Information processing device, information providing system, and information processing method

Abstract

To provide a technique capable of satisfying both boarding a passenger with a long-distance (high fare) first and reducing waiting time until boarding.SOLUTION: An exemplary information processing device includes: a first calculation unit that calculates a first normalized value based on past data of travel distance or fare from passenger boarding to alighting for each area; a second calculation unit that calculates a second normalized value for each area based on historical data on waiting times for passengers to board; and a third calculation unit that calculates an expected value indicating the degree of expectation of boarding of the passengers for each area based on the first normalized value and the second normalized value.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information providing system, and an information processing method. [Background technology]

[0002] Patent Document 1 discloses an information providing device that can propose efficient routes. The information providing device includes a calculation unit and a providing unit. The calculation unit calculates a customer expectation value that indicates actual demand for each area based on a customer demand forecast value for each area and the number of mobile units that can be supplied for each area. The providing unit provides recommended travel information based on the customer expectation value for each area calculated by the calculation unit.

[0003] In Patent Document 1, the calculation unit weights areas where relatively close destinations are likely to be set so that customer expectations are higher. In other words, the calculation unit weights areas where short-distance rides are expected so that customer expectations are higher than areas where long-distance rides are expected. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2019-79267 Summary of the Invention [Problem to be solved by the invention]

[0005] The configuration of Patent Document 1 allows passengers traveling short distances to board the bus more frequently. However, it may be possible to increase sales by allowing passengers traveling long distances (passengers with higher fares) to board the bus more frequently with as little waiting time as possible.

[0006] In view of the above, the present invention aims to provide a technology that allows passengers traveling long distances (paying high fares) to board preferentially while shortening the waiting time before passengers can board. [Means for solving the problem]

[0007] An exemplary information processing device of the present invention includes a first calculation unit that calculates a first normalization value for each area based on past data of the distance traveled or the fare from when passengers board until they disembark, a second calculation unit that calculates a second normalization value for each area based on past data of the waiting time until passengers board, and a third calculation unit that calculates an expected value indicating the degree to which passengers expect to board for each area based on the first normalization value and the second normalization value.

[0008] An exemplary information providing system of the present invention includes an information processing device having the above-described configuration, and a terminal device that is capable of communicating with the information processing device.

[0009] An exemplary information processing method of the present invention includes a first calculation step of calculating a first normalization value for each area based on past data of the distance traveled or the fare from when passengers board until they disembark, a second calculation step of calculating a second normalization value for each area based on past data of the waiting time before passengers board, and a third calculation step of calculating an expected value indicating the degree to which passengers expect to board for each area based on the first normalization value and the second normalization value. [Effects of the Invention]

[0010] According to the exemplary embodiment of the present invention, it is possible to give priority to passengers traveling long distances (paying high fares) while shortening the waiting time for passengers to board. [Brief explanation of the drawings]

[0011] [Figure 1] A diagram showing the general configuration of an information provision system. [Figure 2] Block diagram showing the configuration of an information processing device [Figure 3] 10 is a flowchart illustrating an example of the flow of an expected value calculation process. [Figure 4] Block diagram showing the configuration of a terminal device [Figure 5] A schematic diagram showing an example of a screen displaying the area where passengers should wait. [Figure 6] 10 is a flowchart illustrating the flow of an expected value calculation process executed by an information processing device according to a modified example. [Figure 7] A table showing an example of the average value of the travel distance calculated for each area and each time period. [Figure 8] A table showing an example of the average waiting time calculated for each area and time period [Figure 9] FIG. 6 is a schematic diagram showing a modified example of the screen display shown in FIG. 5 . DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the drawings.

[0013] <1. Information provision system> FIG. 1 is a diagram showing a schematic configuration of an information provision system 100 according to an embodiment of the present invention. The information provision system 100 is a system that provides information suitable for the operation of vehicles that transport passengers. In detail, the information provision system 100 provides a user with information on locations where passengers with high unit prices can be picked up with as little waiting time as possible. In this embodiment, the vehicle that transports passengers is a taxi. However, the vehicle that transports passengers is not limited to a taxi, and may be other vehicles such as a shared bus or a railroad car. In this embodiment, the user is, for example, a taxi driver or a taxi operator.

[0014] As shown in FIG. 1, an information providing system 100 according to this embodiment includes an information processing device 1 and a terminal device 2.

[0015] The information processing device 1 acquires past data of taxis and performs information processing based on the data. In detail, the information processing device 1 derives information that enables estimation of a location where a specific passenger should wait based on the past data of taxis. The past data is taxi performance information. Details of the performance information will be described later. A specific passenger is a passenger who pays a high fare, for example, a passenger traveling a long distance. The information that enables estimation of a location where a specific passenger should wait is an expected value, which will be described later. The information processing device 1 may be configured to identify a location where a specific passenger should wait, or may be configured not to identify the location. The information processing device 1 may be a so-called server device. The server device that constitutes the information processing device 1 may be a physical server or a virtual server such as a cloud server. Details of the information processing device 1 will be described later.

[0016] The terminal device 2 is provided so as to be able to communicate with the information processing device 1. In detail, the terminal device 2 communicates with the information processing device 1 via a network 4 such as the Internet or a mobile phone network. The terminal device 2 is, for example, an in-vehicle device mounted on a vehicle, a mobile terminal such as a smartphone or a tablet terminal, or a personal computer. When the terminal device 2 is an in-vehicle device, the in-vehicle device may be, for example, a navigation device. Details of the terminal device 2 will be described later.

[0017] 1, the number of terminal devices 2 is one, but typically, a plurality of terminal devices 2 are provided so as to be able to communicate with the information processing device 1. The information providing system 100 may be configured to include at least one terminal device 2.

[0018] The information providing system 100 of this embodiment further includes an in-vehicle device 3. The in-vehicle device 3 is provided so as to be able to communicate with the information processing device 1 via a network 4. Although FIG. 1 shows one in-vehicle device 3, typically, a plurality of in-vehicle devices 3 are provided so as to be able to communicate with the information processing device 1. The information providing system 100 may be configured to include at least one in-vehicle device 3.

[0019] In detail, the in-vehicle device 3 is a device that provides the information processing device 1 with performance information of the taxi in which the device is installed. The taxi performance information includes, for example, riding performance information including passenger boarding locations, disembarking locations, boarding times, and disembarking times, and driving performance information including routes traveled by the taxi. The in-vehicle device 3 transmits the taxi performance information to the information processing device 1 at a predetermined timing. The predetermined timing may be, for example, every predetermined time or when the date changes. The in-vehicle device 3 may be configured as, for example, a navigation device or a drive recorder.

[0020] The in-vehicle device 3 and the above-described terminal device 2 may be the same device. That is, the terminal device 2 may also serve as the in-vehicle device 3. Furthermore, taxi performance information may be collected by a device (such as a server) separate from the information processing device 1, and the information processing device 1 may acquire the taxi performance information from the separate device or from a recording medium on which the information collected by the separate device is recorded. In such a configuration, the information provision system 100 does not need to include the in-vehicle device 3.

[0021] <2. Information processing device> Fig. 2 is a block diagram showing the configuration of an information processing device 1 according to an embodiment of the present invention. Note that Fig. 2 shows only components necessary for explaining the features of the embodiment, and general components are omitted. As shown in Fig. 2, the information processing device 1 includes a control unit 11, a storage unit 12, and a communication unit 13.

[0022] The control unit 11 is a controller that performs overall control of the information processing device 1. The control unit 11 is configured to include, for example, a CPU (Central Processing Unit), RAM (Random Access Memory), and ROM (Read Only Memory). The storage unit 12 stores various programs and various data. The storage unit 12 is at least one type of non-transitory physical storage medium, such as a semiconductor memory, a magnetic medium, or an optical medium. The communication unit 13 is connected to the network 4 by wire or wirelessly, and communicates with the terminal device 2 and the in-vehicle device 3 via the network 4.

[0023] 2 are functions of the control unit 11 that are realized by the CPU of the control unit 11 executing arithmetic processing in accordance with a computer program. That is, the control unit 11 includes the acquisition unit 111, the first calculation unit 112, the second calculation unit 113, the third calculation unit 114, the identification unit 115, and the transmission processing unit 116.

[0024] At least one of the acquisition unit 111, first calculation unit 112, second calculation unit 113, third calculation unit 114, identification unit 115, and transmission processing unit 116 included in the control unit 11 may be configured with hardware such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). Also, the acquisition unit 111, first calculation unit 112, second calculation unit 113, third calculation unit 114, identification unit 115, and transmission processing unit 116 are conceptual components. The function performed by one component may be distributed among multiple components, or the functions of multiple components may be integrated into one component.

[0025] The acquisition unit 111 acquires information from outside the information processing device 1. In this embodiment, the acquisition unit 111 acquires the above-mentioned taxi performance information from outside the information processing device 1 via the communication unit 13. The taxi performance information is transmitted as needed from an in-vehicle device 3 installed in each taxi and capable of communicating with the information processing device 1. The acquisition unit 111 performs processing to store the acquired information in the memory unit 12 as needed.

[0026] The first calculation unit 112 calculates a first normalization value for each area based on past data (actual results) of the distance traveled from when a passenger boards the vehicle until when he or she disembarks or the fare. The first normalization value is a normalized value related to the distance traveled or the fare. In this embodiment, the first calculation unit 112 calculates the first normalization value for each area based on past data (actual results) of the distance traveled from when a passenger boards the vehicle until when he or she disembarks.

[0027] The mileage may be calculated, for example, from passenger boarding point information and drop-off point information included in the taxi performance information described above. Furthermore, if the taxi performance information includes mileage information for each passenger, the mileage information may be used. If the fare is used instead of the mileage, the fare information included in the taxi performance information may be used. Furthermore, the fare may be calculated, for example, using information regarding taxi fare regulations and the mileage information obtained as described above.

[0028] An area may be set arbitrarily. An area may be a region represented by a mesh obtained by dividing the earth's surface into a large number of squares or the like according to a certain rule. The mesh may be, for example, a fourth-order mesh (a 500m square area). However, the mesh is not limited to a fourth-order mesh, and may be, for example, a third-order mesh (a 1000m square area), a fifth-order mesh (a 250m square area), a 100m square mesh (a 100m square area), or the like. Furthermore, in a configuration in which an area is represented by a mesh, a location where passengers are particularly likely to be present, such as a taxi stand, may be exceptionally treated as a region (mesh) that constitutes one area. Furthermore, an area may be represented by a map code, or the like.

[0029] In this embodiment, the first calculation unit 112 calculates the first normalization value NV_1 for each area using the following equation (1). NV_1 = (X-x_min) / (x_max-x_min) (1) X: Average driving distance in the calculation area x_min: The minimum average distance traveled calculated for each area x_max: The maximum average distance traveled calculated for each area

[0030] Note that which area information about a certain mileage is processed as information about is determined based on the passenger's boarding point. For example, if a passenger boards in area A and gets off in area A, the information about the mileage is processed as information about area A. Also, if a passenger boards in area A and gets off in area B, which is different from area A, the information about the mileage is processed as information about area A.

[0031] As can be seen from equation (1), the first normalization value is a value calculated by a conversion equation that converts the average travel distance in the calculation target area to 1 when it is the maximum value and to 0 when it is the minimum value. Also, as can be seen from equation (1), in this embodiment, the first calculation unit 112 calculates the first normalization value using the average value of the travel distance. In detail, the first calculation unit 112 calculates the first normalization value using the average value of the travel distance for each area.

[0032] In this embodiment, the average value of the travel distance for each area is used to calculate the first normalization value, but this is merely an example. Instead of the average value, for example, a median value or a percentile value may be used. By using the average value, the first normalization value can be calculated using all of the data collected for each area.

[0033] When the first normalization value is calculated using the fare instead of the mileage, the first normalization value may be calculated using the average fare. In this case, the average may be replaced by, for example, a median or percentile value. The first normalization value may be calculated only for areas where passengers have boarded a predetermined number of times or more (areas where information on mileage has been obtained a predetermined number of times or more). The predetermined number may be determined as appropriate, for example, through experiments.

[0034] The second calculation unit 113 calculates a second normalized value for each area based on past data (performance data) of the waiting time until passengers board the taxi. The second normalized value is a normalized value related to the waiting time. In detail, the waiting time can be calculated as the time from when an empty taxi enters a certain area until it picks up a passenger in the area. The time when the taxi enters a certain area is included in the performance data of the taxi described above or can be calculated from the performance data. In addition, the time when the passenger boards the taxi is included in the performance data of the taxi described above.

[0035] In this embodiment, the second calculation unit 113 calculates the second normalization value NV_2 for each area using the following equation (2). NV_2 =(1-(Y-y_min) / (y_max-y_min))···(2) Y: Average waiting time in the calculation area y_min: The minimum average waiting time calculated for each area y_max: The maximum value of the average waiting time calculated for each area

[0036] As can be seen from equation (2), the second normalization value is a value found by a conversion equation that converts the average waiting time in the calculation target area to 0 when it is at its maximum value and to 1 when it is at its minimum value. Also, as can be seen from equation (2), in this embodiment, the second calculation unit 113 calculates the second normalization value using the average value of the waiting time. In detail, the second calculation unit 113 calculates the second normalization value using the average value of the waiting time for each area.

[0037] In this embodiment, the average value of the waiting time for each area is used to calculate the second normalization value, but this is merely an example. Instead of the average value, for example, a median value or a percentile value may be used. By using the average value, the second normalization value can be calculated using all the data collected for each area.

[0038] The second normalization value may be calculated only for an area where passengers have boarded a predetermined number of times or more (an area where waiting time information has been obtained a predetermined number of times or more). The predetermined number may be the same as the predetermined number used when determining whether the area is for which the first normalization value is calculated.

[0039] The third calculation unit 114 calculates an expected value indicating the degree of expectation of passengers boarding for each area based on the first normalized value and the second normalized value. More specifically, the expected value indicates the degree of expectation of a specific passenger boarding. In this embodiment, the specific passenger is a passenger who will board a long distance, as will be understood from the following explanation, and who can be caught with as short a waiting time as possible. In this embodiment, the third calculation unit 114 calculates the expected value EV for each area using the following formula (3): EV = NV_1 × NV_2 (3) NV_1: First normalized value NV_2: Second normalized value

[0040] As can be seen from equation (3), the third calculation unit 114 calculates the expected value based on the multiplication of the first normalized value and the second normalized value. This configuration allows the expected value to be calculated taking into account both the travel distance (or fare) and the waiting time. This configuration performs normalization to equate the numerical weight (value) of the travel distance (or fare) and the waiting time, and calculates the expected value for each area. This makes it possible to determine a boarding area that prioritizes passengers traveling long distances (those with high fares) and shortens the waiting time before picking up passengers. In other words, the information provision system 100 including the information processing device 1 having this configuration can efficiently board passengers with high unit prices, thereby improving sales.

[0041] The expected value may be calculated only for an area where passengers have boarded a predetermined number of times or more. The predetermined number may be the same as the predetermined number used when determining whether the area is for which the first normalization value and the second normalization value are to be calculated.

[0042] Although equation (3) is configured to calculate the expected value by multiplying only the first normalization value and the second normalization value, the third calculation unit 114 may be configured to calculate the expected value EV by further multiplying the value obtained by multiplying the first normalization value NV_1 and the second normalization value NV_2 by a coefficient r, as in the following equation (4). EV = NV_1 × NV_2 × r (4) With this configuration, it is possible to calculate the expected value taking into account external factors that are likely to affect taxi rides.

[0043] The coefficient r may be determined, for example, according to the time period during which taxis are taken. In this case, the coefficient r may be set to a value determined for each predetermined time period. For example, the coefficient r may be set to 1 during time periods when the number of taxi rides is empirically normal, greater than 1 during time periods when the number of taxi rides is higher than normal, and less than 1 during time periods when the number of taxi rides is lower than normal.

[0044] The coefficient r may be a coefficient that is changed depending on the weather. For example, the coefficient r may be configured to be changed depending on whether it is raining or not. The coefficient r may also be configured to be set taking into account multiple factors, such as a combination of weather and location. For example, when it is raining, it is expected that the taxi occupancy rate will be high in an area where a station is located, so the coefficient r may be set so that the expected value for that area is high.

[0045] In addition, in the present embodiment, the expected value is calculated by multiplying the normalized value, but other configurations are also possible. For example, the expected value may be calculated using a deviation value or a variance. For example, the deviation value or the variance may be derived from the normalized value calculated as described above.

[0046] The identification unit 115 identifies an area where the passenger should wait based on the calculated expected value. Note that in this embodiment, the higher the expected value in an area, the higher the possibility that a long-distance passenger can be picked up in a short time. For example, the identification unit 115 identifies an area where the expected value is equal to or greater than a predetermined threshold as an area where the passenger should wait. The predetermined threshold may be a value determined by experiment, for example. Note that the area where the passenger should wait is an area where the taxi should remain if there is already a taxi in that area, and is an area where the taxi should head if there is a taxi in another area.

[0047] The information processing device 1 does not necessarily have to include the specifying unit 115. The process of specifying the area where the passenger should wait from the expected value may be performed by the terminal device 2 that acquires the expected value from the information processing device 1.

[0048] The transmission processing unit 116 transmits to the outside the area where passengers should wait, which is identified from the expected value. In particular, the transmission processing unit 116 transmits the area where passengers should wait to the terminal device 2 via the communication unit 13. Note that, if the identification unit 115 is not provided, the transmission processing unit 116 may be configured to transmit the expected value to the terminal device 2. In other words, the transmission processing unit 116 may be configured to transmit to the outside at least one of the expected value and the area where passengers should wait, which is identified from the expected value.

[0049] Fig. 3 is a flowchart illustrating the flow of an expected value calculation process executed by the information processing device 1 according to an embodiment of the present invention. The process shown in Fig. 3 is performed, for example, at predetermined intervals. The predetermined interval is preferably a period during which a predetermined amount or more of taxi performance information acquired from the in-vehicle device 3 is accumulated. The predetermined interval may be, for example, one day or one week.

[0050] In step S1, the first calculation unit 112 identifies the passenger's past boarding and alighting points. In detail, the first calculation unit 112 identifies the passenger's past boarding and alighting points using taxi performance information stored in the memory unit 12. The boarding and alighting points are made up of a pair of a boarding point and a disembarking point. The first calculation unit 112 identifies multiple boarding and alighting points, each of which is made up of a pair of a passenger's boarding point and a disembarking point. When the processing of step S1 is completed, the processing proceeds to the next step S2.

[0051] In step S2, the first calculation unit 112 calculates the travel distance, which is the distance from the boarding point to the disembarking point, for each of the multiple boarding and disembarking points identified in step S1. For example, if the taxi performance information includes a travel route, the travel distance may be calculated according to the travel route. Alternatively, the travel distance may be the distance drawn in a straight line between two points. When the processing of step S2 is completed, the processing proceeds to the next step S3.

[0052] In step S3, the first calculation unit 112 calculates the average value of the mileage for each area using the mileage calculated in step S2. The area data to which each mileage calculated in step S2 belongs is determined by the passenger's boarding point used to calculate the mileage. In other words, each mileage calculated in step S2 becomes data for the area of ​​the boarding point used to calculate the mileage. For each area, the average value of the mileage for that area is calculated using all of the mileage data belonging to that area. Note that if there is no mileage data belonging to an area, the average value of the mileage may not be calculated. When the processing of step S3 is completed, the processing proceeds to the next step S4.

[0053] In step S4, the first calculation unit 112 calculates the first normalization value for each area using the above-mentioned formula (1). Note that if there is an area for which the average value of the traveled distance has not been calculated, the first normalization value for that area may not be calculated.

[0054] Here, assume that the minimum value (x_min) of the average travel distance across all areas is 300 m and the maximum value (x_max) is 30,000 m. In this case, the first area, where the average travel distance is 10,000 m, has a first normalized value of 0.327 (= (10,000 - 300) / (30,000 - 300)). Also, the second area, where the average travel distance is 3,000 m, has a first normalized value of 0.091 (= (3,000 - 300) / (30,000 - 300)). When the processing of step S4 is completed, the process proceeds to the next step S5.

[0055] In step S5, the second calculation unit 113 identifies the passenger's past boarding time. In detail, the second calculation unit 113 identifies the passenger's past boarding time using the taxi performance information stored in the memory unit 12. The second calculation unit 113 identifies multiple passenger's past boarding times. The number of boarding times identified is the same as the number of boarding and disembarking points identified in step S1. When the processing of step S5 is completed, the processing proceeds to the next step S6.

[0056] In step S6, the second calculation unit 113 calculates the waiting time until the taxi picks up the passenger at each boarding point having the boarding time identified in step S5. In this embodiment, the waiting time is the boarding time minus the time when the taxi enters the area where the passenger was picked up. When the processing of step S6 is completed, the processing proceeds to the next step S7.

[0057] In step S7, the second calculation unit 113 calculates the average waiting time for each area using the waiting time calculated in step S6. The average waiting time for each area is calculated using all of the waiting time data belonging to each area. Note that if there is no waiting time data belonging to an area, the average waiting time may not be calculated. When the processing of step S7 is completed, the processing proceeds to the next step S8.

[0058] In step S8, the second calculation unit 113 calculates the second normalization value for each area using the above-mentioned formula (2). Note that if there is an area for which the average waiting time has not been calculated, the second normalization value for that area may not be calculated.

[0059] Here, assume that the minimum value (y_min) of the average waiting time of all areas is 10 seconds and the maximum value (y_max) is 300 seconds. In this case, the second normalized value for the first area, where the average waiting time is 100 seconds, is 0.69 (=(1-(100-10) / (300-10))). Also, the second area, where the average waiting time is 25 seconds, is 0.949 (=(1-(25-10) / (300-10))). When the processing of step S8 is completed, the processing proceeds to the next step S9.

[0060] In step S9, the third calculation unit 114 calculates an expected value for each area using the first normalization value for each area calculated in step S4 and the second normalization value for each area calculated in step S8 according to the above-mentioned formula (3). Note that an expected value may not be calculated for areas for which the first normalization value and the second normalization value have not been calculated.

[0061] 3, the second normalization value is calculated after the first normalization value is calculated, but this is merely an example. For example, the first normalization value may be calculated after the second normalization value is calculated. Also, for example, the first normalization value and the second normalization value may be calculated in parallel.

[0062] The expected value for the first area, where the average driving distance is 10,000 m and the average waiting time is 100 seconds, is 0.226 (= 0.327 × 0.69). Similarly, the expected value for the second area, where the average driving distance is 3,000 m and the average waiting time is 25 seconds, is 0.086 (= 0.091 × 0.949). Based on this example, theoretically, by simply adding 75 seconds of waiting time, it is possible to identify areas where passengers should be waiting, where the driving distance for passengers can be extended by 7 km (from 3,000 m to 10,000 m). This can be expected to increase sales while reducing the number of taxis in operation.

[0063] In this embodiment, once the expected value for each area is calculated, the area where the passenger should wait is identified by the identification unit 115. Then, the transmission processing unit 116 transmits information about the area where the passenger should wait to the terminal device 2.

[0064] <3. Terminal Device> Fig. 4 is a block diagram showing the configuration of a terminal device 2 according to an embodiment of the present invention. Note that Fig. 4 shows only components necessary for explaining the features of the embodiment, and general components are omitted. As shown in Fig. 4, the terminal device 2 includes a control unit 21, a storage unit 22, a communication unit 23, an operation unit 24, and a display unit 25.

[0065] The control unit 21 is a controller that performs overall control of the entire terminal device 2. The control unit 21 is configured to include, for example, a CPU, RAM, and ROM. The storage unit 22 stores various application software (apps) and various data. The storage unit 22 is configured, for example, with semiconductor memory elements such as RAM and flash memory. The communication unit 23 is connected to the network 4 wirelessly or via a cable, and communicates with the information processing device 1 via the network 4.

[0066] The operation unit 24 enables operation by a user of the terminal device 2. The user is, for example, a taxi driver or an employee of a taxi business. The operation unit 24 is configured with, for example, a touch panel, buttons, a rotary knob, a mouse, a touchpad, etc. The display unit 25 is configured using, for example, a liquid crystal panel or an organic EL panel, etc. In this embodiment, the display unit 25 is included in the terminal device 2. However, the display unit 25 does not have to be included in the terminal device 2. For example, if the terminal device 2 is a personal computer, the terminal device 2 and the display unit 25 may be separate devices.

[0067] The terminal device 2 receives information transmitted from the information processing device 1 via the communication unit 23. In this embodiment, the information transmitted from the information processing device 1 includes the information on the area where passengers should wait. When the control unit 21 of the terminal device 2 acquires the information on the area where passengers should wait, the control unit 21 performs a process of displaying the information on the screen of the display unit 25. That is, the terminal device 2 performs a process of displaying the area where passengers should wait, which is identified from the expected value, on the screen. This allows the user to easily understand the location of the area where passengers should wait by looking at the screen.

[0068] As described above, the terminal device 2 may be configured to receive the expected value for each area from the information processing device 1 and specify the area where the passenger should wait. In this case, the terminal device 2 may be configured to perform a process of displaying information about the area on the display unit 25 after specifying the area where the passenger should wait.

[0069] FIG. 5 is a schematic diagram showing an example of a screen displaying an area where a passenger should wait. In the example shown in FIG. 5, a map is displayed on the screen with a mark 200 indicating the current location of the taxi vehicle superimposed thereon. In addition, a boundary line indicating the boundary of each area (mesh) is superimposed and displayed as a dashed line. Each area is square. Of the multiple areas displayed on the screen, areas where a passenger should wait are indicated by hatching 300. In the example shown in FIG. 5, two areas where a passenger should wait are displayed. The user can easily determine the area where a passenger should wait by looking at the screen of the display unit 25.

[0070] In the example shown in FIG. 5, only whether or not an area is suitable for waiting for passengers is displayed on the screen of the display unit 25. However, this is merely an example. For example, areas suitable for waiting for passengers may be classified into multiple levels and displayed. When areas suitable for waiting for passengers are classified into multiple levels, the classification may be based on the magnitude of the expected value. That is, the terminal device 2 may be configured to display each area on the screen in a different display mode depending on the degree of recommendation as an area suitable for waiting for passengers, which is determined according to the calculation process performed by the information processing device 1. Furthermore, as in the example shown in FIG. 5, when there are multiple areas suitable for waiting for passengers, only the area closest to the current location may be displayed on the display unit 25. Furthermore, each area does not need to be square and may have another shape. When a taxi stand constitutes a single area, the area may be a small area arranged within the square area.

[0071] <4. Modifications> Fig. 6 is a flowchart illustrating the flow of the expected value calculation process executed by an information processing device according to a modified example. The flowchart shown in Fig. 6 is generally the same as the flowchart shown in Fig. 3. Therefore, the following description will focus on the differences. Note that in this modified example, as in the above-described embodiment, the fare may be used instead of the mileage.

[0072] In step S1A, the first calculation unit 112 identifies the passenger's past boarding and alighting points and the boarding times associated with those boarding and alighting points. The first calculation unit 112 performs this identification using taxi performance information stored in the storage unit 12. The first calculation unit 112 identifies multiple pieces of information each consisting of a pair of a boarding and alighting point and a corresponding boarding time. When the processing of step S1A is completed, the process proceeds to the next step S2A.

[0073] In step S2A, similar to the embodiment described above, the first calculation unit 112 calculates the travel distance, which is the distance from the boarding point to the disembarking point, for each of the multiple boarding and disembarking points identified in step S1A. When the processing of step S2A is completed, the processing proceeds to the next step S3A.

[0074] In step S3A, the first calculation unit 112 uses the mileage calculated in step S2A to calculate the average mileage for each area and time period. As in the above-described embodiment, the area data to which each mileage calculated in step S2 belongs is determined by the passenger's boarding point used to calculate the mileage. The time periods may be, for example, time periods classified every hour, every two hours, or every six hours, based on the start of the day (midnight). The time period data to which the mileage belongs is determined by the boarding time acquired in step S1A. The mileage data calculated in step S2A is classified by area and time period, and the average mileage is calculated for each classified group. This results in, for example, the results shown in FIG. 7. FIG. 7 is a table illustrating the average mileage calculated for each area and time period. Note that the example shown in FIG. 7 shows only the average mileage for each time period for some areas (area A and area B). When the processing of step S3A is completed, the processing proceeds to the next step S4A.

[0075] In step S4A, the first calculation unit 112 calculates the first normalization value for each area and for each time period using the above-mentioned formula (1). That is, in the modified example, the first calculation unit 112 calculates the first normalization value for each area and for each time period. The first calculation unit 112 calculates the first normalization value using the average value of the travel distance (or fare) for each area and for each time period.

[0076] In this example, x_min in formula (1) is the minimum value of the average traveled distance calculated for each area in the time period to be calculated. Also, x_max in formula (1) is the maximum value of the average traveled distance calculated for each area in the time period to be calculated. For example, a case where the traveled distance data shown in FIG. 7 is obtained will be described.

[0077] Assume that the minimum value of the average travel distance calculated for each area during the time period from midnight to 6:00 is 1000m and the maximum value is 30000m. In this case, the first normalized value for area A from midnight to 6:00 is 0 (=(1000-1000) / (30000-1000)). The first normalized value for area B from midnight to 6:00 is 0.017 (=(1500-1000) / (30000-1000)).

[0078] Assume that the minimum average travel distance calculated for each area during the time period from 6:00 to 12:00 is 300m and the maximum is 30,000m. In this case, the first normalized value for area A from 6:00 to 12:00 is 0.091 (=(3,000-300) / (30,000-300)). The first normalized value for area B from 6:00 to 12:00 is 0.22 (=(7,000-300) / (30,000-300)).

[0079] Similarly, the first normalized value of each area is calculated for each time period. When the process of step S4A is completed, the process proceeds to the next step S5A.

[0080] 6, in step S5A, the second calculation unit 113 identifies the passenger's past boarding time. However, in this example, since the first calculation unit 112 has already identified the past boarding time, that data may be used. When the processing of step S5A is completed, the processing proceeds to the next step S6A.

[0081] In step S6A, the waiting time until the passengers are picked up at each boarding point is calculated, as in the above-described embodiment. When the processing of step S6A is completed, the processing proceeds to the next step S7A.

[0082] In step S7A, the second calculation unit 113 uses the waiting times calculated in step S6A to calculate the average waiting times for each area and time period. As in the case of the travel distance described in step S3A, the waiting time data calculated in step S6A is classified by area and time period, and the average waiting time is calculated for each classified group. As a result, a result such as that shown in FIG. 8 can be obtained. FIG. 8 is a table illustrating the average waiting times calculated for each area and time period. Note that the example shown in FIG. 8 shows only the average waiting times for each time period for some areas (area A and area B). When the processing of step S7A is completed, the processing proceeds to the next step S8A.

[0083] In step S8A, the second calculation unit 113 calculates the second normalization value for each area and for each time period using the above-mentioned formula (2). That is, in the modified example, the second calculation unit 113 calculates the second normalization value for each area and for each time period. The second calculation unit 113 calculates the second normalization value using the average value of the waiting time for each area and for each time period.

[0084] In this example, y_min in formula (2) is the minimum value of the average waiting time calculated for each area in the time period being calculated. Also, y_max in formula (2) is the maximum value of the average waiting time calculated for each area in the time period being calculated. For example, a case will be described where the waiting time data shown in FIG. 8 is obtained.

[0085] Assume that the minimum average waiting time calculated for each area during the time period from midnight to 6:00 is 600 seconds and the maximum is 1800 seconds. In this case, the second normalized value for area A from midnight to 6:00 is 0.667 (=(1-(1000-600) / (1800-600))). The second normalized value for area B from midnight to 6:00 is 0.25 (=(1-(1500-600) / (1800-600))).

[0086] Assume that the minimum average waiting time calculated for each area during the time period from 6:00 to 12:00 is 10 seconds and the maximum is 300 seconds. In this case, the second normalized value for area A from midnight to 6:00 is 0.690 (= (1-(100-10) / (300-10))). The second normalized value for area B from midnight to 6:00 is 0.759 (= (1-(80-10) / (300-10))).

[0087] Similarly, the second normalized value of each area is calculated for each time period. When the process of step S8A is completed, the process proceeds to the next step S9A.

[0088] In step S9A, the third calculation unit 114 calculates an expected value for each area and each time period using the first normalized value for each area and each time period calculated in step S4A and the second normalized value for each area calculated in step S8A, according to the above-described formula (3). That is, in the modified example, the third calculation unit 114 calculates an expected value for each area and each time period. With this configuration, the expected value for taxis is calculated for each time period, so that it is possible to appropriately respond to demand for taxis and efficiently pick up long-distance passengers (passengers with high fares).

[0089] For example, in the examples shown in Figures 7 and 8 above, the expected value for area A from midnight to 6:00 is 0 (= 0 x 0.667), and the expected value for area B is 0.004 (= 0.017 x 0.25). Furthermore, the expected value for area A from 6:00 to 12:00 is 0.063 (= 0.091 x 0.690), and the expected value for area B is 0.167 (= 0.22 x 0.759). In the same way, the expected value for each area is calculated for each time period.

[0090] In this modification, the area where passengers should wait for each time period is identified based on the expected value calculated for each area and for each time period. When identifying the area where passengers should wait based on the expected value, the threshold value for determining whether or not to identify the area as a place where passengers should wait may be changed for each time period.

[0091] In this modification, a screen similar to that shown in FIG. 5, displaying the area where passengers should wait, can be displayed on the display unit 25 according to the time period, based on the expected values ​​calculated for each area and for each time period. FIG. 9 is a schematic diagram showing a modification of the screen display shown in FIG. 5. As shown in FIG. 9, on the screen displaying the area where passengers should wait, each area may be hatched according to the average waiting time according to the time period (the average waiting time for each time period shown in FIG. 8 is exemplified). Note that in FIG. 9, the area 300 where passengers should wait, identified based on the expected value, is the same as in FIG. 5 and is hatched according to the same hatching as in FIG. 5. The other areas are hatched according to the average waiting time determined by the time period. In FIG. 9, one of three types of hatching is used according to the average waiting time for each area. This configuration allows a user (e.g., a taxi driver) to easily understand the waiting time in each area. For this reason, for example, when there is a traffic jam on the way to the waiting area 300 calculated from the expected value and it is expected that it will take a long time to arrive at the area 300, the destination can be changed to an area that can efficiently pick up passengers by using the classification of average waiting times displayed on the screen. That is, in the example shown in Fig. 9, the terminal device 2 is configured to perform processing to display each area on the screen in different display modes depending on the degree of recommendation as an area where passengers should wait, which is determined according to the result of the calculation processing performed by the information processing device 1.

[0092] In the example shown in FIG. 9, the length of the average waiting time is classified by hatching, but this is merely an example, and classification by color, for example, may also be used. The number of classifications is not limited to three and may be any other number. When the waiting area 300 obtained from the expected value is shown by hatching, the classification color of the average waiting time, which is color-classified, may be superimposed on the area 300. Furthermore, hatching or color indicating the classification of the average waiting time may be applied to all areas or only to some areas. The some areas may be, for example, areas determined to be areas where passengers should particularly wait.

[0093] <5. Things to keep in mind> In addition to the above embodiments, the various technical features disclosed in this specification can be modified in various ways without departing from the spirit of the technical creation. In other words, the above embodiments should be considered to be illustrative in all respects and not limiting. The technical scope of the present invention is defined by the claims, not by the description of the above embodiments, and should be understood to include all modifications that fall within the meaning and scope of the claims. Furthermore, the multiple embodiments and modifications shown in this specification may be combined as appropriate to the extent possible. [Explanation of symbols]

[0094] 1. Information processing device 2. Terminal device 100···Information provision system 112 First calculation unit 113...Second calculation unit 114...Third calculation unit

Claims

1. An information processing device including a controller, The controller calculating a first normalized value from an average value of the travel distance or the fare calculated for each time period for each area based on past data of the travel distance or the fare from when the passenger boards until when the passenger gets off; calculating a second normalized value from an average value of the waiting time calculated for each time period for each area based on past data of waiting times for passengers to board the train; an information processing device that calculates an expected value indicating the degree of expectation of passengers boarding for each time period for each area by multiplying the first normalized value by the second normalized value;

2. The information processing device described in Claim 1, wherein the controller calculates the first normalization value NV_1 using the following formula <1> and the second normalization value NV_2 using the following formula <2>. NV_1=(X-x_min) / (x_max-x_min) <1> X: Average distance traveled or average fare in the calculation area during the calculation time period x_min: The minimum value of the average distance traveled or the average fare calculated for each area during the calculation time period x_max: The maximum value of the average distance or average fare calculated for each area during the calculation time period NV_2=(1-(Y-y_min) / (y_max-y_min)) <2> Y: Average waiting time in the calculation area during the calculation time period y_min: The minimum value of the average waiting time calculated for each area during the calculation period y_max: The maximum value of the average waiting time calculated for each area during the calculation period

3. The information processing apparatus according to claim 1 , wherein the controller calculates the expected value by further multiplying a value obtained by multiplying the first normalized value and the second normalized value by a coefficient.

4. An information processing device according to any one of claims 1 to 3; a terminal device that is communicable with the information processing device; An information provision system comprising:

5. 5. The information providing system according to claim 4, wherein the terminal device performs processing for displaying on a screen an area where the passenger should wait, which is specified based on the expected value.

6. The information provision system according to claim 4, wherein the terminal device performs processing to display each of the areas on the screen in different display modes depending on the degree of recommendation of the area as a place for waiting for passengers, which is determined based on calculation processing performed by the information processing device.

7. An information processing method executed by an information processing device, comprising: calculating a first normalized value from an average value of the travel distance or the fare calculated for each time period for each area based on past data of the travel distance or the fare from when the passenger boards until when the passenger gets off; calculating a second normalized value from an average value of the waiting time calculated for each time period for each area based on past data of waiting times for passengers to board the train; An information processing method, comprising: multiplying the first normalized value by the second normalized value to calculate an expected value indicating the degree of expectation of passengers boarding for each time period for each area.

Images