JP7538062B2 - Fare determination method, fare determination system, and fare determination device - Google Patents

Description

The present invention relates to a fare determination method, a fare determination system, and a fare determination device.

Public transportation is considered to be a necessary means of transportation for local residents who do not have their own means of transportation, such as a personal car. Therefore, it is undesirable for the content of services to be changed or services to be stopped at the sole discretion of the transportation operator. On the other hand, there are also cases where businesses are no longer able to operate due to a decrease in the number of people using public transportation services caused by a falling birthrate and changes in lifestyle. Therefore, in order to ensure the continuation of public transportation services, it is necessary to monitor usage by users and flexibly change fares to stabilize the business balance while taking into account the convenience and burden on users.

One method for determining train fares is to refer to information linked to the ID of an IC ticket when the ticket is swiped through a ticket gate to determine the fare. For example, Patent Document 1 discloses a method in which the conditions for discounts and information about the discounts corresponding to those conditions are stored in the ticket gate, and when the ticket gate receives a user's ticket information, it extracts the discount information corresponding to the ticket conditions and determines the fare by taking the discounted fare as the final fare.

Patent Application No. 2009-122649

As explained above, in order to continuously stabilize the revenues of transportation operators and the satisfaction of users, it is necessary to calculate appropriate discount values according to the situation at the time and to raise fares if necessary. For example, during times of heavy congestion, it is thought that by raising fares and dispersing the congestion, it is possible to expect an increase in revenue while eliminating passenger dissatisfaction. Also, during times of extremely low passenger numbers, it is thought that it is necessary to require users to bear a certain burden in order to maintain the number of services. It is also thought that it is necessary to balance fares by simultaneously raising and lowering fares to avoid excessive price increases. However, it is thought that the above-mentioned Patent Document 1 does not disclose any technology related to raising or lowering fares while taking such a balance into account.

The present invention aims to provide a fare determination method, a fare determination system, and a fare determination device that can determine a balanced fare by taking into account both the usage status of users and the financial status of the transportation operator.

According to a first aspect of the present invention, the following fare determination method is provided. That is, the fare determination method calculates a fare variation constant for determining the amount of fare variation using the passenger load rate of each section in each time period, with the boarding and alighting locations of public transportation as the base of the section, and a constant associated with the passenger load rate. A fare variation amount, which is the amount of fare variation, is determined based on the calculated fare variation constant and the target revenue of the transportation operator.

According to a second aspect of the present invention, the following fare determination system is provided. That is, the fare determination system includes a fare determination device and an external system. The fare determination device includes a processor and a memory unit. In the memory unit, a passenger load calculation unit, a fare fluctuation constant calculation unit, and a fare calculation unit are arranged as programs. The passenger load calculation unit is used to calculate the passenger load of each section in each time period, where the boarding and alighting locations of public transportation are used as the base of the section. The fare fluctuation constant calculation unit is used to calculate a fare fluctuation constant for determining the amount of fare fluctuation, using the calculated passenger load of each section and a constant associated with the passenger load. The fare calculation unit is used to determine a fare fluctuation amount, which is the amount of fare fluctuation, based on the calculated fare fluctuation constant and the target revenue of the transportation business operator. The processor executes the passenger load calculation unit to calculate the passenger load of each section in each time period, where the boarding and alighting locations of public transportation are used as the base of the section. The processor executes a fare fluctuation constant calculation unit to calculate a fare fluctuation constant for determining the fare fluctuation amount using the calculated passenger load factor for each section and a constant associated with the passenger load factor. The processor executes a fare calculation unit to determine a fare fluctuation amount, which is the amount of fare fluctuation, based on the calculated fare fluctuation constant and the transportation operator's target revenue.

According to a third aspect of the present invention, the following fare determination device is provided. That is, the fare determination device includes a processor and a storage unit. In the storage unit, a passenger load calculation unit, a fare fluctuation constant calculation unit, and a fare calculation unit are arranged as programs. The passenger load calculation unit is used to calculate a passenger load for each section in each time period, with the boarding and alighting locations of the public transport facility set as the base of the section. The fare fluctuation constant calculation unit is used to calculate a fare fluctuation constant for determining the amount of fare fluctuation, using the calculated passenger load for each section and a constant associated with the passenger load. The fare calculation unit is used to determine a fare fluctuation amount, which is the amount of fare fluctuation, based on the calculated fare fluctuation constant and the target revenue of the transportation business operator. The processor executes the passenger load calculation unit to calculate a passenger load for each section in each time period, with the boarding and alighting locations of the public transport facility set as the base of the section. The processor executes the fare fluctuation constant calculation unit to calculate a fare fluctuation constant for determining the amount of fare fluctuation, using the calculated passenger load for each section and a constant associated with the passenger load. The processor executes the fare calculation unit to determine the fare variation amount, which is the amount of variation in the fare, based on the calculated fare variation constant and the transportation operator's target revenue.

The present invention provides a fare determination method, a fare determination system, and a fare determination device that can determine a balanced fare by taking into account both the usage status of users and the financial status of the transportation operator.

FIG. 1 is a block diagram for explaining the overall configuration of a fare calculation system. FIG. 11 is a diagram showing an example of occupancy rate data. FIG. 13 is a diagram showing an example of a fare variation condition table. FIG. 13 is a diagram showing an example of a fare variation constant table. FIG. 13 is a diagram illustrating an example of an output screen of a fare variation calculation result. FIG. 13 is a diagram illustrating an example of an output screen of a fare variation calculation result. This is an example of a passenger price sensitivity model. 11 is a flowchart illustrating an example of an automatic adjustment process. FIG. 11 is a diagram illustrating an example of a setting screen. 11 is a flowchart for explaining an example of fare determination.

In the embodiment, an example of a fare calculation system (fare determination system) that can determine the amount by which public transportation fares are to be changed (fare change amount), determine fares based on the fare change amount, and determine fares for users is described. Figure 1 is a block diagram for explaining the overall configuration of the fare calculation system.

The fare determination system includes a fare calculation device 1, an external system 2, and an external server 3. In this embodiment, the fare calculation device 1, the external system 2, and the external server 3 are connected via a network 4 and can communicate with each other.

First, the hardware configuration of the fare calculation device 1 will be described with reference to FIG. 1. The fare calculation device 1 is configured as a computer including a central control device 11, an input device 12, an output device 13, a communication device 14, and a storage unit. The central control device 11 is a processor, and can be, for example, a CPU. The input device 12 is an interface through which an operator inputs data. The output device 13 (display device) is a display that displays information output by the central control device 11. The communication device 14 is an interface for communicating via the network 4. The fare calculation device 1 can receive and acquire information from the outside by communication via the communication device 14.

The storage unit of the fare calculation device 1 stores data and programs. In this embodiment, the storage unit includes a main storage device 15 and an auxiliary storage device 16. The main storage device 15 and the auxiliary storage device 16 are suitable recording devices capable of storing (locating) data and programs, and can be configured using, for example, a hard disk drive (HDD). The storage unit may also include a memory, and the programs stored in the storage unit are, for example, realized by being loaded into the memory and executed by the central control device 11.

Next, the external system 2 and the external server 3 will be described. The external system 2 is a system that acquires data on the user's use of public transportation. The external system 2 can then transmit the acquired data via the network 4. The external system 2 acquires data to be used in the processing of the fare determination device 1.

The external system 2 can be configured to include an IC card reader unit that targets the transportation IC cards used by users. Here, if the public transportation is a railway, the external system 2 can be configured to include an IC card reader unit installed at the ticket gate inside the station. In this case, the external system 2 can read the transportation IC card into the IC card reader unit and obtain the entry record (e.g., the user's entry location and entry time) of the user entering through the ticket gate. The external system 2 can also read the transportation IC card into the IC card reader unit and obtain the exit record (e.g., the user's exit location and exit time) of the user exiting through the ticket gate.

The external server 3 is configured as a computer that includes a processor, a storage device, and a communication device, and can receive and store data acquired by the external system 2. The storage device stores programs for appropriately operating the external server 3 (i.e., programs used for sending and receiving data, programs used for storing data, etc.). The processor then reads and executes the programs, thereby acquiring and storing data from the external system 2, and transmitting the data to the outside.

Next, the programs and data stored in the fare calculation device 1 will be described. In the main memory device 15, a data acquisition unit (not shown), a passenger load calculation unit 21, a fare variation constant calculation unit 22, a passenger number prediction unit 23, a fare calculation unit 24, a fare determination unit (not shown), and a display output unit (not shown) are stored as programs. Each program is executed by the central control device 11. Meanwhile, in the auxiliary memory device 16, user information 31, timetable information 32, passenger load data 33, a fare variation condition table 34, a fare variation constant table 35, and a fare calculation result 36 are stored as data.

The fare calculation device 1 executes a data acquisition unit to acquire data from an external source and store user information 31 in the auxiliary storage device 16. The user information 31 is data on the user's use of public transportation, and as an example, when the public transportation is a railway, it includes data on the user's entry location (starting point) and exit location (arrival point), as well as the user's entry time and exit time.

In the fare calculation device 1, the timetable information 32 stored in the auxiliary storage device 16 includes data on the public transport timetable (i.e., data on the departure and arrival times of moving objects). If the public transport is a railway, data on the departure and arrival times of trains is included.

Next, the processing of the occupancy rate calculation unit 21 will be described. The occupancy rate calculation unit 21 is a program used to calculate the occupancy rate of a public transport vehicle and generate occupancy rate data 33. As shown in an example of occupancy rate data 33 in FIG. 2, the occupancy rate data 33 generated by the processing of the occupancy rate calculation unit 21 includes the time period, departure point, arrival point, number of users, number of sections, and occupancy rate (average occupancy rate in this example).

In the occupancy rate data 33, the time period is the section for calculating the occupancy rate. As an example, the time period is appropriately set by the operator, and in the example of Figure 2, it is set in one-hour units. Here, in Figure 2, for example, the time period 5:00 indicates the time after 5:00 and before 6:00.

In the occupancy rate data 33, the departure point and arrival point indicate the departure point and arrival point of the user, and are the range for calculating the occupancy rate and determining the amount by which the fare for public transportation is changed (fare change amount). Note that the example in Figure 2 shows a case where a user travels from A to C using public transportation.

In the occupancy rate data 33, the number of users is the number of users of public transportation from the departure point to the arrival point for each time period. As an example, the number of users is obtained based on the entry and exit locations and entry and exit times of users for each time period. The number of users may be obtained, for example, by executing an appropriate program (e.g., the occupancy rate calculation unit 21) using the user information 31. In addition, the fare calculation device 1 may execute the data acquisition unit to acquire the number of users from the outside. For example, the external server 3 may calculate the number of users for each time period, and the fare calculation device 1 may execute the data acquisition unit to acquire the number of users from the external server 3.

In the calculation process of the occupancy rate by the occupancy rate calculation unit 21 in this embodiment, data that estimates the public transportation (transportation facility) that the user is likely to use on the travel route from the departure point to the arrival point is used, and the average occupancy rate of the moving body on the travel route from the departure point to the arrival point is calculated for each section. Note that in this embodiment, the section is based on the boarding and alighting points of the public transportation facility (i.e., adjacent boarding and alighting points of the public transportation facility are considered to be one section). And, when the public transportation facility is a railway, the section between the boarding and alighting point of a certain station and the boarding and alighting point of the station next to that station on the line is counted as one section.

The method of estimating the public transportation that the user is likely to use on the travel route from the departure point to the destination is not particularly limited as long as it is a valid estimation method, but for example, estimation based on the following method may be performed. That is, when there are multiple travel routes from the departure point to the destination, it is assumed that the user will travel on the travel route that takes the shortest time from the departure point to the destination, and it is estimated that the user will use the public transportation on the travel route that takes the shortest time from the departure point to the destination.

To explain using an example of passenger occupancy rate data 33 in Figure 2, if there are multiple travel routes from departure point A to destination point C, and the travel time is shortest by using transportation line A for section A-B and transportation line B for section B-C (i.e., the travel time is shorter than the travel time required for traveling via other travel routes), it is estimated that users traveling from departure point A to destination point C will use transportation line A and transportation line B.

The process of estimating the public transportation mode may be performed by the fare calculation device 1. In this case, as an example, the memory unit stores data on route information of the public transportation mode and an appropriate program for making the estimation (e.g., the occupancy rate calculation unit 21). The central control device 11 then executes the program to estimate the public transportation mode used by the user using the timetable information 32 and route information. Meanwhile, the fare calculation device 1 may execute the data acquisition unit to acquire the estimated data from outside.

In the occupancy rate data 33, the number of sections is the number of sections on the travel route from the departure point to the arrival point. In the example of Figure 2, values for two sections, section A-B and section B-C, are stored.

In this embodiment, the average passenger rate is calculated for each time period. The average passenger rate is calculated for each section. The method of calculating the average passenger rate is not particularly limited as long as it can be calculated appropriately. As an example, the average passenger rate can be calculated by multiplying the quotient of the passenger number statistics in the section and the number of passengers of the mobile body (passenger number statistics/mobile body capacity) by 100. Here, the passenger number statistics can be, as an example, the statistical value of the number of passengers of the mobile body in the section from a certain day in the past, and can be a statistical value collected for each time period. The average passenger rate may be calculated as the average value of the actual passenger rate (actual passenger rate) for multiple days in the section. In other words, the average passenger rate may be calculated as the average value of the statistical value of the passenger rate for multiple days in a certain section in a certain time period.

In this way, in this embodiment, the average occupancy rate of each section from the departure point to the destination of the mobile body that the user is predicted to board is calculated using data that estimates the public transportation means that the user will use on the travel route from the departure point to the destination.

Although an example of calculating an average occupancy rate has been described, the occupancy rate calculation unit 21 may calculate an occupancy rate based on a median or a mode. For example, instead of the average occupancy rate, the occupancy rate may be calculated as the median of the occupancy rate records (occupancy rate records) for multiple days in a section. Also, instead of the average occupancy rate, the occupancy rate may be calculated as the mode of the occupancy rate records (occupancy rate records) for multiple days in a section. Here, as in the case of calculating the average value described above, the occupancy rate may be calculated as the median of the occupancy rate statistics for multiple days in a certain section in a certain time period. The occupancy rate may be calculated as the mode of the occupancy rate statistics for multiple days in a certain section in a certain time period.

In the above explanation, the occupancy rate of each section from the departure point to the arrival point of the mobile body where the user is predicted to board is calculated using data that estimates the public transportation used by the user. However, as an example, the occupancy rate of a section appropriately set by the operator may be calculated. In other words, the occupancy rate of each section appropriately set by the operator on the travel route from the departure point to the destination may be calculated.

Next, the fare fluctuation constant calculation unit 22 will be described. The fare fluctuation constant calculation unit 22 is a program used to calculate the fare increase/decrease total score (fare fluctuation constant) for determining the amount of fare fluctuation, using passenger load data 33 and a fare fluctuation condition table 34. Here, the fare fluctuation condition table 34 used in the calculation process of the fare fluctuation constant calculation unit 22 will be described with reference to Figure 3.

As shown in FIG. 3, the fare variation condition table 34 includes conditions (conditional expressions) and fare increase/decrease scores (constants). In the fare variation condition table 34, the conditions include information included in the occupancy rate data 33 (information related to the time period, occupancy rate, number of sections, etc.). The fare increase/decrease scores are constants corresponding to the conditions (i.e., constants determined according to the conditions). The fare variation condition table 34 is set by the operator.

The total fare increase/decrease score is calculated for each time period by accumulating the fare increase/decrease scores for the corresponding conditions in the fare change condition table 34. The calculation of the total fare increase/decrease score will be specifically explained below, using the occupancy rate data 33 in Figure 2 as appropriate.

In the fare change condition table 34 in FIG. 3, a fare increase/decrease score of -5 is associated with the condition of a time period after 6:00 and before 7:00, and a fare increase/decrease score of +5 is associated with the condition of a time period after 23:00.

When calculating the total fare increase/decrease score for the occupancy rate data 33 in Figure 2, for the time period after 6:00 and before 7:00, there is a fare increase/decrease score for the corresponding condition in the fare fluctuation condition table 34 (in other words, there is a corresponding record in the fare condition fluctuation table 34), so the fare increase/decrease score value for the time period condition is calculated as -5 for that time period.

In the fare change condition table 34 in FIG. 3, thresholds are set for the occupancy conditions, and a fare increase/decrease score is set according to each threshold (each occupancy range). Note that the occupancy conditions in FIG. 3 are just an example, and a conditional expression that adds information to the occupancy (for example, a conditional expression that combines the occupancy and the time period) may be set as appropriate.

In the fare change condition table 34 in Figure 3, a fare increase/decrease score of +2 is associated with the condition where the occupancy rate is less than 10% and the condition where the occupancy rate is 150% or more and less than 200%, and a fare increase/decrease score of +1 is associated with the condition where the occupancy rate is 10% or more and less than 20%, and the condition where the occupancy rate is 100% or more and less than 150%. In addition, a fare increase/decrease score of 0 is associated with the condition where the occupancy rate is 20% or more and less than 100%, and a fare increase/decrease score of +3 is associated with the condition where the occupancy rate is 200% or more.

When calculating the total fare increase/decrease score for the occupancy rate data 33 in Figure 2, since in the time period after 5:00 and before 6:00, the average occupancy rate for section A-B is 15% and the average occupancy rate for section B-C is 10%, the value of the fare increase/decrease score related to the occupancy rate condition for that time period is calculated as +2 (i.e., the sum of +1 for section A-B and +1 for section B-C). In the time period after 6:00 and before 7:00, the average occupancy rate for section A-B is 50% and the average occupancy rate for section B-C is also 50%, the value of the fare increase/decrease score related to the occupancy rate condition for that time period is calculated as 0. Furthermore, in the time period after 7:00 and before 8:00, the average occupancy rate for section A-B is 160%, and the average occupancy rate for section B-C is 210%, so the fare increase/decrease score value for the occupancy rate condition for that time period is calculated as +5 (the sum of +2 for section A-B and +3 for section B-C). In this way, the calculation process of the fare variation constant calculation unit 22 uses the occupancy rate for each section.

In the fare change condition table 34 of FIG. 3, a fare increase/decrease score of +2 is associated with the condition that the time period is between 8:00 and 8:00 and transportation mode A (i.e., a conditional expression combining the time period and transportation mode). Since there is no corresponding data in the occupancy rate data 33 of FIG. 2, the fare increase/decrease score value based on this condition is calculated as 0. Also, in the fare change condition table 34 of FIG. 3, a fare increase/decrease score of -3 is associated with the condition that the number of sections is 15 or more. Since there is no corresponding data in the occupancy rate data 33 of FIG. 2, the fare increase/decrease score value based on this condition is calculated as 0.

As described above, the total fare increase/decrease score is calculated for each time period by accumulating the fare increase/decrease scores. That is, for the time period from 5:00 to 6:00 in the occupancy rate data 33 in FIG. 2, the time period condition is 0, the occupancy rate condition is +2, and other conditions are 0, so the total fare increase/decrease score is +2 (i.e., 0+2+0). For the time period from 6:00 to 7:00, the time period condition is -5, the occupancy rate condition is 0, and other conditions are 0, so the total fare increase/decrease score is -5 (i.e., -5+0+0). For the time period from 7:00 to 8:00, the time period condition is 0, the occupancy rate condition is +5, and other conditions are 0, so the total fare increase/decrease score is +5 (i.e., 0+5+0).

The fare increase/decrease score value can be thought of as a weight (more precisely, 0 is the base, + values indicate an increase, - values indicate a decrease).

Considering the fare change condition table 34 in Figure 3 as an example, since the boarding rate is extremely high from 7:00 onwards and before 8:00 onwards, in order to disperse congestion and encourage use from 6:00 onwards and before 7:00 onwards, a fare increase/decrease score (-5) related to a fare reduction is set as a condition for that time period. Also, in order to reduce the number of users from 23:00 onwards, a fare increase/decrease score (+5) related to a fare increase is set as a condition for that time period.

In this example, in order to balance occupancy rates (i.e., to reduce transportation modes with extremely high and low occupancy rates and encourage the use of transportation modes with occupancy rates between 20% and 100%), the fare increase/decrease score (+ value) becomes larger as the occupancy rate approaches the maximum value, and the fare increase/decrease score (+ value) becomes larger as the occupancy rate approaches 0%.

In addition, to reduce the number of users of a specific transportation mode (specifically, transportation mode A during the 8:00 a.m. time slot), a fare increase/decrease score (+2) related to a fare increase is set for this condition. When the number of sections increases (in Figure 3, when the number of sections is greater than 15), a fare increase/decrease score (-3) related to a fare decrease is set.

In this way, in this embodiment, the fare change condition table 34, in which conditions and fare increase/decrease scores are appropriately set, is used to carefully evaluate the occupancy rates calculated for each time period and section, and to calculate a well-balanced total fare increase/decrease score.

Next, we will explain the number of users prediction unit 23. The number of users prediction unit 23 is a program used to predict and output the number of users of moving objects on a travel route from a departure point to a destination during a target time period.

The prediction of the number of users may be performed by any suitable method. For example, the prediction of the number of users of mobile objects on a route from a departure point to a destination in a specific time period may be performed based on past data (e.g., data from several years) regarding the number of users of mobile objects on the route from the departure point to the destination in the specific time period.

In addition, based on a trained model that estimates and outputs the number of users of moving objects on a route from a departure point to a destination during a specific time period, a prediction of the number of users of moving objects on the route from the departure point to the destination during the specific time period may be performed. Here, the trained model may be generated by any appropriate method.

In the processing of the user number prediction unit 23, as an example, a predicted value of the number of users (i.e., a fixed value) is calculated, but the number of users may be predicted as a numerical range rather than a fixed value. Here, the range of the number of users can be calculated based on the predicted values of users calculated on multiple scales.

For example, a predicted number of users may be calculated based on an optimistic scale, a pessimistic scale, and an average scale (i.e., a scale intermediate between the optimistic scale and the pessimistic scale), and the range between the predicted value based on the pessimistic scale (i.e., a predicted value calculated taking into account a smaller number of users) and the predicted value based on the optimistic scale (i.e., a predicted value calculated taking into account a larger number of users) may be set as the predicted range of the number of users. In addition, the predicted range of the number of users may be adjusted so as not to deviate from the predicted value of the number of users based on the average scale.

The fare calculation device 1 generates a fare variation constant table 35 by executing an appropriate program located in the storage unit, and stores it in the auxiliary storage device 16. As shown in FIG. 4, the fare variation constant table 35 includes the results of processing by the fare variation constant calculation unit 22 and the results of processing by the number of passengers prediction unit 23. In detail, the fare variation constant table 35 includes time periods, departure points, arrival points, actual number of passengers, predicted number of passengers, and a total fare increase/decrease score. Here, the time periods, departure points, and arrival points correspond to the occupancy rate data 33. Here, the actual number of passengers is the number of passengers who actually traveled from the departure point to the arrival point for each time period.

The actual number of users can be calculated, for example, using an appropriate program based on the user information 31. That is, the actual number of users for each time period may be calculated by referring to the user's entry time, entry location, exit time, and exit location. On the other hand, the fare calculation device 1 may obtain the actual number of users from an external source, for example, the actual number of users calculated in the external server 3.

The predicted number of users is a predicted value of the number of users predicted by executing the user number prediction unit 23. Note that, when a predicted range of the number of users is predicted by executing the user number prediction unit 23, the fare variation constant table 35 may include the predicted range of the number of users. Also, instead of the predicted number of users, a predicted range of the number of users may be included. Here, the predicted range of the number of users may be, for example, a numerical range of 5 to 15, 20 to 30, etc.

Next, we will explain the fare calculation unit 24. The fare calculation unit 24 is a program used to calculate the fare fluctuation amount, calculate the fare based on the fare fluctuation amount, and store the fare calculation result 36 in the memory unit.

In this embodiment, the fare fluctuation amount can be determined using the transportation operator's target revenue, the total fare increase/decrease score, and the number of users. In addition, the fare fluctuation amount is determined so that the calculated fare approaches the target revenue.

Here, the number of users can be the actual number of users, the predicted number of users, or the predicted range of the number of users described above.

Target revenues are set appropriately by transportation operators and are the target amount of fare revenues from the use of public transportation.

In this embodiment, the fare fluctuation amount is determined for each time period in the fare fluctuation constant table 35. In the fare fluctuation constant table 35 of FIG. 4, since the fare increase/decrease total score for the time period of 5:00 is +2, the fare fluctuation amount is calculated for the time period to increase the fare increase/decrease total score by +2 minutes. Since the fare increase/decrease total score for the time period of 6:00 is -5, the fare fluctuation amount is calculated for the time period to decrease the fare increase/decrease total score by -5 minutes. Also, since the fare increase/decrease total score for the time period of 7:00 is +5, the fare fluctuation amount is calculated for the time period to increase the fare increase/decrease total score by +5 minutes.

Here, the fare fluctuation amount may be adjusted according to the number of users. For example, in order to bring fare revenue closer to the target revenue, the fare fluctuation amount may be adjusted so that the fare increase is larger during times when the number of users is high, and so that the fare increase is smaller during times when the number of users is low.

The target revenue can be, for example, the target amount of annual fare revenue. If a predicted number of users or a predicted range of the number of users is used as the number of users, the magnitude of the fare fluctuation amount may be automatically adjusted using the predicted number of users so that the annual fare revenue approaches the target revenue. In other words, since the number of users (predicted value) fluctuates by predicting the number of users at predetermined times (for example, a certain period of time), the fare fluctuation amount may be automatically adjusted to correspond to the fluctuation in the number of users (predicted value). An example of this automatic adjustment will be described in detail later.

The fare based on the fare fluctuation is calculated for each time period as a post-fluctuation fare by estimating the fare fluctuation to the current fare. For example, for the 5:00 time period, a fare (post-fluctuation fare) based on the fare fluctuation to increase the fare by 2 minutes is determined. For the 6:00 time period, a fare (post-fluctuation fare) based on the fare fluctuation to decrease the fare by 5 minutes is determined. For the 7:00 time period, a fare (post-fluctuation fare) based on the fare fluctuation to increase the fare by 5 minutes is determined. The determined fares for each time period are then stored in the auxiliary storage device 16 as data for the fare calculation result 36.

The fare calculation result 36 can be output to the output device 13 by the central control device 11 executing a display output unit (not shown), which is a program used to output the display screen. Next, with reference to Figure 5, the data structure of the fare calculation result 36 and an example of an output screen for the fare variation calculation result will be described. Figure 5 is a diagram for explaining an example of an output screen for the fare variation calculation result.

In the example of Figure 5, the fare fluctuation calculation result output screen displays the fare fluctuation calculation result (i.e., fare calculation result 36) and details of the fare increase/decrease total score. The fare fluctuation calculation result includes, as an example, the time period, departure point, arrival point, fare increase/decrease total score, the pre-fluctuation fare, and the post-fluctuation fare. Here, the time period, departure point, arrival point, and fare increase/decrease total score correspond to the data in the fare fluctuation constant table 35. The pre-fluctuation fare is the fare before the fare fluctuation amount is applied. The post-fluctuation fare is the fare after the fluctuation based on the fare fluctuation amount. The pre-fluctuation fare and the post-fluctuation fare are summarized by time period.

The pre-change fare from departure point A to arrival point C in each time period is set at 200 yen. In other words, if the departure point and arrival point are the same, the pre-change fare is the same for each time period. On the other hand, the post-change fare fluctuates compared to the pre-change fare due to the fare fluctuation amount.

In this example, in the 5:00 time slot, a +2% charge (i.e., 20 yen) is added to the pre-change fare due to a weighting of +2% in the total fare increase/decrease score, and the post-change fare is calculated as 220 yen. In the 6:00 time slot, a -5% charge is subtracted from the pre-change fare, and the post-change fare is calculated as 150 yen. In the 7:00 time slot, a +5% charge is added to the pre-change fare, and the post-change fare is calculated as 250 yen.

In the example of FIG. 5, +1 point of the fare increase/decrease total score is set to a fare fluctuation of 10 yen increase, and -1 point is set to a fare fluctuation of 10 yen decrease, but the fare fluctuation can be appropriately estimated so as to approach the target revenue while taking into account the target revenue. For example, the fare fluctuation may be set to a fare increase of 20 yen for +1 point while taking into account the target revenue. Furthermore, the fare fluctuation may be calculated by differently weighting the fare increase/decrease total score for a fare increase and a fare decrease while taking into account the target revenue. For example, the fare fluctuation may be set to a fare increase of 20 yen for +1 point, and a fare decrease of 10 yen for -1 point.

In the example of FIG. 5, the fare increase/decrease total score details include a time zone column, a departure place column, and an arrival place column as setting columns. Here, each setting column only needs to be understandable by the operator and may be displayed in an appropriate format. The operator can then use the input device 12 to input data into each setting column. Note that each setting column may be configured to allow the target item to be selected, for example, by a pull-down menu.

Here, the input device 12 can be, for example, an appropriate interface such as a mouse or keyboard. When the operator inputs the time period, departure point, and arrival point into the respective setting fields, detailed data on the fare increase/decrease total score corresponding to the set time period, departure point, and arrival point is displayed on the output device 13.

Details of the total fare increase/decrease score include the evaluation items, the relevant condition expressions, and the fare increase/decrease score.

The evaluation item column displays the targets entered in each of the above setting columns. That is, in the example of Figure 5, the time slot of 6:00 has been entered in the time slot column, so the time slot of 6:00 is output in the evaluation item column.

In this example, departure point A is entered in the departure point column, and arrival point C is entered in the arrival point column. Therefore, information on the section from departure point A to arrival point C is displayed in the evaluation item column. In detail, each section from departure point A to arrival point C contained in the occupancy rate data 33 and the occupancy rate for each section are displayed.

The evaluation item column also displays the number of sections based on the input in the departure and arrival column. In this example, the A-B section and the B-C section are included between departure A and arrival C, so the number of sections is displayed as two. The number of sections corresponds to the number of sections included in the occupancy rate data 33.

The evaluation item column displays the route to be used (the means of transport set in the fare variation condition table 34). In this example, when traveling from departure point A to arrival point C, transportation line A is used to travel between the A-B section, and transportation line B is used to travel between the B-C section. And since the fare variation condition table 34 has conditions related to transportation line A, transportation line A is displayed as the route to be used in the evaluation item column.

In the relevant condition expression column, the conditions (condition expressions) that correspond to the contents of the evaluation item are extracted from the fare fluctuation condition table 34 and displayed. In this example, the time period 6:00 in the evaluation item column corresponds to the condition of the time period after 6:00 and before 7:00 in the fare fluctuation condition table 34, so this condition is displayed as the relevant condition expression. In other words, the condition expression that links the time period for which the total fare fluctuation score is calculated and the corresponding fare increase/decrease score is displayed.

The occupancy rate of 50% in the A-B section in the evaluation item column meets the condition in fare fluctuation condition table 34 that the occupancy rate is 20% or more and less than 100%, so this condition is displayed as the relevant condition formula. The occupancy rate of 50% in the B-C section in the evaluation item column meets the condition in fare fluctuation condition table 34 that the occupancy rate is 20% or more and less than 100%, so this condition is displayed as the relevant condition formula. In other words, in calculating the total fare fluctuation score, the condition formula that links the occupancy rate in each section to the corresponding constant (fare increase/decrease score) is displayed. The number of sections in the evaluation item: 2 and the line used: Line A do not meet the conditions in fare fluctuation condition table 34, so the relevant condition formula is not displayed.

The fare increase/decrease score field displays the fare increase/decrease score that corresponds to the relevant condition expression. In this example, the fare increase/decrease score value (i.e., -5) that corresponds to the condition of a time period after 6:00 and before 7:00 is displayed, and the fare increase/decrease score value (i.e., 0) that corresponds to the condition of a passenger load factor of 20% or more and less than 100% is displayed.

The operator can refer to the fare increase/decrease total score details displayed on the output device 13 to check the detailed content of the fare increase/decrease total score for the set time period, departure point, and arrival point (i.e., the relationship between the evaluation items, the relevant conditional expressions, and the fare increase/decrease score that determine the fare increase/decrease total score).

Next, an example of a fare variation calculation result output screen different from that of FIG. 5 will be described with reference to FIG. 6. FIG. 6 is a diagram for explaining an example of a fare variation calculation result output screen. In the example of FIG. 6, the fare variation calculation result output screen displays the fare variation calculation result and details of the user forecast scenario. The fare variation calculation result output screen is output to the output device 13 by the central control device 11 executing the display output unit.

The fare fluctuation calculation result (i.e., fare calculation result 36) includes, as an example, the time period, the departure point, the arrival point, a user forecast scenario, a predicted number of users, a total fare increase/decrease score, the fare before fluctuation, and the fare after fluctuation. The time period column, the departure point column, and the arrival point column display the data to be calculated.

The user forecast scenario corresponds to the scale used to predict the number of users in the processing of the user number prediction unit 23, and is set appropriately by the operator, taking into consideration the trend in the number of users when the fare price is not changed. Then, a display showing each scale is displayed in the user forecast scenario column. The display format is not particularly limited as long as the operator can understand it, but for example, each can be displayed in the form of a symbol or sentence to distinguish them. In this example, user forecast scenario X is a pessimistic scale, user forecast scenario Y is an average scale, and user forecast scenario Z is an optimistic scale.

The predicted number of users field displays the predicted number of users. In this example, the predicted number of users (800, 1000, 1200) is displayed based on predictions using each user prediction scenario (X to Z). Here, the predicted number of users field may display the predicted value after the automatic adjustment process described below. In addition, because different user prediction scenarios are used for the same time period, departure point, and arrival point conditions, the number of users is predicted within a range (800 to 1200 in this example) for these conditions.

The total fare score field displays the total fare score, and the pre-change fare field displays the fare before the fare was changed. The post-change fare field displays the fare after the change based on the amount of fare change. Here, the post-change fare may be displayed as the value after the automatic adjustment process described below. Also, because adjustments are made using different user prediction scenarios for the same time period, departure point, and arrival point conditions, the post-change fare is output with a range (210 yen to 230 yen in this example) for these conditions.

Details of the user forecast scenario include the user forecast scenario included in the fare change calculation results above, the expected annual user growth rate, and a passenger price sensitivity model.

The display in the User Forecast Scenario column corresponds to the display of the fare fluctuation calculation results described above. The column for the expected annual user growth rate displays the expected value based on each scale (i.e., each user forecast scenario). This expected value is set appropriately by the operator based on the perspective of each user forecast scenario. In this example, in user forecast scenario X (i.e., pessimistic scale), the number of users is expected to decrease by 5% per year, and an expected value of -5% is set, while in user forecast scenario Z (i.e., optimistic scale), the number of users is expected to increase by 5% per year, and an expected value of +5% is set. Moreover, in user forecast scenario Y (i.e., average scale), the number of users is expected to remain unchanged, and an expected value of 0% is set.

The passenger price sensitivity model column displays the price sensitivity model used in the automatic adjustment process described below. There are multiple types of price sensitivity models, which are assigned appropriately to each user forecast scenario. The assignment of the price sensitivity model may be performed by the operator or may be performed by appropriate program processing. The price sensitivity model may be displayed in a manner that allows the operator to understand the model being used, for example, using symbols or text.

The price sensitivity model is a model used to predict changes in the number of users when fares fluctuate. The price sensitivity model is stored in the memory unit. The central control device 11 then executes the user number prediction unit 23 to calculate a predicted value for the number of users using the price sensitivity model.

An example of a price sensitivity model will be described with reference to Figure 7. Figure 7 is an example of a passenger price sensitivity model. As shown in Figure 7, the price sensitivity model can be, for example, a model that associates the difference in fares between adjacent time periods on a time series with the proportion of users who change their travel time period when the fare for the adjacent time period is cheaper.

The price sensitivity model may be any model that appropriately predicts changes in the number of users (changes in the number of users) due to changes in fares (price changes). The price sensitivity model may be, for example, a model that correlates the amount of a fare reduction with the increase in the number of new users due to the fare reduction. Conversely, it may be a model that correlates the amount of a fare increase with the decrease in the number of existing users due to the fare increase. The price sensitivity model may also be a model that correlates the amount of a fare reduction (or the amount of a fare increase) for a target time period and section with the increase in the number of new users (or the decrease in the number of existing users) for the target time period and section.

Next, the automatic adjustment process will be described with reference to FIG. 8. FIG. 8 is a flowchart for explaining an example of the automatic adjustment process. This automatic adjustment process is realized by the central control unit 11 executing an appropriate program stored in the storage unit.

First, the central control device 11 executes the user number prediction unit 23 to calculate a predicted value for the number of users based on the expected annual user growth rate set by the operator. Here, the predicted value for the number of users is calculated for each user prediction scenario. Then, the central control device 11 executes the fare calculation unit 24 to adjust the fare for each user prediction scenario (as an example, the pre-variation fare for each user prediction scenario) using the predicted number of users (S101).

Next, the central control device 11 executes the user number prediction unit 23 to input the adjusted fare into the price sensitivity model, calculates the change in the number of users based on the price change, and adjusts the number of users predicted in S101 above (S102). The number of users for each user prediction scenario is adjusted.

Then, the central control device 11 executes the fare calculation unit 24 to adjust the fare using the number of users adjusted in S102 above and the target income (S103). The fare for each user forecast scenario is adjusted.

The central control device 11 repeats the processes of S102 and S103. That is, it repeats the process of inputting the adjusted fare into the price sensitivity model to adjust the number of users, and the process of adjusting the fare using the adjusted number of users and the target revenue, and calculates the final predicted number of users and the final fare (i.e., the final post-change fare) (S104). In this way, the predicted number of users and the post-change fare for each user prediction scenario are calculated.

The end of the loop in the processing of S104 can be determined as appropriate. For example, a determination may be made as to whether the fare has converged to a certain value or a certain range. The loop may also be ended when the range of fluctuation before and after processing is equal to or less than a threshold value. Processing may also be performed based on the number of loops. In other words, loop processing may be executed a predetermined number of times.

The predicted number of users and the post-change fare for each user prediction scenario calculated in the process of S104 can be used to display the fare change calculation result in FIG. 6. In addition, a representative value of the post-change fare for the same time period, departure point, and arrival point conditions may be calculated from the predicted number of users and the post-change fare for each user prediction scenario calculated in the process of S104, and the representative value may be stored as the post-change fare in the fare change calculation result in FIG. 5.

Next, an example of a screen on which an operator sets target revenues, etc. will be described. FIG. 9 is a diagram for explaining an example of a setting screen. The setting screen is displayed on the output device 13 by execution of the display output unit by the central control device 11. The setting screen includes information related to setting revenue targets, information related to conditional expressions and constants for fare increase/decrease scores, and information related to management of the price sensitivity model.

The display in the profit target setting section includes the profit target for the next year. The profit target for the next year is the target profit for the next year. The operator can set the profit target for the next year as appropriate by operating the input device 12. The profit for the previous year may also be displayed, in which case the operator can determine the profit target for the next year by referring to the profit for the previous year.

The display in the section for setting the conditional expressions and constants for the fare increase/decrease scores includes various conditions (conditional expressions) set by the operator, and the fare increase/decrease scores corresponding to the various conditions set by the operator. The operator can set the conditional expressions and the fare increase/decrease scores by referring to the setting screen and operating the input device 12. In other words, the operator can set the fare variation condition table 34 described above.

The display in the price sensitivity model management section includes information to explain the various price sensitivity models stored in the memory unit. For example, the display may include the model name, the prediction target, and the calculation formula.

The name of the price sensitivity model is displayed in the model name column. The name of the price sensitivity model can be determined as appropriate. The name of the price sensitivity model may be set as appropriate by the operator operating the input device 12.

The prediction target column displays a specific explanation of the type of users that are the prediction target. For example, an explanation is displayed as to whether the price sensitivity model is predicting users who change to cheaper time slots or new users. The explanatory text may be appropriately created by the operator operating the input device 12.

The calculation formula column displays information explaining the calculation formula of the price sensitivity model. The explanation of the calculation formula may be created as appropriate by the operator operating the input device 12.

As an example, the operator can operate the input device 12 while referring to the setting screen to appropriately assign a price sensitivity model to each user forecast scenario. The allocation of the price sensitivity model may be performed automatically by the central control device 11 executing an appropriate program stored in the memory unit.

The operator can also add a price sensitivity model as appropriate by operating the input device 12 while referring to the setting screen. The price sensitivity model may be generated outside the fare calculation device 1, and the fare calculation device 1 may acquire an externally generated price sensitivity model by execution of a data acquisition unit by the central control device 11. The fare calculation device 1 may also generate a price sensitivity model. The price sensitivity model may be acquired by execution of an appropriate model generation program stored in the memory unit by the central control device 11. On the other hand, the fare calculation device 1 may be configured so that a price sensitivity model selected by appropriate selection by the operator is deleted.

Next, a method for determining a user's fare will be described. The fare calculation device 1 can determine a user's fare using the fare calculation result 36, for example, as follows. When determining a user's fare, a fare determination unit (not shown), which is a program used to determine a user's fare, is placed in the memory unit of the fare calculation device 1. The central control device 11 then executes the fare determination unit to determine the user's fare.

In the processing of the fare determination unit, the fare for the user is determined to be one of the following: a first fare that references the user's entry time, entry location, and exit location; a second fare that references the user's exit time, entry location, and exit location; and a third fare that is the average value of the first fare and the second fare. Here, the fare for the user is determined when the user exits.

For example, we will explain the processing of the fare determination unit in a case where a user travels from departure point A to arrival point C, enters departure point A between 5:00 and 6:00, and exits arrival point C between 6:00 and 7:00.

In this case, the user's fare is determined to be one of the following fares: a first fare that references the entry time (i.e., the 5:00 time slot), departure point A, and arrival point C; a second fare that references the exit time (i.e., the 6:00 time slot), departure point A, and arrival point C; and a third fare that is the average of the first and second fares. Here, the first fare corresponds to the fare based on the fare fluctuation amount for the 5:00 time slot (the fare after fluctuation) in the fare calculation result 36. The second fare corresponds to the fare based on the fare fluctuation amount for the 6:00 time slot (the fare after fluctuation) in the fare calculation result 36.

The process of determining which of the first fare, the second fare, and the third fare should be the user's fare may be executed as appropriate. For example, the process may be performed such that the highest fare among these fares is set as the user's fare. Furthermore, if it is desired to determine the fare based on the departure time, the process may be performed such that the second fare is set as the user's fare.

Furthermore, when the central control device 11 executes the user number prediction unit 23 to predict a range of user numbers using multiple scales (multiple user prediction scenarios), a post-change fare is calculated based on each scale for the same time period, departure point, and arrival point conditions. In other words, the post-change fare is determined with a range. In this case, the post-change fare used to determine the user's fare (i.e., the first fare, second fare, and third fare) may be appropriately selected from the post-change fare based on each user prediction scenario. Furthermore, the user's fare may be determined using a representative value calculated from the post-change fare for each user prediction scenario.

However, it is conceivable that a transportation disruption may occur in public transportation in the section for which the user's fare is calculated. For example, a transportation disruption may occur in the public transportation estimated to be used by the user (transportation line A or transportation line B in this embodiment) between the time the user enters and the time the user exits. In this case (i.e., when a transportation disruption occurs), the user's fare is set to the lower of the first fare and the second fare. As an example, the occurrence of a transportation disruption can be determined by comparing the arrival time of the moving object based on the timetable information 32 with the departure time of the user.

The determined fare can be output to the output device 13 by executing an appropriate program (e.g., a display output unit).

Next, we will explain an example of the flow of fare determination described above. Figure 10 is a flowchart for explaining an example of fare determination.

The fare calculation device 1 executes the occupancy rate calculation unit 21 to obtain occupancy rate data 33. Here, the occupancy rate is calculated for each time period. In addition, the occupancy rate is calculated for each section (S201).

Next, the fare calculation device 1 executes the fare fluctuation constant calculation unit 22 to calculate the total score of the fare increase/decrease for each time period. In calculating the total score of the fare increase/decrease, the passenger load factor for each section is evaluated (S202).

Next, the fare calculation device 1 executes the fare calculation unit 24 to determine the amount of fare fluctuation for each time period. The amount of fare fluctuation is determined based on the total score of the fare increase/decrease for each time period and the target revenue of the transportation business operator. Here, the fare calculation device 1 may use the number of users to adjust the amount of fare fluctuation to approach the target revenue. Then, the fare based on the amount of fare fluctuation for each time period thus determined (i.e., the fare after fluctuation) is stored in the auxiliary storage device 16 as data of the fare calculation result 36 (S203).

Next, the fare calculation device 1 executes the fare determination unit to determine the user's fare. The user's fare can be determined appropriately by referring to the fare calculation result 36 (by referring to the time period, departure point, arrival point, and changed fare in the fare calculation result 36) (S204). That is, as described above, the fare calculation device 1 can determine the first fare, the second fare, or the third fare as the user's fare, as an example.

Although an embodiment of the present invention has been described above, the present invention is not limited to the above embodiment and various modifications are possible.

The time period in the passenger load data 33 may include a time period based on the entry and exit times of the user. In this case, the fare fluctuation amount for the time period based on the entry and exit times of the user and the fare based on the fare fluctuation amount (i.e., the post-fluctuation fare) can be determined, and the user's fare can be determined based on the time period.

The public transportation may be a bus whose fare is not fixed at a minimum fare but varies depending on the departure and arrival points. When the public transportation is a bus, the external system 2 may be configured to include, for example, an IC card reader unit installed near the boarding entrance and the alighting entrance of the bus (mobile body). In this case, the external system 2 can obtain the boarding record of a user who boards the bus from the boarding entrance by having the IC card reader unit read the transportation IC card. The boarding record includes, for example, the user's boarding location (departure point) and boarding time. The external system 2 can also obtain the alighting record of a user who alights from the alighting entrance by having the IC card reader unit read the transportation IC card. The alighting record includes, for example, the user's alighting location (arrival point) and alighting time. The bus boarding and alighting points are used as the basis for the section. The section between a certain bus boarding and alighting point and a bus boarding and alighting point next to the bus boarding and alighting point on the bus route is counted as one section.

In this specification, the entrance time and boarding time are sometimes collectively referred to as the entrance time. The exit time and disembarking time are sometimes collectively referred to as the departure time. The time when a user passes through the ticket gate at the arrival point and the time when the user disembarks from the bus are sometimes collectively referred to as the exit time.

In this embodiment, the fare increase/decrease score and the total fare increase/decrease score indicate a price increase with a + and a price decrease with a -, but it is also possible to indicate a price increase with a - and a price decrease with a +.

In this embodiment, the storage unit includes a main storage device 15 and an auxiliary storage device 16, but the storage unit may be configured with a single storage medium or multiple storage media. In addition, as long as the processing can be performed appropriately, the programs and data may be stored in separate portions.

One example of a processor is a CPU, but other semiconductor devices (e.g., GPUs) may also be used as long as they are capable of executing a specified process.

The display device is provided in the fare calculation device 1 as the output device 13, but may be a display provided outside the fare calculation device 1. The central control device 11 may output to a display device provided outside the fare calculation device 1.

In the above explanation, the fare was determined in cash, but the fare calculation device 1 may determine the fare in, for example, points having monetary value, digital currency such as electronic money or virtual currency, etc. That is, the fare calculation device 1 may determine the fare fluctuation amount using the total fare increase/decrease score, and determine the user's fare in points having monetary value, etc. Here, the fare calculation device 1 may, for example, perform processing using the above-mentioned points having monetary value instead of cash, and determine the user's fare in the above-mentioned points having monetary value, etc. In addition, the fare calculation device 1 may perform a conversion process, for example, a process of converting between cash and points having monetary value, etc.

1. Fare calculation device (fare determination device)
Reference Signs List 2 External system 3 External server 4 Network 11 Central control device 12 Input device 13 Output device 14 Communication device 15 Main memory device 16 Auxiliary memory device 21 Occupancy rate calculation unit 22 Fare fluctuation constant calculation unit 23 Number of passengers prediction unit 24 Fare calculation unit 31 User information 32 Timetable information 33 Occupancy rate data 34 Fare fluctuation condition table 35 Fare fluctuation constant table 36 Fare calculation result

Claims (13)

the processor calculates, for each time period, a fare fluctuation constant used in determining a fare fluctuation amount by summing up the constants corresponding to the occupancy rates for all sections from the departure point to the arrival point using the occupancy rates for each section in each time period in which the boarding and alighting locations of the public transportation system are used as the section criteria and the constants determined in accordance with a conditional expression for the occupancy rate or a conditional expression in which information is added to the occupancy rate;
Obtain the number of users of a mobile device from the departure point to the arrival point during a target time period;
determining a fare fluctuation amount for each of the time periods based on the calculated fare fluctuation constant and the acquired number of users so that the fare revenue approaches the target revenue of the transportation business operator;
Using the fare fluctuation amount for each time period, a base fare, which is the fare before the change, is summed with the fare fluctuation amount to determine the fare after the change for each time period from the departure point to the arrival point.
A fare determination method comprising: The fare determination method according to claim 1 ,
The processor,
Calculating the occupancy rate of the section based on the average occupancy rate of the section over multiple days, the median occupancy rate of the section over multiple days, or the mode occupancy rate of the section over multiple days;
A fare determination method comprising: The fare determination method according to claim 2 ,
The processor,
Among a plurality of travel routes from the departure point to the destination, a public transportation mode on a travel route that takes the shortest time from the departure point to the destination is presumed to be the public transportation mode used by the user;
Calculate the passenger occupancy rate for each section of the estimated public transportation means ;
A fare determination method comprising: The fare determination method according to claim 1,
The time period is based on the arrival time of the user at the departure point of each section and the departure time of the user at the arrival point of each section .
A fare determination method comprising: The fare determination method according to claim 1 ,
If a transportation disruption occurs during the time a user travels from the departure point to the destination on the route that has the shortest travel time from the departure point to the destination among multiple travel routes from the departure point to the destination ,
The processor,
determining , at the time of exit, as the fare for the user, the lower of a first fare in which the entry time, departure point, and arrival point for the departure point of each section of the user are referenced, and a second fare in which the departure time, departure point, and arrival point for the arrival point of each section of the user are referenced;
A fare determination method comprising: The fare determination method according to claim 1,
The processor,
Calculating the occupancy rate of the section based on a statistical value of the number of passengers of the moving body in the section and the capacity of the moving body;
A fare determination method comprising: The fare determination method according to claim 1 ,
The processor,
predicting the number of users based on a past record of the number of users of moving objects on a travel route from a departure point to a destination in a specified time period, or based on a trained model that estimates and outputs the number of users of moving objects on a travel route from a departure point to a destination in a specified time period ;
automatically adjusting the fare fluctuation amount using the predicted number of users so that the annual fare revenue approaches the transportation operator's annual target revenue;
A fare determination method comprising: The fare determination method according to claim 7 ,
The processor,
predicting the number of users over a range using a plurality of different scales;
Using the predicted number of users, the fare fluctuation amount is determined within a range.
A fare determination method comprising: The fare determination method according to claim 1,
The processor,
The number of users is obtained using a model used to predict changes in the number of users when fares fluctuate;
automatically adjusting the fare fluctuation amount using the number of users so that the annual fare revenue approaches the annual target revenue of the transportation operator;
A fare determination method comprising: A fare determination system including a fare determination device and an external system that acquires an entry time for a departure point of each section of a user, the departure point of the user, an arrival point of the user, and a departure time for the arrival point of each section of the user ,
The fare determination device comprises:
A processor is provided.
The processor ,
Calculate the passenger occupancy rate for each section during each time period , with the boarding and alighting points of public transport as the section criteria,
Using the calculated passenger load factor for each section and a constant determined in accordance with the passenger load factor conditional expression or a conditional expression in which information is added to the passenger load factor, a fare fluctuation constant used to determine the amount of fare fluctuation is calculated for each of the time periods by summing up the constants corresponding to the passenger load factors for all sections from the departure point to the arrival point ;
Obtain the number of users of a mobile device from the departure point to the arrival point during a target time period;
determining a fare fluctuation amount for each of the time periods based on the calculated fare fluctuation constant and the acquired number of users so that the fare revenue approaches the target revenue of the transportation business operator;
Using the fare fluctuation amount for each time period, a base fare, which is the fare before the change, is summed with the fare fluctuation amount to determine the fare after the change for each time period from the departure point to the arrival point.
A fare determination system characterized by : The fare determination system according to claim 10 ,
A display device is provided ,
The processor ,
Displaying the base fare and a fare after change, which is a fare after a change in the sum of the base fare and the fare change amount , on the display device.
A fare determination system characterized by: The fare determination system according to claim 10 ,
A display device is provided ,
The processor ,
displaying on the display device the passenger load factor in each section used in the calculation of the fare variation constant, a conditional expression linking the passenger load factor and the constant in the calculation of the fare variation constant, and the constant used in the calculation of the fare variation constant;
A fare determination system characterized by: A processor is provided.
The processor,
Calculate the passenger occupancy rate for each section during each time period , with the boarding and alighting points of public transport as the section criteria,
Using the calculated passenger load factor for each section and a constant determined in accordance with the passenger load factor conditional expression or a conditional expression in which information is added to the passenger load factor, a fare fluctuation constant used to determine the amount of fare fluctuation is calculated for each of the time periods by summing up the constants corresponding to the passenger load factors for all sections from the departure point to the arrival point ;
Obtain the number of users of a mobile device from the departure point to the arrival point during a target time period;
determining a fare fluctuation amount for each of the time periods based on the calculated fare fluctuation constant and the acquired number of users so that the fare revenue approaches the target revenue of the transportation business operator;
Using the fare fluctuation amount for each time period, a base fare, which is the fare before the change, is summed with the fare fluctuation amount to determine the fare after the change for each time period from the departure point to the arrival point.
A fare determination device characterized by the above .

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