Vehicle dispatch management system

Abstract

By efficiently deploying vehicles while considering the variance of prediction error values, we can improve the vehicle utilization rate by balancing the supply and demand of vehicles within the service area. [Solution] The dispatch management system 1 comprises a vehicle V, a dispatch management device 10 for managing the placement of the vehicle V, and a data management device 2. The data management device 2 stores the demand measurement value of the vehicle V, the demand forecast value indicating the future demand for the vehicle V, and the forecast error value. The dispatch management device 10 comprises controllers 34, 44 that perform the following processes: calculating the demand forecast value and error variance for each placement point a; identifying placement points a within a predetermined range as a group of placement points; grouping each placement point a in the group of placement points and classifying them into a plurality of placement destination groups; determining the level of demand for the vehicle V among the plurality of placement destination groups based on the demand forecast value, and prioritizing placement points in the high-demand group where the demand is relatively high to place one vehicle V.

Description

Technical Field

[0001] The present disclosure relates to a vehicle allocation management system for vehicles (service vehicles) used for user mobility services (mobility services).

Background Art

[0002] Mobility services are positioned as services that smoothly provide user mobility and luggage transportation by vehicles. In mobility services, in order to increase the operating rate of vehicles while improving the convenience of users (passengers), it is required to predict the demand for vehicles for each location (placement location) where the vehicles are placed and create an efficient placement plan.

[0003] For example, Patent Document 1 below discloses a method of updating a predicted value based on measured values for various past periods to be predicted. Specifically, the occurrence probability of a prediction error value in a prediction period is calculated based on data showing the time transition of the error (prediction error value) between the measured value and the predicted value in the past period, and the predicted value is corrected by the predicted error value estimated from the calculated occurrence probability, thereby improving the prediction accuracy.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] The technology disclosed in Patent Document 1 above proposes correcting predicted values ​​using prediction error values. However, since this technology does not take into account the variance (dispersion) of prediction error values, even if the accuracy of the average value of the predicted values ​​improves, there is a risk that the actual values ​​(measured values) and the predicted values ​​will deviate from each other at the measurement level. For example, if the variance of the prediction error values ​​is large, the accuracy of the predicted values ​​corrected with the prediction error values ​​will decrease. Therefore, if a vehicle deployment plan is created without considering the variance of prediction error values, an oversupply or undersupply of vehicles at deployment locations can easily occur, worsening the balance of supply and demand for vehicles and potentially reducing the utilization rate of vehicles.

[0006] Therefore, this disclosure aims to provide a technology that can efficiently allocate vehicles while taking into account the variance of prediction error values, thereby improving the balance of supply and demand for vehicles within the service area of ​​mobility services and increasing the utilization rate of vehicles. [Means for solving the problem]

[0007] A dispatch management system according to one aspect of the present disclosure comprises a vehicle for providing mobility services, a dispatch management device for managing the placement of the vehicle at a designated location within a predetermined area, and a storage device, wherein the storage device stores a demand measurement value indicating the demand for the vehicle measured at each location within the area, a demand forecast value indicating the future demand for the vehicle at each location, and a forecast error value indicating the error between the demand measurement value and the demand forecast value at each location; the dispatch management device performs the following processes for each location within the area: calculating the demand forecast value based on the demand measurement value and the error variance indicating the variance of the forecast error value over a predetermined past period; identifying a group of location locations within a predetermined range from the location of one of the vehicles; grouping each location location within the group of location locations based on the error variance and classifying them into a plurality of destination groups; determining the level of demand for the vehicle among the plurality of destination groups based on the demand forecast value, and prioritizing the placement of the vehicle at locations within the high-demand group where the demand is relatively high. [Effects of the Invention]

[0008] According to this disclosure, it becomes possible to efficiently allocate vehicles while taking into account the variance of prediction error values, thereby improving the balance of supply and demand for vehicles within the area where mobility services are provided and increasing the utilization rate of vehicles. [Brief explanation of the drawing]

[0009] [Figure 1A] This figure illustrates an example of a vehicle dispatch management system according to one embodiment of the present disclosure. [Figure 1B] This is a schematic diagram illustrating the locations of vehicles used for the ride-hailing service. [Figure 2] Figure 1 is a block diagram showing an example of the hardware and functional configuration of the data management device in the dispatch management system. [Figure 3A] This diagram illustrates an example of location classification in sample data. [Figure 3B] This figure illustrates an example of a demand factor in sample data. [Figure 4A] Figure 1 is a block diagram showing an example of the hardware and functional configuration of a demand forecasting device in the dispatch management system. [Figure 4B] This is a conceptual diagram illustrating an example of error time series data used in demand forecasting. [Figure 5A] Figure 1 is a block diagram showing an example of the hardware and functional configuration of the dispatch management device in the dispatch management system. [Figure 5B] This is a conceptual diagram illustrating the grouping of vehicle placement locations. [Figure 6] This flowchart shows an example of a dispatch management method using a dispatch management system according to one embodiment of this disclosure. [Figure 7] This flowchart shows an example of grouping of dispatch locations by a dispatch management system according to one embodiment of the present disclosure. [Figure 8] This flowchart shows an example of the evaluation process for a destination group by a dispatch management system according to one embodiment of this disclosure. [Modes for carrying out the invention]

[0010] Embodiments of the present invention will be described below with reference to the drawings. Note that the drawings are schematic and may differ from actual ones. Furthermore, the embodiments of the present invention described below are illustrative examples of devices and methods for realizing the technical concept of the present invention, and the technical concept of the present invention is not limited to the structure, arrangement, etc., of the components described below. The technical concept of the present invention can be modified in various ways within the technical scope defined by the claims described in the patent claims.

[0011] (System Configuration) Figure 1A is a diagram illustrating an example of a dispatch management system according to one embodiment of the present disclosure. Figure 1B is a schematic diagram illustrating a service area, which is the area in which the dispatch service is provided. The dispatch management system 1 is a system that provides a dispatch service for dispatching vehicles V to be used for the user's travel. The dispatch management system 1 includes a data management device (an example of a storage device) 2, a demand forecasting device 3, a dispatch planning device 4, and a vehicle V, which are interconnected via a communication network 9 to enable communication with each other. The demand forecasting device 3 and the dispatch planning device 4 together constitute a dispatch management device 10 that manages the dispatch of vehicle V. The dispatch management system 1 also includes a factor data collection device 5 and a demand measurement device 6, which are connected to the data management device 2 via a communication network 9 so that they can communicate with each other.

[0012] Service area A is a geographical area that includes one or more locations where vehicles V, which are made available for user use, are stationed. For example, service area A is a geographical area that includes multiple locations a, and can be identified as having a core center around places of high user frequency and interest (POIs (Points of Interest)), such as airports, train stations, and large-scale public facilities. As will be described in detail later, the vehicle allocation management device 10 (demand prediction device 3, vehicle allocation planning device 4) manages the allocation of the vehicle V to the allocation point a within the service area A (an example of a predetermined area). For example, when there is a vehicle V in an empty state (a state where the user has already alighted and there is no vehicle reservation) at the position P within the service area A, the vehicle allocation management device 10 allocates the vehicle V from the position P to an allocation point a within a predetermined range based on predetermined conditions. In the present disclosure, although an example where the vehicle allocation management system 1 is adapted to a mobility service for moving passengers and transporting luggage by the vehicle V will be described, the service use is not limited to this. The vehicle V may be at least a vehicle used for providing a mobility service. Further, the allocation point may be a waiting place where the vehicle V waits before the vehicle is allocated to a predetermined boarding place where the user boards, or after the user alights at a predetermined alighting place, or may be a boarding / alighting place where the vehicle V can stop at multiple locations.

[0013] The data management device 2 is a computing device that manages data used for vehicle allocation management in the vehicle allocation management device 10. The data management device 2 functions as a storage device that comprehensively stores and holds data used for vehicle allocation management in the vehicle allocation management system 1. As will be described in detail later, in this example, the data management device 2 stores sample data used for demand prediction of the vehicle V in the service area A, vehicle data and allocation point data used for creating an allocation plan of the vehicle V to each allocation point a. The sample data is used to calculate a demand prediction value for predicting the future demand of the vehicle V in a predetermined time period at each allocation point a in the service area A. Further, the vehicle data is data related to the vehicle V used for the movement of the user, and the allocation point data is data related to each allocation point a within the service area A. The sample data includes a measured value of the past demand of the vehicle V (demand measurement value) in one service area (service area A) and one or more types of demand factor data that affect the demand. That is, in the present embodiment, the data management device 2 stores a demand measurement value indicating the demand of the vehicle V measured at each allocation point where the vehicle V is allocated within one service area.

[0014] The demand for the vehicle V may be the number of dispatch requests (request count) from users who wish to board near each placement location, or the dispatch result (number of dispatched vehicles) to users at each placement location. The demand factor data is data related to the characteristics of the placement location (location factor data) and data related to the characteristics for each period division at the placement location (period division factor data). Here, the period division indicates the division of date and time based on predetermined temporal conditions (season, day of the week, time zone, etc.). The sample data is transmitted from other devices to the data management device 2 via the communication network 9. For example, among the sample data, the demand factor data is transmitted from the factor data collection device 5, and the measured values of the past demand for the vehicle V in the service area A are transmitted from the demand measurement device 6. In addition, the data management device 2 performs data search and transmission of the search result to other devices in response to a data acquisition request from the demand prediction device 3.

[0015] The demand prediction device 3 performs demand prediction for the vehicle V in a predetermined time period in the future at each placement location a in the service area A, which is one service area, using at least the sample data stored in the data management device 2. Specifically, the demand prediction device 3 calculates a demand prediction value, which is a value predicted for the demand for the vehicle V in the future predetermined time period, based on the demand measurement values in the sample data. When executing demand prediction, the demand prediction device 3 transmits a request for acquiring sample data (for example, demand measurement values) to the data management device 2 via the communication network 9 for use in demand prediction. Also, the demand prediction device 3 can correct the calculated demand prediction value based on the error (prediction error value) between the past demand prediction value and the measured value. Thereby, the accuracy of demand prediction can be improved. Also, the demand prediction device 3 calculates the variance (error variance) of the prediction error values at each placement location a. Details of the calculation of the demand prediction value, correction, and error variance will be described later. In addition, the demand prediction device 3 transmits the demand prediction value and the error variance as the demand prediction result to the vehicle allocation planning device 4 via the communication network 9.

[0016] The dispatch planning device 4 creates a dispatch plan for operating vehicle V from a designated pick-up location to a designated drop-off location within a service area (in this example, service area A) based on a dispatch request from a user (received dispatch request information). The dispatch planning device 4 also creates a vehicle placement plan for each placement point a within the service area (service area A) based on the demand forecasting results (demand forecast value and error variance) transmitted from the demand forecasting device 3. This allows for efficient placement of vehicle V at each placement point in the service area, taking into account the variance of the forecast error value, thereby improving the supply-demand balance of vehicle V within the service area and increasing the utilization rate. The placement plan may include vehicle data for vehicle V and placement point data indicating the placement point a. The dispatch planning device 4 instructs vehicle V to move to each placement point based on the placement plan via the communication network 9. The dispatch planning device 4 transmits dispatch request information to the demand measurement device 6. The dispatch planning device 4 is also capable of sending and receiving data with the vehicle V, and may receive updated data from the vehicle V and transmit the received updated data to the data management device 2 via the communication network 9. The updated data may include the current location and current status of the vehicle V. The updated data may also be a dispatch plan or deployment plan created by the dispatch planning device 4.

[0017] The factor data collection device 5 periodically measures the above-mentioned demand factor data (location factor data, period classification factor data) at predetermined time intervals and transmits it to the data management device 2 via the communication network 9. The demand measurement device 6 periodically measures the demand for vehicles V in a service area (service area A) at predetermined unit time intervals (e.g., 15 minutes) and transmits demand measurement data indicating the measured demand value to the data management device 2 via the communication network 9. Specifically, the demand measurement device 6 measures the number of requests per unit time at each location in a service area based on the dispatch request information transmitted from the dispatch planning device 4. As will be explained in more detail later, the dispatch request information includes at least the boarding location where the user boards the vehicle V and the date and time (reception date and time) when the dispatch planning device 4 received the dispatch request from the user. The demand measurement device 6 identifies the boarding location based on the dispatch request information and identifies the unit time in which the dispatch request was made from the reception date and time. As a result, the demand measurement device 6 measures the number of requests per unit time (number of dispatch requests) for each boarding location as a demand measurement value. In this embodiment, each deployment point is associated with a predetermined area of ​​responsibility, and the demand measurement value of the deployment point corresponding to the area of ​​responsibility is identified by summing the demand measurement values ​​of each boarding location located within that area of ​​responsibility.

[0018] (Data management device configuration) Next, using Figure 2, the data management device 2 in the dispatch management system 1 will be explained. The data management device 2 receives demand factor data, demand measurement data, and other-region data from the factor data collection device 5 and the demand measurement device 6, respectively, and stores them as sample data. In addition to sample data, the data management device 2 also stores vehicle data and location data. Then, in response to a data acquisition request, it transmits this data to the demand forecasting device 3 and the vehicle dispatch planning device 4. The data management device 2 consists of an input device 21, an output device 22, a communication device 23, and a controller 24. The data management device 2 may be, for example, an information processing device such as a personal computer or a server computer.

[0019] The input device 21 is a keyboard or mouse, and the output device 22 is a display or printer. The communication device 33 is equipped with a NIC (Network Interface Card) for connecting to a wireless LAN or a wired LAN. The data management device 2 sends and receives data to and from each device via the communication device 33. The controller 24 is an electronic control unit (ECU) that controls the operation of the data management device 2. The controller 24 comprises a processor 24a and a storage device 24b.

[0020] The processor 24a may be, for example, a CPU (Central Processing Unit) or an MPU (Micro-Processing Unit). The storage device 24b may include memory such as ROM (Read Only Memory) and RAM (Random Access Memory) used as main memory, as well as non-transitory tangible storage media such as memory registers and cache memory. The storage device 24b stores a database which functions as a data storage module 241. The data storage module 241 holds sample data 241a, vehicle data 241b, and location data 241c. The sample data 241a consists of demand factor data (location factor data, period segmentation factor data) transmitted from the factor data collection device 5 and demand measurement data transmitted from the demand measurement device 6. The demand measurement data includes the demand measurement value at each location in service area A and the measurement time of said demand measurement value. The measurement time only needs to include at least the start time or end time of measurement, and may include both. The demand measurement value is measured at regular intervals (e.g., 15 minutes) as described above. For this reason, the demand measurement data, including the demand measurement value, may be stored in the data storage module 241 for each unit time.

[0021] Here, we will explain the details of sample data 241a using Figures 3A and 3B. As described above, sample data 241a includes demand factor data transmitted from factor data collection device 5 and demand measurement data transmitted from demand measurement device 6. For example, sample data 241a may be data in which demand measurement data (demand measurement value, measurement time) is linked to factor classifications (location classification and period classification) for linking with demand factors. The location classification identifies each deployment location within service area A where demand measurement data was collected, and is used to associate location factor data with demand measurement data. As shown in Figure 3A, each deployment location a (for example, locations a1 to a8) is associated with a location ID (A001 to A008), and by assigning a location ID to the transmitted demand measurement data, the deployment location where the demand measurement data was collected can be identified.

[0022] Furthermore, as mentioned above, period divisions are divisions of time and date based on seasonal conditions (season, day of the week, time of day, etc.), and are used to associate period division factor data with the unit time on which demand measurement data was measured. For example, period divisions can be broadly divided into three categories: season, day of the week, and time of day. For example, among the category categories, "season" is divided into four categories: "spring, summer, autumn, winter," "day of the week" is divided into two categories: "weekdays, holidays," and "time of day" is divided into eight categories: "pre-dawn, early morning, morning, before noon, noon, afternoon, evening, night." Each time of day is, for example, divided into 3-hour intervals from 0:00 to the next 0:00 24 hours later. Period divisions may be identified by a combination of category IDs that uniquely represent each item of each category.

[0023] As shown in Figure 3B, among the demand factor data, location factor data may include, for example, population, POI clusters indicating the density of POIs in the vicinity of the location, and data indicating the convenience of public transportation (transportation convenience). These are data collected and aggregated at predetermined intervals for each location a in a service area (service area A). Note that location factor data is not limited to these, and may include various factor data indicating location characteristics that affect demand forecasting, such as the degree of gradient, bridges, wide roads, etc. Location factor data is stored in the data storage module 241, linked to location IDs. Furthermore, demand measurement data can be associated with location factor data using location IDs. This allows the demand forecasting device 3 to identify demand measurement data linked to specific location factor data when forecasting demand for vehicles V.

[0024] Furthermore, among the demand factor data, the period classification factor data may include, for example, climate data such as temperature and weather, data indicating the frequency of public transportation operations, and data indicating the degree of congestion of public transportation. However, the period classification factor data is not limited to these and may include various factor data that change depending on seasonal conditions and affect demand forecasting. The period segmentation factor data is data collected and aggregated for each period segment at each location. Therefore, in the data storage module 241, the period segmentation factor data is stored linked to the location ID and the period segment ID. The data storage module 241 stores sample data 241a. In other words, the sample data 241a is not overwritten, and includes past demand factor data and demand measurement data. Note that the structure of the sample data 241a shown in Figures 3A and 3B is just one example, and the structure of the sample data in this disclosure is not limited to this; any structure that allows for the correspondence between demand measurement data and demand factor data (location factor data, period segmentation factor data) is acceptable.

[0025] Returning to Figure 2, the vehicle data 241b is data for vehicle V used for mobility services within service area A. Vehicle data 241b includes, for example, data on vehicle ID, vehicle attributes (vehicle registration number, vehicle type, and maximum passenger capacity / maximum load capacity, manned or unmanned type, etc.), vehicle performance, other specifications, current dispatch plan, current location, and current status (service status, battery remaining capacity (driving range), and number of passengers, etc.). Vehicle data 241b may be used, for example, to create a dispatch plan for vehicle V in the dispatch planning device 4. Furthermore, the deployment location data 241c is data for each deployment location a within service area A. The deployment location data 241c includes data such as the deployment location ID, location, the number of vehicles V (deployed vehicles) deployed, and the maximum number of vehicles deployed. The deployment location data 241c may be used, for example, to create a vehicle deployment plan in the vehicle dispatch planning device 4. Vehicle data 241b and deployment location data 241c may be updated based on update data transmitted from the vehicle dispatch planning device 4.

[0026] The storage device 24b stores a program for implementing the data management module 242. The processor 24a executes this program, thereby realizing the functions of the data management device 2. The data management module 242 adds and extracts sample data 241a to the data storage module 241. Specifically, the data management module 242 adds new sample data 241a to the data storage module 241 based on the reception of demand factor data and demand measurement data, and extracts specific data from the sample data 241a based on a sample data acquisition request and transmits it to the demand forecasting device 3. Furthermore, the data management module 242 extracts specific data from vehicle data 241b based on a vehicle data acquisition request and transmits it to the dispatch planning device 4, and extracts specific data from location data 241c based on a location data acquisition request and transmits it to the dispatch planning device 4. Furthermore, the data management module 242 updates the vehicle data 241b and the location data 241c based on the update data received from the dispatch planning device 4.

[0027] (Configuration of a demand forecasting system) Next, the demand forecasting device 3 in the dispatch management system 1 will be explained using Figure 4. The demand forecasting device 3 acquires specific demand measurement data from the sample data 241a from the data management device 2 and calculates the demand forecast value for each vehicle V at each location in a service area (service area A) based on the demand measurement data. The calculated demand forecast value is transmitted to the dispatch planning device 4 and the data management device 2. The demand forecasting device 3 consists of an input device 31, an output device 32, a communication device 33, and a controller 34. The demand forecasting device 3 may be an information processing device such as a personal computer or a server computer. The configuration of the input device 31, output device 32, and communication device 33 is equivalent to that of the input device 21, output device 22, and communication device 23 in the data management device 2, so a detailed explanation is omitted.

[0028] The controller 34 is an electronic control unit (ECU) that controls the operation of the demand forecasting device 3. The controller 34 comprises a processor 34a and a storage device 34b. The processor 34a has the same configuration as the processor 24a in the data management device 2, so a detailed explanation is omitted. The storage device 34b has basically the same configuration as the storage device 24b in the data management device 2, but the programs it stores are different. The storage device 34b stores programs for implementing control modules such as the prediction value calculation module 341 and the error calculation module 342. The functions of the demand forecasting device 3 are realized when these programs are executed by the processor 34a.

[0029] The forecast value calculation module 341 calculates the forecast demand for vehicles V at each location a in service area A based on demand measurement data and the like stored in the data management device 2. For example, the forecast value calculation module 341 may calculate the forecast demand for the next unit time (7:15 to 7:30) at the start of a new unit time in which demand measurement values ​​are taken (for example, 7:00).

[0030] The forecast value calculation module 341 calculates a demand forecast value based on demand measurement values ​​for each location a within service area A, based on sample data 241a stored in the storage device 24b of the data management device 2, for example. Specifically, the forecast value calculation module 341 may calculate the latest demand forecast value based on past demand measurement data and demand factor data (location factor data, period division factor data) of the location a to be predicted from the sample data 241a. For example, the forecast value calculation module 341 may extract sample data 241a corresponding to the location a to be predicted and the period division to which the unit time of the prediction belongs from the data management device 2, and calculate the demand forecast value based on the extracted sample data 241a. The forecast value calculation module 341 may include a demand forecasting model that has been machine-learned according to a predetermined machine learning algorithm, for example, using the acquired sample data 241a (demand measurement data, demand factor data) as explanatory variables and the demand forecast value (e.g., the number of requests per unit time) as the dependent variable.

[0031] The forecast value calculation module 341 transmits forecast value data, including the calculated demand forecast value, to the dispatch planning device 4 via the communication device 33. The forecast value data may include the demand forecast value, the location of the vehicle to be forecasted, and the start time of the unit time of the vehicle to be forecasted. This allows the dispatch planning device 4 to create a dispatch plan for the vacant vehicle V based on the latest demand forecast value. In this embodiment, the forecast value calculation module 341 may transmit forecast value data to the dispatch planning device 4, which includes at least the forecast demand values ​​for multiple deployment locations a indicated by the deployment location group data received from the dispatch planning device 4. As will be described in detail later, the deployment location group data is data that identifies multiple deployment locations a that can be the deployment destination for a single vehicle V. The forecast value calculation module 341 may also transmit the forecast demand values ​​for all deployment locations a within the service area A to the dispatch planning device 4. Furthermore, the forecast value calculation module 341 may transmit forecast value data, including all calculated demand forecast values, to the data management device 2. The data management module 242 of the data management device 2 stores the received forecast value data in the data storage module 241 at unit time intervals. This allows the data management device 2 to maintain a correspondence between demand forecast values ​​and demand measurement data. In other words, the storage device 24b of the data management device 2 stores forecast error values, which indicate the error between the actually measured demand measurement values ​​and the demand forecast values ​​at each location a in a single district (service district A). Note that the forecast value data and forecast error values ​​may also be stored in a database separate from the data storage module 241.

[0032] As described above, in this embodiment, demand forecast values ​​are calculated based on sample data 241a. However, due to various factors, it may become difficult to forecast demand based on demand factor data and past demand measurements at the target location, and the calculated demand forecast values ​​may deviate from the actual demand measurements. In this case, the dispatch efficiency will be insufficient, and user convenience will be reduced.

[0033] Therefore, in this embodiment, the forecast value calculation module 341 may correct the latest demand forecast value calculated as described above using error time series data that shows the forecast error values ​​for a predetermined period in the past at the location of the target of forecasting. In other words, the demand forecast value included in the forecast value data may be the corrected value.

[0034] Here, an example of error time series data will be explained with reference to Figure 4B. Error time series data is data that shows the error between measured demand values ​​and forecast demand values ​​measured over a predetermined period in the past, in time series. The predetermined period in the past is, for example, the period including the previous unit time "t-1" (the immediately preceding unit time) when the current unit time is "t". If one unit time (for example, 15 minutes) is considered "1 unit", then the predetermined period in the past can be the past period of n units including the unit time "t-1", that is, a continuous period from unit time "t-1" to unit time "tn" (15 minutes × n). In other words, error time series data is data that shows the forecast error values ​​over multiple (n units) unit times including the immediately preceding unit time in time series. Furthermore, the time period to be predicted may be, for example, the current unit time "t", or for example, the next unit time "t+1" (not shown). In other words, the prediction calculation module 341 may calculate the demand forecast value for the target location a at the start of a unit time (current unit time t), or it may calculate the demand forecast value for the next unit time (next unit time t+1) before the start of a unit time. The forecast value calculation module 341 calculates the average value of the forecast error values ​​for a predetermined past period included in the error time series data, and adds this average value to the latest demand forecast value to perform the above correction. For example, if the average value to be added is a positive number, the latest demand forecast value will be higher by the average value than the calculated latest demand forecast value, and if it is a negative number, the latest demand forecast value will be lower by the average value. The forecast value calculation module 341 transmits the corrected demand forecast values ​​to the vehicle dispatch planning device 4, as described above. This makes it possible to create an efficient vehicle allocation plan based on the corrected demand forecast values.

[0035] The forecast value calculation module 341 may also derive the mode of the forecast error values ​​for a predetermined past period included in the error time series data, and add this mode to the latest demand forecast value as the above correction. In other words, after calculating the demand forecast value for each location a, the forecast value calculation module 341 may correct the demand forecast value with a representative value (mean or mode) of the forecast error values ​​(error time series data) for a predetermined past period.

[0036] The error calculation module 342 calculates the error variance, which shows the variance of the predicted error values ​​over a predetermined past period. The error variance is calculated using the predicted error values ​​included in the error time series data. The error calculation module 342 may obtain the average value of the predicted error values ​​included in the error time series data calculated by the predicted value calculation module 341, and calculate the error variance based on this average value. For example, the error calculation module 342 may calculate the error variance using the following equation (1). Note that in equation (1), the error variance is "S 2 ", and the prediction error values ​​from unit time "t-1" to unit time "tn" are each "x 1、 x2···x n Let "n" be the number of data points for the prediction error values, and "x" be the average value of the prediction error values.

number

[0037] The error calculation module 342 may also calculate the error variance from the distribution of multiple predicted error values ​​(predicted error values ​​included in the error time series data). The error calculation module 342 may output the calculated error variance to the forecast value calculation module 341. The forecast value calculation module 341 may transmit the forecast value data for each of the above-mentioned placement points a, the forecast error value (error time series data), and the error variance for each placement point a to the vehicle dispatch planning device 4 as demand forecast results. The error calculation module 342 may calculate the error variance for placement point a indicated by the placement point group data, or it may calculate the error variance for all placement points a in service area A.

[0038] (Configuration of the vehicle dispatch planning system) Next, the dispatch planning device 4 in the dispatch management system 1 will be described using Figures 5A and 5B. The dispatch planning device 4 obtains forecast data including the forecasted demand value and the error variance showing the variance of the forecast error value as the demand forecast result for each location a from the demand forecasting device 3. Based on the demand forecast result and the error variance, it creates a dispatch plan that includes the location a to which the vehicle V, which is currently empty, will be placed, and then places the vehicle V to the specific location a.

[0039] Furthermore, the dispatch planning device 4 creates a dispatch plan for vehicle V based on dispatch requests from users who wish to ride in vehicle V, and dispatches vehicle V from each deployment point a to the user's desired boarding location. The dispatch planning device 4 may also transmit dispatch request information, which associates the dispatch request with the user's boarding location (boarding point), to the demand measurement device 6. This allows the demand measurement device 6 to aggregate the number of requests per unit time (number of dispatch requests) for each deployment point a that includes the boarding location in its service area as a demand measurement value. However, it is not limited to this, and the demand measurement device 6 may also extract dispatch request information and dispatch plan information stored in the storage device 44b of the dispatch planning device 4 (described later), and measure the number of requests per unit time for each deployment point a based on these.

[0040] The dispatch planning device 4 consists of an input device 41, an output device 42, a communication device 43, and a controller 44. The dispatch planning device 4 may be an information processing device such as a personal computer or a server computer. The configuration of the input device 41, output device 42, and communication device 43 is equivalent to that of the input device 21, output device 22, and communication device 23 in the data management device 2, so a detailed explanation is omitted.

[0041] The controller 44 is an electronic control unit (ECU) that controls the operation of the dispatch planning device 4. The controller 44 comprises a processor 44a and a storage device 44b. The processor 44a has the same configuration as the processor 24a in the data management device 2, so a detailed explanation is omitted. The storage device 44b has basically the same configuration as the storage device 24b in the data management device 2, but the programs it stores are different. The storage device 44b stores programs for implementing control modules such as the vehicle management module 441, the destination determination module 442, and the plan creation module 443. The functions of the vehicle dispatch planning device 4 are realized when the processor 44a executes these programs.

[0042] The vehicle management module 441 manages the status of vehicle V. For example, when the vehicle management module 441 receives identification information (vehicle ID), location information indicating the current location, and data indicating the service status from vehicle V, it may transmit this data as update data to the data management device 2 via the communication device 43. As a result, the vehicle data 241b and the location data 241c are updated in the data management device. The data indicating the service status indicates whether or not vehicle V is in service (a user is riding in it). Furthermore, if a vehicle V is in an empty state, the vehicle management module 441 determines that it is necessary to determine a waiting location (deployment location a) for the vehicle V to wait, and outputs information identifying the vehicle V (e.g., "vehicle ID") to the deployment location determination module 442, requesting that the deployment location be determined. For example, the vehicle management module 441 may determine whether each vehicle V is empty or not based on data indicating the service status of each vehicle V received from each vehicle V, a dispatch plan, etc. If there are multiple empty vehicle Vs, the vehicle management module 441 may sequentially output the vehicle IDs of the multiple vehicle Vs to the deployment location determination module 442, requesting that the deployment location be determined. In addition, the vehicle management module 441 may store the location information received from the vehicle V in a predetermined storage area of ​​the storage device 44b during the status management of the vehicle V, and output this location information to the deployment location determination module 442 when requesting that the deployment location be determined.

[0043] The placement determination module 442 acquires vehicle data for a vehicle V from the data management device 2 based on the vehicle information (e.g., vehicle ID) output from the vehicle management module 441. Based on the acquired vehicle data, the placement determination module 442 acquires the demand forecast results (demand forecast value and error variance) for each placement location a from the demand forecasting device 3 and determines the placement location a to which the vehicle V will be placed. The following describes in detail the processing performed by the placement determination module 442.

[0044] <Identification of deployment locations> The destination determination module 442 obtains the vehicle data of a vehicle V from the vehicle data 241b stored in the data management device 2 based on the vehicle ID of a vehicle V output from the vehicle management module 441. The destination determination module 442 may send the vehicle ID of a vehicle V to the data management device 2 as a request to obtain vehicle data, and obtain the vehicle data of a vehicle V (at least the current location information of a vehicle V) from the data management device 2. The placement location determination module 442 identifies placement locations a within a predetermined range from the current position of a vehicle V as a group of placement locations, and obtains the demand forecast value, forecast error value (error time series data), and error variance for each placement location a included in the group of placement locations from the demand forecasting device 3 as demand forecast results. The group of placement locations represents multiple placement locations a that are candidates for the placement of a vehicle V. For example, the placement determination module 442 designates an area within a predetermined range from the current position of a vehicle V within service area A as the placement area, and extracts data for placement points a located within the placement area from the data management device 2. The placement determination module 442 may send location information indicating the placement area to the data management device 2 as a request to acquire placement point data, and acquire placement point data (data of a group of placement points) related to placement points a included in the placement area from the data management device 2. The above placement area may be an area within a predetermined distance from the current position of a vehicle V, or an area that can be reached within a predetermined time from the current position of a vehicle V. In other words, the above placement area may be an area that can be reached within a predetermined range with a travel cost (distance or time).

[0045] The placement location determination module 442 may send data of the group of placement locations extracted from the data management device 2 to the demand forecasting device 3 as a forecast result request, and obtain demand forecast results from the demand forecasting device 3, including forecast value data (demand forecast values), error time series data, and error variance for multiple placement locations a included in the group of placement locations. The destination determination module 442 groups each destination location a in the group of destination locations based on error variance and classifies them into multiple destination groups, and then determines a destination from among the destination locations a in one of the multiple destination groups. This allows for efficient vehicle placement while considering the variance of prediction error values. Grouping can be performed using known methods such as cluster analysis (e.g., hierarchical clustering). The destination determination module 442 may include a grouping model that performs clustering processing according to a predetermined machine learning algorithm. The generation of destination groups will be described in detail below.

[0046] <Identification of reference points> The placement determination module 442 identifies a placement point a, which will be the center (cluster nucleus) of a placement group (cluster), as a reference point, and generates a placement group for each of these reference points. The characteristics of the placement group are determined by the characteristics of the reference point (error variance, demand forecast value). The placement determination module 442 may identify multiple placement locations a whose error variance is below a predetermined threshold as reference points for multiple placement groups. By using placement locations a whose error variance is below a predetermined threshold as reference points, it is possible to generate placement groups in which the overall error variance is suppressed. Preferably, the placement determination module 442 identifies multiple placement locations a whose error variance is below a predetermined threshold and whose demand forecast value is relatively large as reference points. This makes it possible to generate placement groups in which the overall error variance is suppressed and which are centered around placement locations a where the demand for vehicles V is predicted to be large. The grouping of location point a included in the location point group will be explained in detail below with reference to Figure 5B.

[0047] In the example shown in Figure 5B, the deployment area A1 within service area A is set within a predetermined range (e.g., within a predetermined distance) from the position P of a vehicle V, and includes multiple deployment points a that constitute a group of deployment points. One of the deployment points a within deployment area A1 becomes the deployment destination for a vehicle V. The placement determination module 442 may divide the placement area A1 into subdivided areas A11, A12, A13, and A14 with an area less than or equal to a predetermined area. Subdivided areas A11 to A14 may have equivalent areas. The placement determination module 442 may divide the placement area A1 within a single service area (e.g., service area A) into subdivided areas of a predetermined size (default area) or less, and identify one or more placement points a as reference points for each subdivided area. This makes it possible to generate well-balanced placement groups within the placement area A1.

[0048] The placement location determination module 442 may extract (primary extraction) multiple placement locations a within the placement area A1 (placement locations a in the group of placement locations) whose error variance is below a predetermined threshold. By performing primary extraction, placement locations a with relatively low error variance (small discrepancy between the forecasted demand value and the measured demand value (actual value)) can be selected as candidates for reference locations. Furthermore, the placement location determination module 442 may extract multiple placement locations a from the multiple placement locations a extracted in the primary extraction, in descending order from the location with the largest demand forecast value (secondary extraction), and identify them as reference locations. In the secondary extraction, one or more placement locations a may be identified as reference locations in each divided area (divided areas A11, A12, A13, A14). This allows for the further extraction (secondary extraction) of multiple locations from the initially extracted location a that have relatively large demand forecast values ​​in each divided area, and their identification as reference locations.

[0049] For example, the placement location determination module 442 may use only the placement location a with the highest demand forecast value within each divided area, that is, the first placement location a to be selected in each divided area, as the reference point. Furthermore, the placement location determination module 442 may continue identifying reference points, including in divided areas where reference points have already been identified, until at least one placement location a is identified as a reference point (selected in the second half) in all divided areas (divided areas A11 to A14). In other words, the number of reference points in each divided area may be one or more. In this example, one reference point (reference points a1, a2, a3, a4) is identified in each of the divided regions A11 to A14. Reference points a1, a2, a3, a4 are the locations a in each of the divided regions A11, A12, A13, and A14 where the error variance is less than or equal to a predetermined value, and the location a has the largest demand forecast value. Multiple reference points (reference points a1 to a4) identified within a location region (location region A1) are called a "reference point group".

[0050] <Identifying potential members> The placement determination module 442 identifies multiple reference points (reference point clusters) and, for each reference point, identifies multiple placement points a that are candidates for members of a placement group. The candidate members of a placement group consist of multiple placement points a other than the reference points. In other words, the placement points a that are candidates for members of each placement group are placement points excluding the reference points included in the reference point cluster. The placement points a that are candidates for members only need to be within a predetermined range from each reference point (within a predetermined distance or time range), and may be placement points a within the divided region to which each reference point belongs. Placement points a from other divided regions may also be included as candidate members.

[0051] The placement determination module 442 identifies candidate members for each reference point (reference points a1, a2, a3, a4), and then generates a placement group with each central (core) reference point and the candidate member. Since each placement point a that is a candidate member is not yet confirmed as a member of the placement group, the placement group containing the candidate member is sometimes referred to as the "initial group". For example, the placement location determination module 442 identifies multiple placement locations a (excluding other reference locations a2~a4) that are within a predetermined range (within a predetermined distance or time) from one reference location a1 within the divided area A11 as member candidates from the reference location group, and generates a placement location group (initial group) with these member candidates and reference location a1. Member candidates for the placement location group with reference location a1 as the core may be placement locations a other than reference location a1 within the divided area A11 (indicated by white circles) and placement locations a on the upper side of the divided area A12 (towards the divided area A11). The initial group may be generated in the same manner for the other reference locations a2~a4 in the reference value point group.

[0052] <Adjusting potential members> As described above, the placement determination module 442 generates a placement group (initial group) as a combination of a single reference point (e.g., reference point a1) and multiple placement points a (member candidates) within a predetermined range from the single reference point among the placement points a (group of placement points) in the placement area A1. In the initial group, it adjusts the member candidates so that the error variance is below a predetermined threshold. The threshold for error variance may be set according to the scale of demand. The scale of demand for vehicles V (e.g., the sum of the forecasted demand values ​​of placement points a included in the initial group) may differ among the initial groups, and the error variance tends to increase as the scale of demand increases. By setting the threshold according to the scale of demand, the member candidates can be adjusted appropriately for each initial group. The adjusted combination (placement group) after adjusting the member candidates for the initial group is sometimes called an "adjusted group". The adjustment of member candidates is performed for each initial group. The initial group that is subject to adjustment of member candidates is called the "own group".

[0053] The placement determination module 442 determines whether or not to add each member candidate (each placement point a within a predetermined range from a single reference point) to the adjustment group based on the error variance of each member candidate in the initial group (its own group). This allows for the selection of member candidates in the initial group that can be adjusted so that the error variance of the adjustment group is below a predetermined threshold. For example, the error variance of the adjustment group is calculated using the sum of the predicted error values ​​(considering positive and negative) for each unit time of the error time series data of placement point a in the adjustment group, and the average value of the predicted error values ​​for each unit time, using a formula similar to the method for calculating the error variance of placement point a alone (for example, formula (1) above). For example, the placement determination module 442 may add member candidates (placement location a) that satisfy the predetermined joining conditions (an example of the first condition) from among the placement location a (member candidate) that constitute the initial group (its own group) to the adjustment group. The placement determination module 442 determines whether each member candidate of the initial group (its own group) satisfies the above joining conditions and generates an adjustment group. As a result, member candidates that satisfy the joining conditions are extracted in each initial group, and multiple adjustment groups are generated in the placement area A1 based on each initial group. Details of the joining conditions are as follows.

[0054] The placement determination module 442 calculates the error variance (A) of the adjustment group, which includes all member candidates from the initial group (its own group), and the error variance (B) of the adjustment group, which includes the remaining member candidates after excluding one member candidate (placement location a) from the initial group. By comparing error variance (A) and error variance (B), it is possible to estimate the change in error variance when one member candidate is not included and when one member candidate is included. The above joining condition is that the error variance in the adjustment group changes to a single state upon the joining of one member candidate (placement point a) in the initial group.

[0055] The error variance (A) may be calculated based on the predicted error values ​​of all placement points a (reference point and all candidate members) included in the initial group (our group). In other words, the error variance (A) may be the error variance calculated by applying the sum and mean values ​​of the predicted error values ​​(considering positive and negative) for each unit time in the error time series data of all placement points within our group to the above formula (1). Furthermore, the error variance (B) may be the error variance calculated by applying the sum and mean values ​​of the predicted error values ​​(considering positive and negative) for each unit time in the error time series data of the remaining placement points a after removing one member candidate (placement point a) from all placement points a included in the initial group (the group), to the above formula (1). The error variance (B) is calculated for each member candidate in the group. For example, if the group consists of 5 member candidates (placement points a) and a reference point (e.g., reference point a1), then 5 patterns of error variance (B) are calculated for the remaining placement points a after removing each of the 5 placement points a.

[0056] The placement determination module 442 determines that if error variance (A) is smaller than error variance (B) (error variance (A) < error variance (B)), the addition of one member candidate (placement point a) will reduce the error variance of the adjustment group (the state described above). In this case, the placement determination module 442 determines that the above addition condition is met and may add the member candidate to the adjustment group. This makes it possible to configure the adjustment group with member candidates that can reduce the error variance of the adjustment group.

[0057] <Exceptions for candidate members> As described above, the placement determination module 442 extracts member candidates that meet the default joining conditions in the initial group and generates adjustment groups. However, it is possible that some member candidates may be missed from joining the adjustment groups. For example, a member candidate who does not meet the joining conditions may not be joined to any placement group (adjustment group). Therefore, the placement determination module 442 adds any member candidates who do not meet the joining conditions (error variance (A) < error variance (B)) in the initial group (its own group) but who do meet specific conditions, to the adjustment group. This helps to prevent any member candidates from being missed from joining the adjustment group.

[0058] Specifically, the placement determination module 442 may determine whether there are other initial groups to which a candidate member belongs if one of the initial group members does not meet the above joining conditions, and based on the result of this determination, determine whether the above specific conditions are met. The placement determination module 442 may determine that there are other initial groups to which the candidate member (placement point a) belongs if there are multiple reference points within a predetermined range from the candidate member (placement point a). If there are no other initial groups, the placement determination module 442 may add the member candidate who does not meet the above joining conditions to the adjustment group based on the initial group (its own group). This prevents the member candidate who belongs only to its own group from being omitted from the adjustment group. On the other hand, if there are other initial groups, the placement determination module 442 calculates the error variance (A) of the adjusted combination (adjusted group) of the other initial groups, and if the error variance (A) based on its own group is smaller than the error variance (A) based on the other initial groups, it may add the member candidate to the adjusted group based on its own initial group (its own group). In other words, the member candidate may be added to the adjusted group based on its own group and the other initial groups, which is estimated to have a smaller error variance (A) when the member candidate is added. This prevents member candidates who do not meet the above-mentioned joining conditions from being left out of the adjusted group, and also suppresses the impact that member candidates who do not meet the joining conditions have on the error variance (increase in error variance (A)) of the adjusted group.

[0059] <Evaluation of placement groups> The placement determination module 442 generates placement groups (adjusted groups) by adjusting member candidates in the initial group, and then evaluates each adjusted group. This determines the placement locations a that will become members of the placement group. The adjusted group being evaluated is called the "own group". The placement determination module 442 may generate a single placement group (confirmed group) based on the adjusted combination of member candidates (adjusted group) whose error variance can be kept below a predetermined threshold. This makes it possible to efficiently select placement locations a that will become members of the confirmed group, even within a placement area A1 with many placement locations a. Specifically, the placement determination module 442 determines whether the error variance in the adjustment group containing the member candidate (placement location a) that satisfies the above joining conditions is below a predetermined threshold. The placement determination module 442 calculates the error variance (C) of each adjustment group, and if the error variance (C) is not below a predetermined threshold, it excludes the member candidate (placement location a) that satisfies the default exclusion conditions (an example of the second condition) from the adjustment group, thereby generating a placement group (confirmed group) in which the error variance is below a predetermined threshold. In other words, the adjustment group in which the error variance (C) is below a predetermined threshold becomes the placement group (confirmed group). With the generation of the confirmed group, the placement group to which placement location a is classified is determined, and the grouping is completed. The exclusion condition described above is that the error variance (C) in the adjustment group changes to a single state, i.e., a state where it is reduced compared to before the exclusion, due to the exclusion of one candidate member (placement location a).

[0060] The error variance (C) in the adjustment group is the error variance of the adjustment group (the group itself) including all member candidates, and is calculated in the same way as the error variance (A) described above. If the error variance (C) exceeds a predetermined threshold, the placement determination module 442 calculates the error variance (D) of the adjustment group consisting of the remaining member candidates excluding one member candidate (placement location a). Error variance (D) is the error variance of the adjustment group (the group itself) consisting of the remaining member candidates after excluding one member candidate (placement point a), and is calculated in the same way as error variance (B) above.

[0061] The placement determination module 442 determines that if the error variance (D) is smaller than the error variance (C) (error variance (D) < error variance (C)), the error variance of the adjustment group will decrease compared to before the exclusion (the above state 1) by excluding one candidate member (placement location a). In this case, the placement determination module 442 may determine that the above exclusion condition is met and exclude the candidate member from the adjustment group. This allows the member whose error variance can be reduced in the adjustment group to be determined and the placement group (determined group) to be generated. The placement determination module 442 may select member candidates (placement location a) in descending order of the error variance within the adjustment group (its own group), calculate the error variance (D) excluding the selected member candidates, and determine whether the selected member candidates satisfy the exclusion conditions. This allows for efficient evaluation of the adjustment group and generation of the placement group (confirmed group).

[0062] <Decision on placement> The deployment location determination module 442 generates deployment location groups (confirmed groups) and classifies each deployment location a within the deployment location group into multiple deployment location groups based on error variance. It then determines the level of demand for vehicles V among the multiple deployment location groups based on demand forecast values ​​and prioritizes deployment location a in the high-demand group where the demand is relatively high, determining it as the deployment location for one vehicle V. As a result, the vehicle dispatch management system 1 can efficiently deploy vehicles considering the variance of forecast error values, improve the supply and demand balance of vehicles within the area where mobility services are provided, and increase the vehicle utilization rate.

[0063] Specifically, the placement determination module 442 may calculate the total forecast value by summing the forecast values ​​of each placement point a (reference point and member) within each placement group (confirmed group). The deployment location determination module 442 may also calculate a total number of vehicles by summing the number of vehicles currently deployed at each deployment location a within the deployment location group (confirmed group). The deployment location determination module 442 obtains the number of vehicles currently deployed at each deployment location a within the deployment area A1 (number of deployed vehicles) from the data management device 2. For example, when identifying a group of deployment locations, the deployment location determination module 442 obtains the number of deployed vehicles at each deployment location a based on the deployment location data 241c obtained from the data management device 2. The number of deployed vehicles in the deployment location data 241c obtained from the data management device 2 when identifying the group of deployment locations may also be used to calculate the total number of vehicles. Furthermore, the total number of vehicles in a deployment location group may be updated sequentially when a deployment location group (adjustment group) is created and when a deployment location group (confirmed group) is created.

[0064] The deployment determination module 442 may subtract the total number of units from the predicted total to calculate a group demand value indicating the demand for vehicles V in each deployment group (confirmed group). The deployment determination module 442 can then determine the level of demand for vehicles V among multiple deployment groups by comparing these group demand values. In other words, among multiple deployment groups (confirmed groups), the group with the relatively higher group demand value may be determined as the high-demand group. For example, the confirmed group with the highest group demand value may be determined as the high-demand group. This allows the dispatch management system 1 to more reliably identify dispatch groups with high demand for vehicles V as high-demand groups.

[0065] In this way, the placement determination module 442 derives a high-demand group from among multiple placement groups (confirmed groups) based on the demand forecast value and the number of vehicles V located at each placement point a, and designates this as the placement group (confirmed group) to which one vehicle V that is currently vacant will be placed. The placement determination module 442 then determines the placement point a that will be the location of one vehicle V from among the placement points a included in the high-demand group.

[0066] The destination determination module 442 may designate a reference point of a destination group that is a high-demand group as the destination for one vehicle V. In other words, the dispatch planning device 4 may place an empty vehicle V at a reference point (for example, reference point a1) in a high-demand group. By placing an empty vehicle V at a reference point, which is a destination point a with a relatively large demand forecast value within each divided area, the utilization rate can be improved. Furthermore, since each destination point a, which is a member of each destination group (confirmed group), is within a predetermined range from the reference point, even if the demand for vehicle V (dispatch request) increases at a destination point a other than the reference point, the vehicle V can be quickly moved to the destination point a where the demand has increased. In addition, by prioritizing the placement of vehicle V at the reference point, it becomes possible to smoothly distribute vehicle V based on the demand at each destination point a within the same group, thereby efficiently improving the utilization rate. Furthermore, if location a, where the demand forecast value is greater than that of the reference point, is included in the high-demand group, location a may be designated as the location.

[0067] As another example, the deployment location determination module 442 may calculate the location demand, which indicates the demand for vehicle V at each deployment location a within the high-demand group, and prioritize deployment locations a with relatively high location demand within the high-demand group to determine the deployment location for a single vehicle V. This allows for the deployment of an empty vehicle V to a deployment location a that has relatively high demand for vehicle V, even among deployment locations a belonging to a deployment group with high demand for vehicle V, where the current error variance is low and there is little risk of discrepancy between the demand forecast value and the actual value (measured demand). In other words, vehicle V can be deployed to the deployment location a that has the highest potential to improve the utilization rate at this time.

[0068] The placement determination module 442 outputs the location information of the determined placement location (placement point a) to the planning creation module 443. This allows a vehicle V to be placed in priority to placement point a within the high-demand group. More specifically, within the high-demand group, a vehicle V can be placed in priority to placement point a which is relatively numerous. A relatively numerous placement point a may be, for example, the placement point a with the highest location demand within the high-demand group. This allows vehicle V to be placed in a way that more reliably improves the utilization rate.

[0069] The planning module 443 creates a deployment plan for the deployment location (deployment point a) determined by the deployment location determination module 442. The deployment plan includes a "vehicle ID" that identifies at least one vehicle V, the current status of the vehicle V, and location information of the deployment location a. The planning module 443 transmits the contents of the created deployment plan as update data to the data management device 2, and also transmits an instruction to move to the deployment location (movement instruction information) to the vehicle V. The movement instruction information includes at least location information of the destination deployment location a. This allows the vehicle V to be deployed to the deployment location a. In addition, the vehicle data 241b related to the vehicle V and the deployment location data 241c related to the deployment location a (for example, the number of vehicles to be deployed at the deployment location a) are updated in the data management device 2.

[0070] Vehicle V is equipped with a communication device (not shown) and an output device (not shown) capable of outputting information from the dispatch planning device 4, and is configured to receive and output instructions from the dispatch planning device 4. For example, vehicle V displays movement instruction information on its output device (e.g., display device). Upon receiving movement instruction information, vehicle V uses its navigation function to search for a route to the destination location and guides the driver. Alternatively, in the case of a fully autonomous vehicle, vehicle V, upon receiving movement instruction information, moves to the destination location autonomously. In this way, the dispatch planning device 4 places vehicles V at each location within a single area (service area A) based on the demand forecast values ​​calculated by the demand forecasting device 3 as described above. This makes it possible to place vehicles V at each location in a way that increases the utilization rate of vehicles V while improving user convenience.

[0071] Furthermore, the planning module 443 creates a vehicle dispatch plan based on dispatch requests from the user's terminal device (not shown) received via the communication device 43, and dispatches the vehicle V from each dispatch point a to the boarding location desired by the user. The dispatch management system 1 provides, for example, an on-demand mobility service (dispatch service) using multiple vehicles V targeting a specific service area (e.g., service area A). For this reason, the dispatch request includes information on the boarding location where the user wishes to board the vehicle V (desired boarding location), the disembarking location where the user wishes to disembark from the vehicle V (desired disembarking location), and the number of people using the vehicle V, but does not include information on the date and time the user wishes to board the vehicle V (desired boarding date and time). The dispatch plan may include, for example, the location of the vehicle V to be dispatched to the user, the travel schedule, and the travel route. The travel schedule may include the estimated arrival time at the pick-up location where the user boards and the estimated arrival time at the drop-off location where the user alights. The travel route shows the route taken from the pick-up location to the drop-off location.

[0072] For example, when the planning module 443 creates a dispatch plan, it sends the plan to a designated vehicle V located at the nearest dispatch point a to the pick-up location, and moves vehicle V to the pick-up location by the desired pick-up date and time. As a result, vehicle V is dispatched to the user. For example, when the planning module 443 creates a dispatch plan and dispatches a vehicle V to a user, it associates the boarding location in the dispatch plan with the date and time the dispatch request was received and transmits this as dispatch request information to the demand measurement device 6. This allows the demand measurement device 6 to measure the demand measurement value (number of requests) per unit time at each deployment location that includes the boarding location in its service area. The dispatch request information may also include the content of the dispatch request. Furthermore, when the planning module 443 creates a dispatch plan, it transmits the contents of the dispatch plan as update data to the data management device 2. This updates the vehicle data 241b related to a single vehicle V and the deployment location data 241c related to the deployment location a where the vehicle is deployed (for example, the number of vehicles deployed at deployment location a where the vehicle V dispatched to the user was waiting).

[0073] (Operation of the dispatch management system) Figure 6 is a flowchart showing an example of a dispatch management method in a dispatch management system 1 according to one embodiment of the present disclosure. This method is realized by each device constituting the dispatch management system 1 executing a dispatch management program under the control of a processor, and thereby cooperating with hardware resources. In this example, the processing performed by each controller (controllers 34, 44) of the dispatch management device 10 (demand forecasting device 3 and dispatch planning device 4) in the dispatch management system 1 will be described.

[0074] As shown in Figure 6, the demand forecasting device 3 (forecast value calculation module 341) calculates the forecast demand for each location a in the service area A based on sample data 241a extracted from the data management device 2 (S601), and corrects the calculated forecast demand using representative values ​​of the forecast error (error time series data) for a predetermined past period at each location a (S602).

[0075] Next, the demand forecasting device 3 (error calculation module 342) obtains the average value of the predicted error values ​​included in the error time series data and calculates the error variance for each placement point a (S603). The dispatch planning device 4 (vehicle management module 441) determines whether or not there are any empty vehicles V (S604). For example, if the vehicle management module 441 determines that a vehicle V is empty based on the service status of the vehicle V in the vehicle data 241b, the dispatch plan, etc. (Yes in S604), it outputs the identification information (vehicle ID) of the vehicle V to the destination determination module 442 and requests the determination of the destination location a for the vehicle V. On the other hand, if the dispatch planning device 4 (vehicle management module 441) determines that there are no empty vehicles V (No. in S604), it waits for an empty vehicle V to become available.

[0076] The dispatch planning device 4 (determination module 442) acquires vehicle data 241b of a vehicle V from the data management device 2 based on the fact that a vehicle V has become vacant and a determination of its destination has been requested (S605), and identifies a destination point a within a predetermined range (determination area A1) from the location information (current position) of the vehicle V in the acquired vehicle data (S606). Specifically, the determination module 442 extracts data of destination points a included in the destination area A1 identified based on the current position of the vehicle V from the destination point data 241c stored in the data management device 2, and identifies them as a group of destination points. The dispatch planning device 4 (location determination module 442) transmits data of the group of locations as a forecast result request to the demand forecasting device 3, and the demand forecasting device 3 obtains the forecasted demand value, forecast error value (error time series data), and error variance for each location a in the group of locations as the demand forecast result (S607).

[0077] Next, the vehicle dispatch planning device 4 (location determination module 442) divides the area containing the identified group of locations (location area A1) into multiple areas of a specified area or less (S608), and sets one or more reference points (reference points a1 to a4) in each divided area (divided areas A11 to A14) based on the error variance of each location a (S609). Next, the vehicle dispatch planning device 4 (location determination module 442) identifies multiple location locations a (member candidates) within a predetermined range from the identified reference point (S610), and groups the member candidates for each reference point to generate location groups (confirmed groups) (S611).

[0078] Next, the vehicle dispatch planning device 4 (destination determination module 442) identifies a destination group to which a vehicle V that is currently vacant will be assigned, based on the assignment conditions (S612). For example, the destination determination module 442 identifies a group with a relatively high group demand value from among several destination groups (confirmed groups) as a high-demand group that satisfies the above assignment conditions, and identifies this high-demand group as the destination for a vehicle V. Next, the dispatch planning device 4 (plan creation module 443) places an empty vehicle V at a location a (for example, a location a1 in the divided area A11), which is a reference point for the high-demand group (S613). Specifically, when the location information of location a (for example, a location a1 in the divided area A11), which is a reference point for the high-demand group and has been determined as the placement destination for vehicle V, is output from the placement destination determination module 442, the plan creation module 443 transmits movement instruction information including the location information to vehicle V. As a result, vehicle V is placed at the reference point for the high-demand group.

[0079] Figure 7 is a flowchart showing an example of the grouping process of location points a in the dispatch management system 1 according to one embodiment of the present disclosure. The figure shows details of the process S611 shown in Figure 6.

[0080] The vehicle dispatch planning device 4 (location determination module 442) generates a group of destinations (initial groups) based on the location of the location a (member candidates identified in step S610) near each reference point (reference points a1 to a4 in this example) in each divided region A11 to A14 (step S701). As described above, for example, the location determination module 442 generates a group of destinations (adjusted groups) by adjusting the member candidates based on error variance for each initial group, and then generates a group of destinations (final groups) by evaluating the member candidates based on error variance for the adjusted groups. Steps S702 to S710 below correspond to the process of determining which adjustment group a particular placement point a (placement point a other than the reference point) will join, targeting placement point a which is a candidate member of each initial group.

[0081] The vehicle dispatch planning device 4 (destination determination module 442) identifies a group of destinations (initial group) that includes one destination point a as a member candidate (S702), and selects the initial group to be processed (self group) (S703). For example, if there are multiple reference points within a predetermined range from one destination point a, the destination determination module 442 identifies the multiple initial groups and selects one of them as the initial group to be processed (self group). Next, the dispatch planning device 4 (destination determination module 442) calculates the error variance of the destination group (adjustment group) before and after the addition of one destination point a (S704). For example, the destination determination module 442 calculates the above error variance (A) and error variance (B) as the error variance of the adjustment group based on its own group. Error variance (A) corresponds to the error variance of the adjustment group after the addition of one destination point a, and error variance (B) corresponds to the error variance of the adjustment group before the addition of one destination point a.

[0082] Next, the dispatch planning device 4 (location determination module 442) determines whether the addition of one location a will reduce the error variance of the adjustment group based on the initial group (itself group) (S705). If the location determination module 442 determines that the error variance will decrease (error variance (A) < error variance (B)) (Yes in S705), it proceeds to step S708. On the other hand, if the dispatch planning device 4 (location determination module 442) determines that the addition of one location a will not reduce the error variance of the adjustment group (error variance (A) ≥ error variance (B)) (No in S705), it determines whether there are other adjustment groups that one location a can join (are eligible to join) (S706).

[0083] If the dispatch planning device 4 (destination determination module 442), based on the identification result of step S702, determines that a single dispatch point a is not included in any other initial group and therefore there are no other adjustment groups to join (No. in S706), it proceeds to step S708. Note that if a single dispatch point a is included in multiple initial groups, the dispatch determination module 442 does not consider initial groups that have already been processed. Therefore, for example, even if a single dispatch point a is included in multiple initial groups, if other initial groups have already been processed, it will determine that there are no other adjustment groups to join. On the other hand, if the placement location determination module 442 determines that a placement location a is included in another initial group that is not being processed, and that there is another adjustment group that is eligible to join (Yes in S706), it calculates the error variance (A-1) in the other adjustment group and determines whether the error variance (A) of the adjustment group based on its own group is smaller than the error variance (A-1) of the other adjustment group (S707). Here, the error variance (A-1) of the other adjustment group is the error variance (A) of the adjustment group generated based on the other initial group, and represents the error variance of the other adjustment group after the addition of a placement location a.

[0084] If the dispatch planning device 4 (destination determination module 442) determines that the error variance (A) of the adjustment group based on its own group is smaller than the error variance (A-1) of the other adjustment groups, that is, the adjustment group based on its own group will experience less increase in error variance due to the addition of one placement point a (Yes in S707), it proceeds to step S708. On the other hand, if the destination determination module 442 determines that the error variance (A) is greater than or equal to the error variance (A-1), it returns to step S703. In other words, it selects another initial group as the target for processing.

[0085] In step S708, the dispatch planning device 4 (destination determination module 442) decides to add one dispatch point a to the adjusted combination (adjusted group) of the initial group to be processed (its own group). In other words, the dispatch planning device 4 adds one dispatch point a to the adjusted group based on its own group if the addition of one dispatch point a reduces the error variance (A) of the adjusted group based on its own group (Yes in S705), if there are no other adjusted groups to which one dispatch point a can be added (No in S706), or if the error variance (A) of the adjusted group based on its own group is smaller than the error variance (A-1) of other adjusted groups (Yes in S707).

[0086] Next, the dispatch planning device 4 (location determination module 442) updates the group information of the adjustment group (adjustment group information) (S709). The adjustment group information is based on candidate members (location point a) that have joined the adjustment group and a reference point, and consists of the adjustment group's demand forecast value (average of the demand forecast value at location point a), the adjustment group's error variance (for example, the error variance (C) based on the forecast error value at location point a), and the number of vehicles to be assigned (the number of vehicles V to be assigned at location point a). With this, the classification of a location point a into an adjustment group is completed.

[0087] Next, the dispatch planning device 4 (location determination module 442) determines whether all of the placement points a (group of placement points) within the placement area A1 belong to one of the placement groups (adjustment groups) (S710). If the location determination module 442 determines that there are still placement points a remaining within the group of placement points that do not belong to an adjustment group (No in S710), it returns to step S702. In other words, it classifies the remaining placement points a into adjustment groups. On the other hand, if the location determination module 442 determines that all of the placement points a within the group of placement points belong to an adjustment group (Yes in S710), it evaluates the placement points a (candidate members) within each adjustment group (S711) to determine the placement groups within the placement area A1 (S712). In other words, it determines that the placement points a included in the evaluated adjustment groups are members of the placement groups (confirmed groups).

[0088] Figure 8 is a flowchart showing an example of the evaluation process for a dispatch management system 1 according to one embodiment of the present disclosure. The figure shows the details of the process in step S711 shown in Figure 7. The processes in steps S801 to S807 shown below are an evaluation process for one adjustment group, and this evaluation process is repeated for each adjustment group.

[0089] The vehicle dispatch planning device 4 (destination determination module 442) selects an adjustment group to be evaluated from among multiple destination groups (adjustment groups) (S801), and determines whether the error variance (C) of the selected adjustment group exceeds a predetermined threshold (S802). For example, if the placement determination module 442 determines that the error variance (C) of the adjustment group (itself) including all member candidates is below a predetermined threshold (No. in S802), it terminates the evaluation process for the selected adjustment group. On the other hand, if the placement location determination module 442 determines that the error variance (C) exceeds a predetermined threshold (Yes in S802), it selects placement locations a within the adjustment group in descending order of error variance (S803), and calculates the error variance (D) of the adjustment group excluding the selected placement locations a (S804).

[0090] Next, the placement location determination module 442 determines whether excluding the selected placement location a will reduce the error variance of the adjustment group (S805). Specifically, the dispatch planning device 4 (placement location determination module 442) determines that excluding the selected placement location a will reduce the error variance of the adjustment group (Yes in S805) if the error variance (D) of the adjustment group is smaller than the error variance (C), and proceeds to step S806. On the other hand, the placement location determination module 442, if the error variance (D) of the adjustment group is greater than or equal to the error variance (C), determines that excluding the selected placement location a will not reduce the error variance of the adjustment group and therefore does not need to exclude it, and returns to step S803. In other words, it selects the next placement location a in descending order of error variance.

[0091] In step S806, the dispatch planning device 4 (destination determination module 442) removes the selected destination location a from the adjustment group and updates the adjustment group information (S807). This reduces the error variance (C) of the adjustment group, bringing it closer to a predetermined threshold. After removing one destination location a and updating the adjustment group, the destination determination module 442 returns to step S802. If the error variance (C) of the adjustment group falls below a predetermined threshold due to the above removal, the evaluation process can be terminated and the members of the arrangement group can be determined. If the error variance (C) of the adjustment group still exceeds a predetermined threshold even after the above removal, the search for an arrangement location a that can be removed from the adjustment group can continue.

[0092] As described above, the data management device 2 (an example of a storage device) of the dispatch management system 1 stores the measured demand values ​​for vehicles V measured at each deployment point a within the service area A, the predicted demand values ​​indicating the future demand for vehicles V at each deployment point a, and the predicted error values ​​indicating the error between the measured demand value and the predicted demand value at each deployment point a. The dispatch management device 10 (demand forecasting device 3) also calculates the predicted demand value based on the measured demand value and the error variance indicating the variance of the predicted error values ​​over a predetermined past period for each deployment point a within the service area A (S601~S603). Furthermore, the dispatch management device 10 (dispatch planning device 4) identifies multiple placement points a within a predetermined range from the location of a single vehicle V as a group of placement points (S606), groups each placement point a in the group of placement points based on error variance and classifies them into multiple placement destination groups (S608~S611), determines the level of demand for vehicle V among the multiple placement destination groups (confirmed groups) based on demand forecast values, and prioritizes placing a single vehicle V at a placement point a in the high-demand group where the demand is relatively high (S612, S613). As a result, the dispatch management system 1 can efficiently allocate vehicles while taking into account the variance of prediction error values, thereby improving the balance of supply and demand for vehicles within the area where mobility services are provided and increasing the vehicle utilization rate.

[0093] (modified version) (1) In the above embodiment, with regard to identifying the core reference point of the destination group in the dispatch planning device 4, multiple destination points a are extracted in descending order of demand forecast values ​​from among the destination points a whose error variance is less than or equal to a predetermined threshold, and one or more reference points are identified in each divided area. However, the configuration of the present disclosure is not limited thereto. For example, in the vehicle dispatch planning device 4, the destination determination module 442 may extract destination locations a from among the destination locations a whose error variance is below a predetermined threshold, in order of proximity to the current location of a vehicle V that is currently empty, and identify one or more reference points in each divided region. This suppresses the travel distance of a vehicle V to the destination location a, and allows the vehicle V to be quickly placed at a suitable destination location a while taking into account the variance of the predicted error value.

[0094] (2) In the above embodiment, the placement locations a are grouped when determining the placement location of a single vehicle V by performing the grouping process shown in Figures 7 and 8, but the disclosure is not limited thereto. For example, if the number of placement locations a included in the group of placement locations (within placement area A1) is small, instead of gradually extracting group members (placement locations a) as initial group, adjustment group, and final group, the destination groups may be generated by creating all possible combinations of placement locations a within the group of placement locations. For example, in each divided area, multiple patterns of combinations of placement locations a within the initial group may be created by creating all possible combinations, and the combination of the pattern in which the error variance of the combinations is below a predetermined threshold and the difference between the sum of the demand forecast values ​​of each placement location included in the combination and the sum of the number of vehicles to be placed may be selected as the destination group.

[0095] (Effects of the embodiment) (1) The dispatch management system 1 according to this embodiment comprises a vehicle V used to provide mobility services, a dispatch management device 10 for managing the placement of the vehicle V to placement points a within the service area A, and a data management device 2, wherein the data management device 2 stores demand measurement values ​​indicating the demand for the vehicle V measured at each placement point a within the service area A, demand forecast values ​​indicating the future demand for the vehicle V at each placement point a, and forecast error values ​​indicating the error between the demand measurement value and the demand forecast value at each placement point a, The vehicle dispatch management device 10 includes controllers 34 and 44 that perform the following processes for each placement point a within the service area A: calculating a demand forecast value based on demand measurement values ​​and an error variance showing the variance of the forecast error value over a predetermined past period; identifying placement points a within a predetermined range from the location of a vehicle V as a group of placement points; grouping each placement point a in the group of placement points based on the error variance and classifying them into multiple placement groups; and determining the level of demand for vehicle V among the multiple placement groups based on demand forecast values, and prioritizing the placement point a in the high-demand group where the demand is relatively high to place a vehicle V. This configuration allows for the efficient allocation of vehicles V by grouping locations within the service area based on error variance and deploying vehicles V to locations within the high-demand group. This ensures a good balance between supply and demand for vehicles V within the service area and improves vehicle utilization.

[0096] (2) The data management device 2 stores the number of vehicles V (deployed vehicles) currently deployed at each deployment location a. The controller 44 calculates a total predicted value by summing the predicted demand values ​​for each deployment location a within each deployment group. The controller 44 calculates a total number of vehicles by summing the above-mentioned number of vehicles at each deployment location a within each deployment group. The controller 44 subtracts this total number of vehicles from the predicted total value to calculate a group demand value indicating the demand for vehicles V in each deployment group. The controller 44 may determine the level of demand for vehicles V among multiple deployment groups by comparing these group demand values. With this configuration, the dispatch management system 1 can more reliably identify dispatch groups with high demand for vehicles V as high-demand groups by calculating group demand values ​​using the number of vehicles V deployed at each deployment point a and the demand forecast values. (3) After calculating the demand forecast value for each location a, the controller 34 may correct the demand forecast value with a representative value of the forecast error value over a predetermined past period. This configuration allows for the creation of an efficient vehicle allocation plan based on forecast demand values, thereby improving dispatch efficiency and enhancing user convenience.

[0097] (4) The controller 44 may calculate the location demand indicating the demand for vehicle V at each location a by subtracting the number of vehicles to be deployed from the demand forecast value at each location a within the high-demand group, and may prioritize deploying one vehicle V at location a where the location demand is relatively high. This configuration allows vehicle V to be placed at location a, which currently has the highest potential for improving operational efficiency. (5) The controller 44 may identify several placement locations a from the group of placement locations, where the error variance is below a predetermined threshold and the demand forecast value is relatively large, as reference locations that will serve as the basis for multiple placement groups. This configuration suppresses overall error variance and generates a group of deployment locations centered around deployment point a, where the demand for vehicle V is predicted to be high. (6) The controller 44 may position one vehicle V at a reference point in the high-demand group. This configuration allows for efficient improvement of utilization rates and enables the smooth allocation of vehicles V based on the demand at each location a within the group.

[0098] (7) The controller 44 may divide the placement area A1 within a predetermined range from the position of a vehicle V within the service area A into divided areas of a predetermined area or less, and identify one or more reference points for each divided area. This configuration allows for the creation of well-balanced placement groups within placement area A1. (8) The controller 44 may generate a combination (initial group) of multiple placement points a within a predetermined range from one reference point among the group of placement points and the one reference point, adjust the multiple placement points so that the error variance in the combination is less than or equal to a predetermined threshold, and generate a group of placement destinations (final group) based on the adjusted combination (adjusted group). This configuration allows for the efficient selection of placement locations a that will become members of a confirmed group, even within a placement area A1 where there are many placement locations a. (9) The controller 44 may determine, based on the error variance, whether or not to include each placement point a located within a predetermined range from a reference point in the adjusted combination. This configuration allows for the selection of candidate members whose error variance in the adjustment group that forms the basis of the confirmed group is below a predetermined threshold.

[0099] (10) The controller 44 adds to an adjustment group (adjusted combination) each placement point a within a predetermined range from a reference point that satisfies the first condition (addition condition), and if the error variance (C) in the adjusted combination including the placement point that satisfies the first condition is not below a predetermined threshold, the controller 44 removes the placement point that satisfies the second condition (exclusion condition) from the adjustment group to generate a single placement destination group (final group) in which the error variance (C) is below a predetermined threshold, the first condition being that the error variance (A) in the adjustment group changes to a single state due to the addition of each placement point a, and the second condition being that the error variance (C) in the adjustment group changes to the single state due to the removal of each placement point. With this configuration, location point a that satisfies the inclusion criteria based on error variance is added to the adjustment group, and location point a that satisfies the exclusion criteria based on error variance is removed from the adjustment group, thereby reliably and efficiently selecting members (location point a) that constitute the final group. (11) The controller 44 may determine that the addition condition (first condition) is met when it determines that the error variance value in the adjustment group (adjusted combination) has decreased compared to before the addition of one placement point a, and may determine that the exclusion condition (second condition) is met when the error variance in the adjustment group has decreased compared to before the exclusion of one placement point a. This configuration allows for the formation of an adjustment group using candidate members whose error variance can be reduced, and then the members capable of reducing the error variance within the adjustment group can be determined to generate a destination group (determined group). (12) If a placement point a that constitutes an initial group (combination) does not satisfy the joining condition (first condition), the controller 44 determines whether there is another initial group to which the placement point a belongs. If there is no other initial group, the controller 44 adds the placement point a to an adjusted group (adjusted combination) based on the initial group. If there is another initial group, the controller 44 may add the placement point a to the combination with the smallest error variance (A) among the adjusted groups based on the initial group and the other initial group, respectively. This configuration prevents a single member candidate (a single placement location a) belonging only to their own group from being overlooked in joining the adjustment group, and also suppresses the impact of member candidates who do not meet the joining conditions on the adjustment group's error variance (increase in error variance (A)). [Explanation of symbols]

[0100] 1. Dispatch Management System 2. Data Management Device 21 Input device 22 Output device 23 Communication equipment 24 controllers 24a processor 24b Storage device 241 Data Storage Module 241a Sample data 241b Vehicle Data 241c Placement location data 242 Data Management Modules 3. Demand forecasting device 31 Input device 32 Output device 33 Communication equipment 34 controllers 34a processor 34b Storage device 341 Prediction Calculation Module 342 Error Calculation Module 4. Vehicle dispatch planning device 41 Input device 42 Output device 43 Communication equipment 44 controllers 44a processor 44b Storage device 441 Vehicle Management Module 442 Deployment location determination module 443 Planning Module 5-Factor Data Acquisition Device 6. Demand measurement device 9. Communication Network 10. Dispatch management device a Placement point A1 placement area A11, A12, A13, A14 divided area V Vehicle

Claims

1. A dispatch management system comprising: vehicles used to provide mobility services; a dispatch management device for managing the placement of said vehicles at designated locations within a specified area; and a storage device, The aforementioned storage device is The system stores demand measurement values ​​indicating the demand for the vehicles measured at each location within the district, demand forecast values ​​indicating the future demand for the vehicles at each location, and forecast error values ​​indicating the error between the demand measurement value and the demand forecast value at each location. The aforementioned dispatch management device is For each location within the district, a process is performed to calculate the demand forecast value based on the demand measurement value and the error variance showing the variance of the forecast error value over a predetermined past period. A process to identify a group of placement locations within a predetermined range from the position of the aforementioned vehicle, A process of grouping each placement location in the aforementioned group of placement locations based on the error variance and classifying them into multiple placement destination groups, A vehicle dispatch management system comprising a controller that performs the following processes: determining the level of demand for the vehicle among the multiple dispatch destination groups based on the demand forecast value, and prioritizing the dispatch location within the high-demand group where the demand is relatively high when dispatching the vehicle.

2. The aforementioned storage device stores the number of vehicles currently deployed at each deployment location. The aforementioned controller, The demand forecast values ​​for each placement location within each placement group are summed up to calculate the total forecast value. The total number of units is calculated by summing up the number of units at each deployment location within each deployment group. The total number of units is subtracted from the predicted total to calculate a group demand value indicating the demand for the vehicles in each deployment group, and the level of demand for the vehicles among the multiple deployment groups is determined by comparing these group demand values. The dispatch management system according to claim 1.

3. The controller calculates the demand forecast value for each location and then corrects the demand forecast value using a representative value of the forecast error value over a predetermined past period. The dispatch management system according to claim 1.

4. The aforementioned controller, The number of vehicles deployed is subtracted from the demand forecast value for each deployment location within the high-demand group to calculate the location demand, which indicates the demand for the vehicles at each deployment location. Prioritizing locations where demand is relatively high, the vehicle described above will be deployed to those locations. The dispatch management system according to claim 2.

5. The aforementioned controller, Among the group of placement locations, several placement locations whose error variance is below a predetermined threshold and whose demand forecast value is relatively large are identified as reference locations that will serve as the basis for the group of placement locations. The dispatch management system according to claim 1.

6. The dispatch management system according to claim 5, wherein the controller places the vehicle at the reference point in the high-demand group.

7. The aforementioned controller, Within the district, the area within a predetermined range from the position of one of the vehicles is divided into subdivided areas of a predetermined area or less, and one or more reference points are identified for each subdivided area. The dispatch management system according to claim 5 or 6.

8. The aforementioned controller, A combination of multiple placement points within a predetermined range from one of the group of placement points and the one reference point is generated, and the multiple placement points are adjusted so that the error variance in the combination is less than or equal to a predetermined threshold. The dispatch management system according to claim 5 or 6, which generates a destination group based on the adjusted combination.

9. The controller determines, based on the error variance, whether or not to include each placement point within a predetermined range from one of the reference points in the adjusted combination. The dispatch management system according to claim 8.

10. The aforementioned controller, From among the placement locations within a predetermined range from the aforementioned reference point, the placement location that satisfies the first condition is added to the adjusted combination. If the error variance in the adjusted combination including the placement location that satisfies the first condition is not below a predetermined threshold, the placement location that satisfies the second condition is excluded from the adjusted combination to generate a placement group in which the error variance is below a predetermined threshold. The first condition is that the error variance in the adjusted combination changes to a single state upon the addition of each placement point. The second condition is that the error variance in the adjusted combination changes to the first state by excluding each placement point. The dispatch management system according to claim 9.

11. The aforementioned controller, The first condition is deemed to be met when it is determined that the error variance in the adjusted combination has decreased compared to before the addition of one placement point. The second condition is deemed to be satisfied when the error variance in the adjusted combination decreases compared to before the exclusion of one placement point. The dispatch management system according to claim 10.

12. The aforementioned controller, If one of the placement points constituting a combination does not satisfy the first condition, then it is determined whether there are any other combinations to which that placement point belongs. If there are no other combinations as described above, the aforementioned placement point is added to the adjusted combination based on the aforementioned combination. If there are other combinations, the first placement point is added to the combination with the smallest error variance among the adjusted combinations based on the first combination and each of the other combinations. The dispatch management system according to claim 10.

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