorded on a non-transitory recording medium such as a computer-readable ROM, optical disk, or hard disk drive. The computer program may be stored in advance on the recording medium, or may be supplied to the recording medium via a wide area communication network including the Internet.

Currently, the design of demand transport services (for example, the operating area and number of vehicles operated) relies on the experience and intuition of government officials. As a result, demand transport services do not necessarily provide sufficient convenience to local residents, and in some areas the utilization rate of demand transport services is low. For example, one of the reasons why demand transport services are not used well despite their introduction is that residents in the target area continue to own their own cars. Unless an appropriate service design is made taking into account the travel history of residents (demand transport service users) by private car, it is inevitable that they will continue to use their private cars for transportation. As a result, residents find it difficult to give up their private cars, and since they own them as fixed assets, it is more rational to use private cars, so there is no incentive to use demand transport services.

In the embodiment, we propose a technology that supports rational design and decision-making regarding demand transportation based on the travel history of local residents. The information processing system of the embodiment derives the total travel distance of all users that demand transportation should achieve based on the travel history of local residents using private cars, and derives the number of vehicles that should be operated in demand transportation based on this total travel distance. This results in a rational number of vehicles in demand transportation.

Figure 1 shows the configuration of a demand transportation support system 10 according to an embodiment. The demand transportation support system 10 includes a plurality of private cars 12 (e.g., private car 12a, private car 12b) driven by a plurality of residents living in an area where the introduction of demand transportation is being considered, and a design support system 16. Each device in Figure 1 transmits and receives data via a communication network 18, which may include a LAN, a WAN, the Internet, etc.

Each of the multiple private vehicles 12 is equipped with a GPS logger 14. The GPS logger 14 detects the current position (latitude and longitude) of the private vehicle 12 using the Global Positioning System (GPS). The GPS logger 14 also detects the power status of the private vehicle 12. The GPS logger 14 periodically transmits movement history data regarding the private vehicle 12, including the current position and power status of the private vehicle 12, to the design support system 16. For example, the GPS logger 14 may transmit one or more pieces of movement history data accumulated in the last five minutes to the GPS logger 14 at five-minute intervals.

The design support system 16 is an information processing system that generates information to support design and decision-making regarding demand transportation based on the movement history data of the private vehicle 12 transmitted from the GPS logger 14 of the private vehicle 12.

Figure 2 is a block diagram showing the functional blocks of the design support system 16 of Figure 1. Each block shown in the block diagram in this specification can be realized in hardware terms by elements such as a computer processor, CPU, and memory, electronic circuits, and mechanical devices, and in software terms by computer programs, etc., but here, functional blocks realized by the cooperation of these are depicted. Therefore, those skilled in the art will understand that these functional blocks can be realized in various ways by combining hardware and software. The functions of the multiple blocks shown in Figure 2 may be implemented in one computer, or may be distributed and implemented across multiple computers.

The design support system 16 includes a control unit 20, a memory unit 22, and a communication unit 24. The control unit 20 executes various data processing operations. The memory unit 22 stores data referenced or updated by the control unit 20. The communication unit 24 communicates with external devices according to a predetermined communication protocol. The control unit 20 transmits and receives data to and from external devices such as the GPS logger 14 of the private vehicle 12 via the communication unit 24.

The memory unit 22 includes a map memory unit 26, a trip memory unit 28, a survey result memory unit 30, an operating range memory unit 32, a public transport hinterland memory unit 34, and an expense and income memory unit 36.

The map storage unit 26 stores map data relating to the area where the introduction of demand-responsive transportation is being considered (hereinafter also referred to as the "target area"). The map data includes latitude information, longitude information, and grid information. In the embodiment, the target area is divided in advance into multiple districts (hereinafter also referred to as "grids") of several hundred meters square. The grid information includes the ID of each of the multiple grids, information indicating the range of each grid (which can also be called the geographic range or the range on the map), and the ID (vehicle ID) of the private vehicle 12 that has each grid as its base (for example, the address of the owner of the private vehicle).

The trip memory unit 28 stores multiple trip data related to the movement of multiple private vehicles 12. The trip data includes the ID (vehicle ID) of the private vehicle 12, the departure date and time, the departure point, the arrival point (also called the destination), the travel distance, the travel time, the grid to which the departure point belongs (the departure grid), and the grid to which the arrival point belongs (the arrival grid).

The survey result storage unit 30 stores the results of various surveys (statistical surveys, questionnaire surveys, etc.) on the target area. In the embodiment, the survey result storage unit 30 stores (1) the number of private cars owned in each grid of the target area, (2) the number of private cars whose movement history (also called trip data) has been collected in each grid of the target area, in other words, the number of private cars equipped with GPS loggers 14 in each grid of the target area, (3) the carpooling rate in demand transportation based on past cases and cases in other places, (4) the allowable detour time for carpooling, and (5) the average speed of vehicles (small buses, etc.) used in demand transportation.

(3) In many cases, the carpooling rate in demand transportation is around 30% to 40%. (4) The allowable detour time for carpooling may be the allowable time for delays caused by detours, obtained as a result of a survey of demand transportation users, and may be, for example, 15 minutes. (5) The average speed of vehicles used in demand transportation is the travel speed including stopping time, and the average travel speed of taxis (for example, 17.5 km/hour) may be used.

The operation range memory unit 32 stores data indicating the operation range (also called the implementation range or service provision range) of the on-demand transportation set by the operation range setting unit 46 described below. The data indicating the operation range of the on-demand transportation includes the IDs of multiple grids included in the operation range.

The public transportation hinterland memory unit 34 stores information indicating the range of the hinterland of public transportation (which can also be considered a geographical range or a range on a map). In the embodiment, public transportation is a fixed-time, fixed-route train or route bus, and is different from on-demand transportation. The hinterland of public transportation can also be considered the commercial area of each public transportation base (station, bus stop, etc.) or the walking distance from each public transportation base, and is the range that users visit by walking, etc. from each public transportation base.

The cost and income storage unit 36 stores the number of vehicles used for on-demand transportation derived by the vehicle number derivation unit 56 described below. The cost and income storage unit 36 also stores the estimated operating costs of on-demand transportation and the estimated operating income of on-demand transportation derived by the cost and income derivation unit 58 described below.

The control unit 20 includes a travel history acquisition unit 40, a trip generation unit 42, an expanded estimation unit 44, an operation range setting unit 46, a trip conversion unit 48, a trip extraction unit 50, a user vehicle identification unit 52, a total distance derivation unit 54, a vehicle number derivation unit 56, an expense and income derivation unit 58, and an information provision unit 60. A computer program in which the functions of these multiple functional blocks are implemented may be stored in a recording medium and installed in the storage of the design support system 16 via the recording medium. Alternatively, the computer program may be downloaded via a communication network and installed in the storage of the design support system 16. The CPU of the design support system 16 may perform the functions of the multiple blocks by reading the computer program into main memory and executing it.

The operation of the design support system 16 having the above configuration will now be described.
3 shows the process of the processing by the design support system 16. The processing of S1 to S5 in the figure is the processing of setting the operation range of the demand-responsive transportation.

The movement history acquisition unit 40 receives movement history data periodically transmitted from multiple GPS loggers 14 of multiple private vehicles 12 (S1). As shown in the output data column, the movement history data is data on a vehicle-by-vehicle basis, and includes the ID (vehicle ID) of the private vehicle 12 that sent the data, the current position (latitude and longitude) of the private vehicle 12, the date and time the current position was detected, and the power supply status of the private vehicle 12.

The trip generation unit 42 generates trip data for each unit of movement (hereinafter also referred to as a "trip") of the private vehicle 12 based on the movement history data acquired by the movement history acquisition unit 40 (S2). A trip is a continuous movement in time from a certain departure point to a certain arrival point.

Specifically, the trip generation unit 42 generates a time-series movement trajectory (including information on stop time and power supply state) based on multiple movement history data including the same vehicle ID. The trip generation unit 42 divides the time-series movement trajectory into multiple trips based on the stop time and power supply state for each vehicle ID. For example, a stop time equal to or greater than a predetermined threshold or an off power supply state may be used as a trip division. The trip generation unit 42 identifies the departure date and time, departure point, arrival point, travel distance, and travel time for each trip. The values of the departure point and arrival point may be expressed as latitude and longitude, or may be place names or location information corresponding to the latitude and longitude and indicated by the map data in the map storage unit 26.

The trip generation unit 42 also refers to the grid information stored in the map storage unit 26 to identify the departure grid that includes the departure point and the arrival grid that includes the arrival point. The trip generation unit 42 generates trip data that includes the vehicle ID, departure date and time, departure point, arrival point, travel distance, travel time, departure grid, and arrival grid, and stores the generated trip data in the trip storage unit 28.

The expanded estimation unit 44, as a weight derivation unit, uses a known expanded estimation method to derive weights for trip data related to private cars of residents living in a certain area based on the ratio of the number of private cars for which trip data has been collected in the area to the total number of private cars in the area (S3). The expanded estimation unit 44 derives weights for each of the multiple trip data stored in the trip memory unit 28. This weight is a value indicating how many private car departures and arrivals a trip indicated by the trip data is counted as. In the embodiment, this weight is called the "expanded number of departures and arrivals."

Specifically, the expanded estimation unit 44 refers to the grid information in the map storage unit 26 and identifies, for each trip data, the grid to which the vehicle ID of the trip data is associated. Here, the trip data to be processed is referred to as "target trip data". The expanded estimation unit 44 refers to the data in the survey result storage unit 30 and, for the grid to which the vehicle ID of the target trip data is associated, calculates the ratio of the number B of private cars for which trip data has been collected in the grid to the number A of private cars owned in the grid. The expanded estimation unit 44 derives the expanded number of departures and arrivals for the target trip data based on the ratio of B to A. For example, when A/B=100/5, the expanded number of departures and arrivals for the target trip data is set to "20". The expanded estimation unit 44 adds the value of the expanded number of departures and arrivals to the target trip data stored in the trip storage unit 28.

The operation range setting unit 46 sums up the number of times the trip corresponds to the departure point (departure grid) of the multiple trip data stored in the trip storage unit 28 and the number of times the trip corresponds to the arrival point (arrival grid) of the multiple trip data for each of the multiple grids previously determined in the target area, and derives the sum as the total number of departures and arrivals (S4). The expanded number of departures and arrivals of the trip data is added to the number of times the trip corresponds to the departure point and the number of times the trip corresponds to the arrival point.

The operation range setting unit 46 sets a set of grids with a relatively large total number of departures and arrivals among multiple grids predetermined in the target area as the operation range of demand traffic (S5). Specifically, the operation range setting unit 46 derives the ratio of each grid (a value between 0 and 100) when the total number of departures and arrivals of the grid with the largest total number of departures and arrivals is set to 100. In the embodiment, the operation range setting unit 46 sets three operation range models. The three operation range models include a minimum operation range α1, an intermediate operation range α2, and a wide area operation range α3.

The operation range setting unit 46 extracts grids with a ratio of 90 or more as grids of the minimum operation range α1. The operation range setting unit 46 extracts grids with a ratio of 70 or more as grids of the intermediate operation range α2. The operation range setting unit 46 extracts grids with a ratio of 50 or more as grids of the wide area operation range α3. The operation range setting unit 46 stores information on the grid groups included in the minimum operation range α1 (e.g., the ID of each grid), information on the grid groups included in the intermediate operation range α2, and information on the grid groups included in the wide area operation range α3 in the operation range storage unit 32. The number of grids included in each operation range is minimum operation range α1 < intermediate operation range α2 < wide area operation range α3.

Figure 4 shows an example of a grid. As mentioned above, the target area is divided into multiple grids. In Figure 4, it is divided into 110 grids indicated by 1A, 1B, ... 10J, 10K. In Figure 4, the operating range 70 is indicated by shading, and the operating range 70 shown in Figure 4 includes 20 grids from 3D to 6H. The operating range 70 shown in Figure 4 is, for example, the minimum operating range α1.

Figure 5 shows the process of processing by the design support system 16, following Figure 3. The processes of S6 to S9 in the figure are processes for converting trips that fall outside the operation range of on-demand transportation into trips within the operation range of on-demand transportation, taking into account transfers to public transportation. Furthermore, the processes of S6 to S9 in the figure are executed for each operation range set in S5.

The process shown in FIG. 5 is briefly described below. When the departure point indicated by certain trip data is within the operation range of demand transportation, the arrival point indicated by the trip data is outside the operation range of demand transportation, and the arrival point can be reached by transferring from demand transportation to public transportation at a public transportation facility within the operation range of the demand transportation, the trip conversion unit 48 sets demand travel information indicating the distance from the departure point to the public transportation facility as the travel distance by demand transportation, and a conversion flag in the trip data.

The process shown in FIG. 5 will be described in detail. In the embodiment, a map is used to extract in advance public transportation facilities (e.g., stations and bus stops) that can be reached with one transfer from the operation range of demand transportation. A predetermined range (e.g., a radius of several hundred meters) from the public facility is set as the hinterland of public transportation, and information indicating the range of the hinterland is stored in advance in the public transportation hinterland storage unit 34. In the example of FIG. 4, the operation range 70 includes a railway line 72 and a station 74. It is possible to travel to station 74 by demand transportation, and then travel from station 74 to station 76 using the railway line 72. Therefore, information on the hinterland of station 76 is stored in the public transportation hinterland storage unit 34.

The trip conversion unit 48 extracts conversion candidate trip data from among the multiple trip data stored in the trip memory unit 28 (S6). The conversion candidate trip data is trip data whose departure point is within the operation range of the demand transportation stored in the operation range memory unit 32 and whose arrival point is within the hinterland of the public transportation stored in the public transportation hinterland memory unit 34.

The trip conversion unit 48 refers to the map data in the map storage unit 26 to identify the location (e.g., latitude and longitude) of a transfer point to public transportation that exists within the operation range of the demand transportation (S7). A transfer point to public transportation is a public transportation facility, such as a station or bus stop. In the example of FIG. 4, the trip conversion unit 48 identifies station 74 within the operation range 70 as a transfer point.

The trip conversion unit 48 estimates the travel time to the destination when transferring between demand transportation and public transportation (hereinafter also referred to as the "transfer travel time") and the travel distance on demand transportation (hereinafter also referred to as the "demand travel distance") (S8). The transfer travel time is the travel time when traveling from the departure point indicated by the conversion candidate trip data to a public transportation facility using demand transportation, and then traveling from that facility to the destination indicated by the conversion candidate trip data using public transportation.

Specifically, the trip conversion unit 48 measures the travel distance A and travel time B when traveling from the departure point indicated by the conversion candidate trip data to the transfer point identified in S7 using demand transportation, using a web service (which can also be called a web API) such as an existing search engine. The travel distance A calculated here becomes the demand travel distance.

The trip conversion unit 48 also measures travel time C from the transfer point identified in S7 to a public transportation facility in the hinterland of the public transportation, including the destination, based on the public transportation timetable information (in other words, operation schedule information). The public transportation timetable information may be stored in the memory unit 22, or may be obtained from a website that publishes timetable information (such as the website of the public transportation). The trip conversion unit 48 estimates the transfer travel time as the sum of travel time B, travel time C, and a predetermined average waiting time (e.g., 10 minutes). The trip conversion unit 48 adds the transfer travel time, transfer point, and demand travel distance to the conversion candidate trip data.

When the trip conversion unit 48 identifies multiple transfer points to public transportation that exist within the operation range of the demand transportation in S7, it estimates the transfer travel time and demand travel distance for each transfer point in the conversion candidate trip data. The trip conversion unit 48 compares the transfer travel time for each transfer point and identifies the shortest transfer travel time. The trip conversion unit 48 adds the shortest transfer travel time and the transfer point and demand travel distance corresponding to the shortest transfer travel time to the conversion candidate trip data.

As described above, the conversion candidate trip data records the travel time when traveling by private car from the departure point to the destination. The trip conversion unit 48 compares the travel time by private car recorded in the conversion candidate trip data with the transfer travel time, and if the difference between the two is within a predetermined threshold (e.g., within 30 minutes), adds a conversion flag to the conversion candidate trip data (S9). This threshold may be determined based on the developer's knowledge, survey results, etc. The conversion candidate trip data to which the conversion flag has been added is hereinafter also referred to as "conversion trip data." The conversion trip data indicates a trip in which part of the travel route from the departure point to the destination is converted to travel using demand transportation.

Figure 6 shows the process of processing by the design support system 16, following Figure 5. The processes of S10 to S13 in the figure are executed for each operating range set in S5. Here, the process for one operating range is explained.

The process shown in FIG. 6 will be outlined. The trip extraction unit 50 extracts, from the multiple trip data stored in the trip memory unit 28, multiple trip data in which both the departure point and the destination are within the operation range of the demand transportation. The total distance derivation unit 54 derives the total travel distance of users that should be achieved by the demand transportation based on the travel distance of the multiple trip data extracted by the trip extraction unit 50. The vehicle number derivation unit 56 derives the number of vehicles that should be operated in the demand transportation by dividing the derived total travel distance of users by the travel speed of the vehicles operated in the demand transportation.

The process shown in FIG. 6 will be described in detail. The trip extraction unit 50, the user vehicle identification unit 52, and the total distance derivation unit 54 compile multiple trip data (including diversion trip data) stored in the trip storage unit 28 (S10). The total distance derivation unit 54 derives the total travel distance of all users that the demand transportation should achieve (hereinafter also referred to as the "demand total distance") based on the compilation result of S10, the carpooling rate that occurs in the demand transportation, and the detour distance that occurs due to carpooling (S11).

Specifically, the trip extraction unit 50 extracts, from the multiple trip data stored in the trip memory unit 28, multiple trip data in which both the departure point and the arrival point are included in the operation range of the demand transportation. In addition, the trip extraction unit 50 further extracts, from the multiple trip data stored in the trip memory unit 28, trip data in which a conversion flag is set (i.e., converted trip data).

The user vehicle identification unit 52 derives the total number of trip data stored in the trip storage unit 28 for each vehicle ID. The user vehicle identification unit 52 also derives the number of trip data (including conversion trip data) extracted by the trip extraction unit 50 for each vehicle ID. The user vehicle identification unit 52 derives the ratio of the number of trip data extracted by the trip extraction unit 50 to the total number of trip data for each vehicle ID. The user vehicle identification unit 52 identifies a vehicle ID for which the ratio is equal to or greater than a predetermined threshold as an ID of a private vehicle (hereinafter also referred to as a "user vehicle ID") owned by a user (or potential user) of demand transportation. An appropriate value for this threshold may be determined based on the knowledge of the developer of the design support system 16, experiments using the design support system 16, or the like. For example, this threshold may be a value of 70% to 80%.

In S10, the total distance derivation unit 54 tally up, by time period, the expanded number of departures and arrivals and the travel distance indicated by the trip data extracted by the trip extraction unit 50 and including the user vehicle ID (hereinafter also referred to as "trip data to be tallied"). The time period that is the unit of tallying may be in one-hour intervals. In addition, the total distance derivation unit 54 determines the time period to which the trip data to be tallied belongs based on the departure date and time indicated by the trip data to be tallied.

Here, the tabulated result of the travel distance is called the "total travel distance." The total distance derivation unit 54 adds the product of the expanded number of departures and arrivals and the travel distance for each of the multiple trip data to be tabulated to the total travel distance. For example, when tabulating a first trip data to be tabulated in which the expanded number of departures and arrivals is A and the travel distance is X, and a second trip data to be tabulated in which the expanded number of departures and arrivals is B and the travel distance is Y, the total distance derivation unit 54 generates a tabulated result in which the expanded number of departures and arrivals is (A+B) and the total travel distance is (A×X+B×Y). In this way, the total distance derivation unit 54 reflects the weight of each of the multiple trip data to be tabulated (i.e., the expanded number of departures and arrivals) in the travel distance of the entire users (i.e., the total travel distance).

In S10, if the trip data to be counted includes a conversion flag, i.e., if the trip data to be counted is conversion trip data, the total distance derivation unit 54 counts the demand travel distance included in the trip data. Specifically, the product of the expanded number of departures and arrivals and the demand travel distance is added to the total travel distance. As described above, the demand travel distance indicates the distance over which demand transportation is used between the departure point and the arrival point. As a result of S10, the total distance derivation unit 54 obtains data for each time period, including the expanded number of departures and arrivals and the total travel distance.

Demand transportation may involve multiple people sharing a trip, which may result in a detour (i.e., the route from the departure point to the destination may differ from the shortest route). For this reason, the carpooling rate is set based on past cases and cases in other regions. When sharing a trip, the person may pick up other people along the way, or may drop off other people first, resulting in a detour that is longer than traveling by private car. For this reason, the total travel distance taking detours into account is derived as the demand total distance. Note that it is difficult to calculate the time required for a detour itself, so in the embodiment, the detour allowable time (in other words, the delay allowable time) of the demand transportation user is used as an index value for the time required for a detour.

Specifically, in S11, for each time period, the total distance derivation unit 54 derives the product of (1) the ride-sharing rate stored in the survey result storage unit 30, (2) the allowable detour time, (3) the average speed of vehicles used in demand traffic, and (4) the expanded number of departures and arrivals tallied in S10 as the total detour distance. The product of (2) and (3) can be said to be the distance of the detour caused by ride-sharing. The total distance derivation unit 54 sums up the total travel distance and the total detour distance for each time period, and derives the sum as the demand total distance. As a result of S11, the total distance derivation unit 54 adds the demand total distance for each time period to the data for each time period.

The vehicle number derivation unit 56 divides the demand total distance for each time period by the average speed (e.g., 17.5 km/hour) of the vehicles used in demand traffic stored in the survey result storage unit 30. The vehicle number derivation unit 56 rounds up the result of the division to derive the number of vehicles to be operated in demand traffic for each time period. The vehicle number derivation unit 56 determines the maximum number of vehicles among the number of vehicles in each time period within a predetermined operation time period of demand traffic (e.g., 7:00 to 20:00) as the number of vehicles to be operated in demand traffic (S12). This is to accommodate peak demand. The total distance derivation unit 54 outputs data including the number of vehicles in demand traffic as the result of S12.

The cost and income derivation unit 58 derives the operating costs and operating income of the demand-responsive transportation (S13). Specifically, as a cost derivation unit, the cost and income derivation unit 58 calculates the sum of the vehicle operating costs (variable costs) proportional to the number of vehicles for each operating range and the fixed management costs not proportional to the number of vehicles, and derives the total value as the operating costs. The vehicle operating costs may be derived by multiplying a predetermined operating cost amount per vehicle by the number of vehicles. The fixed management costs may be determined in advance according to the labor costs, prices, etc. of the target area.

The expense and income derivation unit 58, as an income derivation unit, sums up the expanded number of departures and arrivals (equivalent to twice the number of users as these are departures and arrivals) for each time period during the operation time, and divides the total by two to derive the number of users during the operation time period. The vehicle number derivation unit 56 derives the product of the number of users during the operation time period and a predetermined fare (e.g., 300 yen/person) as the operation income. The expense and income derivation unit 58 stores data including the number of vehicles of demand traffic, the amount of operation expenses, and the amount of operation income in the expense and income storage unit 36 as the result of S13.

As mentioned above, the above processing is performed for each operation range. That is, the cost and income storage unit 36 stores data for each operation range, including the number of vehicles for demand traffic, the amount of operation costs, and the amount of operation income.

In response to a request from an external device, the information providing unit 60 transmits design support information based on data resulting from processing by the design support system 16 to the external system. The design support information includes operation range data of on-demand transportation stored in the operation range memory unit 32. The operation range data may be, for example, an image showing the operation range 70 on a map divided into multiple grids, as shown in FIG. 4. The design support information also includes data showing the number of vehicles, operation costs, and operation revenues of on-demand transportation for each operation range, which are stored in the expense and income memory unit 36.

Figure 7 shows an example of design support information. The horizontal axis of the design support information in this figure is the operation range of demand-responsive transportation (minimum operation range α1, intermediate operation range α2, wide-area operation range α3). The vertical axis is the amount of operation costs and operation revenue. The design support information in Figure 7 is intended to show the discrepancy between the operation costs shown by the dashed line and the operation revenue shown by the solid line. Looking at the design support information in Figure 7, an administrative official can easily grasp that (1) the minimum operation range α1 is too narrow to provide sufficient transportation services and has few users, (2) the wide-area operation range α3 is too wide and inefficient, and (3) the intermediate operation range α2 has a good balance between operation costs and operation revenue.

The design support system 16 of the embodiment can estimate the reasonable operating range and number of vehicles for on-demand transportation based on the travel history of residents in the area where the introduction of on-demand transportation is being considered, and can also accurately estimate the operating costs and operating revenues. This can effectively support government officials and others in determining the scale of on-demand transportation and its business feasibility.

The present disclosure has been described above based on examples. The contents described in the examples are merely illustrative, and it will be understood by those skilled in the art that various modifications are possible in the combination of components and processing processes of the examples, and that such modifications are also within the scope of the present disclosure.

In the above embodiment, it has been described that the diversion trip data is set from among the trip data in which the departure point is within the operation range of the demand transport service and the arrival point is in the public transport hinterland outside the operation range of the demand transport service. As a variant, the diversion trip data may be further set from among the trip data in which the departure point is in the public transport hinterland outside the operation range of the demand transport service and the arrival point is within the operation range of the demand transport service.

In this modified example, if the departure point indicated by certain trip data is outside the operation range of demand transportation, the arrival point indicated by the trip data is within the operation range of demand transportation, and it is possible to travel to the arrival point by transferring from public transportation to demand transportation at a public transportation facility within the operation range of the demand transportation, the trip conversion unit 48 sets a demand travel distance indicating the distance from the public transportation facility to the arrival point and a conversion flag in the trip data.

Specifically, the trip conversion unit 48 extracts conversion candidate trip data from among the multiple trip data stored in the trip memory unit 28. The conversion candidate trip data here is trip data whose departure point is within the hinterland of the public transportation stored in the public transportation hinterland memory unit 34 and whose arrival point is within the operation range of the demand transportation stored in the operation range memory unit 32.

The trip conversion unit 48 refers to the map data in the map memory unit 26 and identifies the location (e.g., latitude and longitude) of a transfer point from public transportation that exists within the operation range of the demand transportation. The trip conversion unit 48 estimates the travel time to the destination when transferring from public transportation to demand transportation (transfer travel time) and the travel distance on the demand transportation (demand travel distance). The transfer travel time is the travel time when traveling from the departure point indicated by the conversion candidate trip data to a public transportation facility serving as a transfer point using public transportation, and then traveling from that facility to the destination indicated by the conversion candidate trip data using demand transportation.

Specifically, the trip conversion unit 48 measures travel time A from a public transportation facility in the hinterland of the public transportation, including the departure point indicated by the conversion candidate trip data, to the previously identified transfer point, based on the timetable information of the public transportation facility (in other words, operation schedule information). In addition, the trip conversion unit 48 measures travel time B and travel distance C when traveling from the transfer point to the arrival point using demand transportation, using a web service such as an existing search engine. The travel distance C calculated here becomes the demand travel distance.

The trip conversion unit 48 estimates the connecting travel time as the sum of travel time A, travel time B, and a predetermined average waiting time (e.g., 10 minutes). The trip conversion unit 48 adds the connecting travel time, the connecting point, and the demand travel distance to the conversion candidate trip data.

When the trip conversion unit 48 identifies multiple transfer points to public transportation that exist within the operation range of the demand transportation, it estimates the transfer travel time and demand travel distance for each transfer point in the conversion candidate trip data. The trip conversion unit 48 compares the transfer travel time for each transfer point and identifies the shortest transfer travel time. The trip conversion unit 48 adds the shortest transfer travel time and the transfer point and demand travel distance corresponding to the shortest transfer travel time to the conversion candidate trip data.

As described above, the conversion candidate trip data records the travel time when traveling by private car from the departure point to the destination. The trip conversion unit 48 compares the travel time by private car recorded in the conversion candidate trip data with the transfer travel time, and if the difference between the two is within a predetermined threshold (e.g., within 30 minutes), adds a conversion flag to the conversion candidate trip data. The conversion candidate trip data to which the conversion flag has been added in this modified example is treated in the same way as the conversion trip data of the embodiment in the processing from S10 in FIG. 6 of the embodiment onwards.

The configuration of this modified example allows for a more reasonable value to be obtained as the total travel distance that should be achieved by on-demand transportation, taking into account whether or not there is a shift from travel by private car to travel by on-demand transportation and public transportation.

Any combination of the above-mentioned examples and modifications is also useful as an embodiment of the present disclosure. A new embodiment resulting from the combination will have the combined effects of each of the examples and modifications. It will also be understood by those skilled in the art that the functions to be performed by each of the constituent elements recited in the claims can be realized by each of the constituent elements shown in the examples and modifications alone or in combination with each other.

The techniques described in the embodiments and modifications may be specified by the following items.
[Item 1]
A trip storage unit that stores a plurality of trip data related to a plurality of private car trips of a resident, the trip data including a departure point, an arrival point, and a travel distance related to the resident's private car trips;
a trip extraction unit that extracts, from the plurality of trip data stored in the trip storage unit, a plurality of trip data in which both a departure point and a destination point are included in an operation range of the demand-responsive transportation;
a total distance deriving unit that derives a total travel distance of all users that should be achieved by the demand-responsive transportation based on travel distances of the plurality of trip data extracted by the trip extracting unit;
a vehicle number deriving unit that derives the number of vehicles to be operated in the on-demand transportation by dividing the travel distance of all the users by the travel speed of the vehicles operated in the on-demand transportation;
An information processing system comprising:
This information processing system calculates the total travel distance that demand-responsive transportation should achieve for all users based on the travel records of residents' private cars, and calculates the number of vehicles that should be operated in demand-responsive transportation based on that total travel distance. This makes it possible to derive an appropriate number of vehicles for demand-responsive transportation, and supports rational design and decision-making regarding demand-responsive transportation.
[Item 2]
A vehicle identification unit is further provided,
The trip data further includes a vehicle ID.
the vehicle identification unit derives a ratio of the trip data extracted by the trip extraction unit to a total number of trip data for each of the vehicle IDs, and identifies a vehicle ID for which the ratio is equal to or greater than a predetermined threshold as an ID of a target vehicle;
the total distance deriving unit derives a travel distance of the entire user by summing up travel distances of the trip data extracted by the trip extracting unit and including the ID of the target vehicle;
Item 2. An information processing system according to item 1.
This information processing system identifies the vehicles of users of demand-responsive transportation as target vehicles, and derives the total travel distance of all users based on the trip data of the target vehicles. This makes it possible to obtain a more appropriate value for the total travel distance of all users that demand-responsive transportation should achieve.
[Item 3]
a weight derivation unit that derives weights of trip data related to private cars of residents living in a certain area based on a ratio of the number of private cars for which trip data has been collected in the certain area to the total number of private cars in the certain area;
The total distance derivation unit reflects a weight of each of the plurality of trip data on a total travel distance of the users.
3. The information processing system according to item 1 or 2.
According to this information processing system, the method of extended estimation makes it possible to obtain a more reasonable value for the total travel distance of users that should be achieved through demand-responsive transportation.
[Item 4]
The total distance derivation unit reflects a predetermined ride-sharing rate in the demand traffic and a detour distance caused by the ride-sharing in the demand total distance.
4. The information processing system according to any one of items 1 to 3.
According to this information processing system, by taking into account the carpooling that occurs in demand-responsive transportation, a more reasonable value can be obtained as the travel distance of all users that should be realized by demand-responsive transportation.
[Item 5]
The vehicle number deriving unit further includes a cost deriving unit that derives an operating cost of the on-demand transportation based on the number of vehicles to be operated by the on-demand transportation derived by the vehicle number deriving unit.
5. An information processing system according to any one of items 1 to 4.
According to this information processing system, it is possible to derive a reasonable operating cost based on the number of vehicles in demand traffic.
[Item 6]
a trip conversion unit that sets a demand travel distance indicating a distance from the departure point to the public transportation facility and a conversion flag in the trip data when the departure point indicated by the trip data is within the operation range of the demand transportation, the arrival point indicated by the trip data is outside the operation range of the demand transportation, and the arrival point can be reached by transferring to the public transportation at a public transportation facility within the operation range of the demand transportation,
The trip extraction unit further extracts trip data for which the conversion flag is set,
The total distance derivation unit derives a travel distance of the entire users by using the demand travel distance for the trip data in which the conversion flag is set.
6. An information processing system according to any one of items 1 to 5.
According to this information processing system, by taking into account the travel distance of demand-responsive transportation when switching from demand-responsive transportation to public transportation, a more reasonable value can be obtained as the travel distance of all users that should be realized by demand-responsive transportation.
[Item 7]
The trip data further includes a first travel time when traveling from a departure point to a destination point by private vehicle;
the trip conversion unit derives a second travel time when traveling from the departure point to the public transportation facility using the demand transportation and traveling from the facility to the arrival point using the public transportation, and sets the conversion flag in the trip data if a difference between the first travel time and the second travel time is within a predetermined threshold value;
Item 7. An information processing system according to item 6.
According to this information processing system, by taking into account whether or not there is a shift from traveling by private car to traveling by demand-responsive transportation and public transportation, it is possible to obtain a more appropriate value for the total travel distance that should be achieved by demand-responsive transportation.
[Item 8]
An operation range setting unit is further provided,
The area that may be the target of the demand-responsive traffic is divided into a plurality of districts,
the operation range setting unit derives a total value of the number of times each of the plurality of districts corresponds to the departure point of the trip data and the number of times each of the plurality of districts corresponds to the arrival point of the trip data, and sets a set of districts among the plurality of districts in which the total value is relatively large as the operation range of the demand transportation;
8. An information processing system according to any one of items 1 to 7.
According to this information processing system, the operation area of demand-responsive transportation can be set to an appropriate range.
[Item 9]
a computer having access to a memory unit storing a plurality of trip data relating to a plurality of personal vehicle trips of a resident, the trip data including a starting point, a destination point, and a distance traveled, the trip data relating to a plurality of personal vehicle trips of the resident;
extracting, from the plurality of trip data stored in the storage unit, a plurality of trip data whose departure point and destination are both included in the operation range of the demand-responsive transportation;
A step of deriving a travel distance of all users that should be achieved by the demand-responsive transportation based on the travel distances of the plurality of trip data extracted in the extracting step;
deriving the number of vehicles to be operated in the on-demand transportation by dividing the travel distance of all the users by the travel speed of the vehicles operated in the on-demand transportation;
An information processing method for performing the above.
According to this information processing method, the total travel distance that the demand-responsive transportation should achieve is calculated based on the travel records of residents' private cars, and the number of vehicles that should be operated in the demand-responsive transportation is calculated based on the total travel distance. This makes it possible to derive an appropriate number of vehicles in the demand-responsive transportation, and supports rational design and decision-making regarding the demand-responsive transportation.
[Item 10]
A computer having access to a memory unit that stores a plurality of trip data relating to a plurality of personal vehicle trips of a resident, the trip data including a starting point, a destination point, and a distance traveled, the trip data relating to a plurality of personal vehicle trips of a resident.
A function of extracting, from the plurality of trip data stored in the storage unit, a plurality of trip data whose departure point and destination are both included in the operation range of the demand-responsive transportation;
A function of deriving a travel distance of all users that should be achieved by the demand-responsive transportation based on the travel distances of the plurality of trip data extracted in the extracting step;
A function of deriving the number of vehicles to be operated in the demand transportation by dividing the travel distance of all the users by the travel speed of the vehicles operated in the demand transportation;
A computer program to achieve this.
This computer program calculates the total travel distance that demand-responsive transportation should achieve for all users based on the travel records of residents' private cars, and calculates the number of vehicles that should be operated in demand-responsive transportation based on that total travel distance. This makes it possible to derive an appropriate number of vehicles for demand-responsive transportation, and supports rational design and decision-making regarding demand-responsive transportation.

10 Demand traffic support system, 12 Private vehicle, 28 Trip storage unit, 44 Expanded estimation unit, 46 Operation range setting unit, 48 Trip conversion unit, 50 Trip extraction unit, 52 User vehicle identification unit, 54 Total distance derivation unit, 56 Vehicle number derivation unit.

Claims (10)

A trip storage unit that stores a plurality of trip data related to a plurality of private car trips of a resident, the trip data including a departure point, an arrival point, and a travel distance related to the resident's private car trips;
a trip extraction unit that extracts, from the plurality of trip data stored in the trip storage unit, a plurality of trip data in which both a departure point and a destination point are included in an operation range of the demand-responsive transportation;
a total distance deriving unit that derives a total travel distance of all users that should be achieved by the demand-responsive transportation based on travel distances of the plurality of trip data extracted by the trip extracting unit;
a vehicle number deriving unit that derives the number of vehicles to be operated in the on-demand transportation by dividing the travel distance of all the users by the travel speed of the vehicles operated in the on-demand transportation;
An information processing system comprising: A vehicle identification unit is further provided,
The trip data further includes a vehicle ID.
the vehicle identification unit derives a ratio of the trip data extracted by the trip extraction unit to a total number of trip data for each of the vehicle IDs, and identifies a vehicle ID for which the ratio is equal to or greater than a predetermined threshold as an ID of a target vehicle;
the total distance deriving unit derives a travel distance of the entire user by summing up travel distances of the trip data extracted by the trip extracting unit and including the ID of the target vehicle;
The information processing system according to claim 1 . a weight derivation unit that derives weights of trip data related to private cars of residents living in a certain area based on a ratio of the number of private cars for which trip data has been collected in the certain area to the total number of private cars in the certain area;
The total distance derivation unit reflects a weight of each of the plurality of trip data on a total travel distance of the users.
3. The information processing system according to claim 1 or 2. The total distance derivation unit reflects a predetermined ride-sharing rate in the demand-responsive transportation and a detour distance caused by ride-sharing in the travel distance of the entire users.
4. An information processing system according to claim 1. The vehicle number deriving unit further includes a cost deriving unit that derives an operating cost of the on-demand transportation based on the number of vehicles to be operated by the on-demand transportation derived by the vehicle number deriving unit.
5. An information processing system according to claim 1. a trip conversion unit that sets a demand travel distance indicating a distance from the departure point to the public transportation facility and a conversion flag in the trip data when the departure point indicated by the trip data is within the operation range of the demand transportation, the arrival point indicated by the trip data is outside the operation range of the demand transportation, and the arrival point can be reached by transferring to the public transportation at a public transportation facility within the operation range of the demand transportation,
The trip extraction unit further extracts trip data for which the conversion flag is set,
The total distance derivation unit derives a travel distance of the entire users by using the demand travel distance for the trip data in which the conversion flag is set.
6. An information processing system according to claim 1. The trip data further includes a first travel time when traveling from a departure point to a destination point by private vehicle;
the trip conversion unit derives a second travel time when traveling from the departure point to the public transportation facility using the demand transportation and traveling from the facility to the arrival point using the public transportation, and sets the conversion flag in the trip data if a difference between the first travel time and the second travel time is within a predetermined threshold value;
7. The information processing system according to claim 6. An operation range setting unit is further provided,
The area that may be the target of the demand-responsive traffic is divided into a plurality of districts,
the operation range setting unit derives a total value of the number of times each of the plurality of districts corresponds to the departure point of the trip data and the number of times each of the plurality of districts corresponds to the arrival point of the trip data, and sets a set of districts among the plurality of districts in which the total value is relatively large as the operation range of the demand transportation;
8. An information processing system according to claim 1. a computer having access to a memory unit storing a plurality of trip data relating to a plurality of personal vehicle trips of a resident, the trip data including a starting point, a destination point, and a distance traveled, the trip data relating to a plurality of personal vehicle trips of the resident;
extracting, from the plurality of trip data stored in the storage unit, a plurality of trip data whose departure point and destination are both included in the operation range of the demand-responsive transportation;
A step of deriving a travel distance of all users that should be achieved by the demand-responsive transportation based on the travel distances of the plurality of trip data extracted in the extracting step;
deriving the number of vehicles to be operated in the on-demand transportation by dividing the travel distance of all the users by the travel speed of the vehicles operated in the on-demand transportation;
An information processing method for performing the above. A computer having access to a memory unit that stores a plurality of trip data relating to a plurality of personal vehicle trips of a resident, the trip data including a starting point, a destination point, and a distance traveled, the trip data relating to a plurality of personal vehicle trips of a resident.
A function of extracting, from the plurality of trip data stored in the storage unit, a plurality of trip data whose departure point and destination are both included in the operation range of the demand-responsive transportation;
A function of deriving a travel distance of all users that should be achieved by the demand-responsive transportation based on the travel distances of the plurality of trip data extracted in the extracting step;
A function of deriving the number of vehicles to be operated in the demand transportation by dividing the travel distance of all the users by the travel speed of the vehicles operated in the demand transportation;
A computer program to achieve this.

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