JP6872331B2 - Evaluation system and evaluation method - Google Patents

Description

The present invention relates to an evaluation system and an evaluation method for evaluating operation information of transportation means.

Transportation such as buses and railroads is operated based on the planned schedule (operation information). However, if the operation is stopped due to a transportation obstacle or a disaster and the schedule is disturbed, the schedule will be changed in order to reduce the impact on passengers and quickly return to normal operation. At this time, it is necessary to create an optimal timetable plan in consideration of the flow of passengers.

As a background technique of this technique, there is International Publication No. 2014/073412 (Patent Document 1). Patent Document 1 is an operation schedule evaluation device 1 having a passenger flow calculation unit and a power consumption calculation unit, and the passenger flow calculation unit relates to operation schedule information of each train and entry and exit of a passenger station. Based on the passenger information, passenger flow information regarding the passenger flow by train transportation is created, and the power consumption calculation unit includes the passenger flow information and operation schedule information created by the passenger flow calculation unit, and the vehicle information of each train. Based on, the number of passengers or the occupancy rate of each train is calculated, and the power consumption of each train that reflects the number of passengers or the vehicle weight according to the occupancy rate is calculated for each unit time. ing.

International Publication No. 2014/0734112

When the timetable is evaluated by the method described in Patent Document 1, a boarding train is assigned to each passenger, the required time for each passenger is calculated, and the operation timetable is evaluated. Therefore, the calculation time depends on the number of passengers. Increase. In addition, since the diamond evaluation process is performed for each of the diamond proposals to be evaluated, the calculation time increases depending on the number of diamond proposals. In the event of a transportation failure, it is necessary to determine the correction schedule immediately, and it is an issue to evaluate many passenger flows and multiple schedules at high speed. In addition, it is necessary to accurately predict passenger demand in order to accurately evaluate the draft schedule. Passenger demand fluctuates greatly not only due to transportation obstacles but also due to the effects of events and bad weather, so forecasting passenger demand according to the situation is an issue.

Therefore, it is an object of the present invention to predict passenger demand according to the situation and to evaluate the draft schedule at high speed without depending on the number of passengers.

A typical example of the invention disclosed in the present application is as follows. That is, it is an evaluation system that evaluates train operation information, and is a movement pattern that records transportation user information that records the history of passengers using the train and the movement route between the entrance station and the exit station. With reference to the storage unit that stores the information, the transportation user information, and the moving pattern information, the arrival time of the passengers at the boarding position of the train is estimated, and each station is estimated for each time zone of the arrival time and each entrance station. The number of passengers on the train is estimated by using the passenger flow matrix generation unit that generates the passenger flow matrix represented by the number of passengers using the space, the passenger flow matrix, and the transportation operation information indicating the operation of the train. With reference to the evaluation unit that calculates the invalidity value of passengers, the congestion degree of the train, and the transportation operation information, it is determined whether the train is in a transportation failure, normal time, or non-normal time, and it is a transportation failure. If it is determined that the train is in normal condition, a passenger flow matrix at the time of transportation failure is generated, if it is determined that it is normal, a passenger flow matrix in normal time is generated, and if it is determined that it is not normal, it is non-normal. When the occurrence of a transportation failure is detected by referring to the non-normal information detection unit that generates the passenger flow matrix at the time, the congestion degree of the train acquired in real time, and the transportation operation information. Select the passenger flow matrix at the time of the transportation failure, and if the occurrence of the transportation failure is not detected, refer to the congestion history data showing the congestion degree and the actual congestion rate of the train in real time, and the difference from the past is the largest. It is provided with a passenger demand prediction unit that selects a passenger flow matrix for a small day from the normal passenger flow matrix or the non-normal passenger flow matrix.

According to a typical embodiment of the present invention, passenger demand can be predicted according to the situation, and the schedule can be evaluated at high speed without depending on the number of passengers. Issues, configurations and effects other than those described above will be clarified by the description of the following examples.

It is a figure which shows the structure of the diamond evaluation apparatus which concerns on embodiment of this invention. (A) is a diagram showing a configuration example of transportation user information according to this embodiment, (B) is a diagram showing a configuration example of transportation operation information according to this embodiment, and (C) is a diagram showing a configuration example of transportation operation information according to this embodiment. It is a figure which shows the structural example of the movement pattern information which concerns on Example. (A) is a diagram showing a configuration example of a passenger flow matrix according to this embodiment, and (B) is a diagram showing a configuration example of a passenger flow matrix according to this embodiment. (A) is a diagram showing a configuration example of congestion history data according to this embodiment, and (B) is a diagram showing a configuration example of non-normal information according to this embodiment. It is a figure which shows the concept of the mathematical model which concerns on this Example. It is a figure which shows the relationship between the transport capacity of the line which concerns on this Example, and the number of detour passengers. It is a flowchart of the passenger flow matrix generation processing which concerns on this Example. It is a flowchart of the timetable evaluation process which concerns on this Example. It is a figure which shows an example of the input diamond which concerns on this Example. It is a figure which shows the composition example of the passenger boarding pattern which concerns on this embodiment, (A) is a passenger boarding pattern of a train A, (B) is a passenger boarding pattern of a passenger who transfers from train A to train B, (C) is a train. The passenger boarding pattern of B is shown. (A) is a diagram showing a configuration example of a diamond evaluation result (train) according to this embodiment, and (B) is a diagram showing a configuration example of a diamond evaluation result (overall) according to this embodiment. It is a flowchart of the non-normal information detection process which concerns on this Example. It is a flowchart of the passenger demand forecast processing which concerns on this Example. It is an example of real-time comparison of the congestion degree and congestion history data of the train according to this embodiment, (A) shows a time series graph, and (B) shows the comparison result. It is a figure which shows an example of the menu screen which concerns on this Example. It is a figure which shows an example of the diamond evaluation screen which concerns on this Example.

<Example 1>
Hereinafter, examples of the present invention will be described with reference to figures and the like. The present invention is implemented for railways.

(Diamond evaluation device)
FIG. 1 is a diagram showing a configuration of a diamond evaluation device 1 of this embodiment.

The diamond evaluation device 1 is composed of a general computer system, and includes a central control device 11, an input device 12, an output device 13, a communication device 14, a main storage device 15, and an auxiliary storage device 16. These configurations are interconnected by buses.

The central control device 11 is a processor that executes a program stored in the main storage device 15. The input device 12 is a user interface (for example, a keyboard, a mouse, etc.) for the operator to input data and instructions to the timetable evaluation device 1. The output device 13 is a user interface (for example, a display, a printer, etc.) for presenting the execution result of the program to the user. The communication device 14 is a communication interface (for example, a NIC (Network Interface Card)) for transmitting and receiving information used by the timetable evaluation device 1 to and from the external system 2 and the external server 3 via the network 4.

The main storage device 15 includes a ROM which is a non-volatile storage element and a RAM which is a volatile storage element. The ROM stores an invariant program (for example, BIOS) and the like. The RAM is a high-speed and volatile storage element such as a DRAM (Dynamic Random Access Memory), and temporarily stores a program stored in the auxiliary storage device 16 and data used when executing the program. Specifically, the main storage device 15 stores a program for realizing the passenger flow matrix generation unit 21, the diamond evaluation unit 22, the non-normal information detection unit 23, and the passenger demand forecast unit 24. Hereinafter, the description mainly of the "○○ part" means that the central control device 11 reads the program from the auxiliary storage device 16, loads it into the main storage device 15, and executes the program to realize the function of the program. ..

The auxiliary storage device 16 is a large-capacity, non-volatile storage device such as a magnetic storage device (HDD) or a flash memory (SSD). Further, the auxiliary storage device 16 stores the program executed by the central control device 11 and the data used when the program is executed. Specifically, the auxiliary storage device 16 stores transportation user information 31, transportation operation information 32, movement pattern information 33, passenger flow matrix 34, congestion history data 35, non-normal information 36, and timetable evaluation result 37. Store.

The diamond evaluation device 1 is communicably connected to the external system 2 and the external server 3 via the network 4. In this embodiment, it is assumed that the congestion history data 35 and the transportation operation information 32 are acquired from the external system 2, but the present invention is not limited to these, and various information necessary for this embodiment can be obtained from the external system 2 and the external server 3. You may get it from.

The program executed by the central control device 11 is provided to the diamond evaluation device 1 via removable media (CD-ROM, flash memory, etc.) or the network 4, and is stored in the auxiliary storage device 16 which is a non-temporary storage medium. .. Therefore, the diamond evaluation device 1 may have an interface for reading data from the removable media.

The diamond evaluation device 1 of this embodiment is a computer system composed of physically one computer or a plurality of computers logically or physically configured, and is separate on the same computer. It may run on a thread or on a virtual computer built on multiple physical computer resources.

(Transportation user information)
FIG. 2A is a diagram showing a configuration example of the transportation user information 31. The transportation user information 31 is information indicating the usage history of the transportation by the user.

The transportation user information 31 includes an entry time column 101, an entry time column 102, a transportation column 103, an entry station column 104, and an entry station column 105. The entry time column 101 records the entry time when the user passes through the ticket gate and enters the station, and the entry time column 102 records the entry time when the user passes through the ticket gate and enters the station. .. In the transportation column 103, the name of the transportation used by the user is recorded. The name of the station where the user entered is recorded in the entry station column 104, and the name of the station where the user entered is stored in the entry station column 105.

(Transportation operation information)
FIG. 2B is a diagram showing a configuration example of the transportation operation information 32.

The transportation operation information 32 includes a transportation column 111 and a train arrival column 112 to 115. The train number is recorded in the transportation column 111. The arrival times of trains at each station are stored in the train arrival columns 112 to 115.

The transportation operation information 32 is information indicating the operation of the train. The transportation operation information 32 is information in which departure time and arrival information for each train station are recorded, and is usually referred to as a diamond. In the transportation operation information 32, the arrival information of the train may not be recorded, the departure time may be recorded, or both the departure time and the arrival time may be recorded. Further, the transportation operation information 32 includes information on the planned schedule, information on the actual schedule in which a delay has occurred from the planned schedule, the history of adjustment of the schedule performed in the past, and real-time operation acquired from the running position of the train. Includes information on diamonds, information on predicted diamonds that predict future schedules, and so on. Unless otherwise specified, the transportation operation information 32 may be stored for each route.

(Movement pattern information)
FIG. 2C is a diagram showing a configuration example of the movement pattern information 33. The movement pattern information 33 is information indicating the pattern of the movement route that the user can take.

The movement pattern information 33 includes an entry station column 121, an entry station column 122, a transportation column 123, a movement route column 124, and a reference travel time column 125. The name of the station where the user passed through the ticket gate and entered is recorded in the entry station column 121, and the name of the station where the user passed through the ticket gate and entered is recorded in the entry station column 122. In the transportation column 123, the name of the transportation used by the user is recorded, and in the travel route column 124, information on the route used corresponding to the pair of entry / exit stations (hereinafter referred to as OD) is recorded. In the reference travel time column 125, the reference boarding time on the travel route 124 is stored.

The movement route 124 may store a plurality of usage route information for one OD. For example, the optimum route information may be stored from the viewpoints of boarding time, number of transfers, train congestion (whether to sit), fare, and the like. Further, when the optimum movement route differs depending on the time zone, different movement pattern information 33 may be stored for each time zone.

The reference travel time 125 may be calculated from a timetable (planned timetable) in normal times, or may be calculated from a statistical value of boarding time of a transportation user in normal times. When the travel route 124 includes a transfer, the transfer time may also be included in the boarding time.

(Passenger flow matrix)
FIG. 3 is a diagram showing a configuration example of the passenger flow matrix 34. The passenger flow matrix 34 is information obtained by calculating the passenger flow at predetermined time intervals and forming a matrix. For example, the route, the time zone of the arrival time of the passenger's train at the boarding position, and the distance between each station are used for each entrance station. Information represented by the number of passengers. The boarding position indicates the position where the passenger actually gets on the vehicle, such as a station platform.

As shown in FIG. 3A, the passenger flow matrix 34 includes a passenger flow matrix classification column 301, a passenger flow matrix ID column 302, an entrance station column 303, and a matrix ID column 304. The passenger flow matrix classification column 301 records the classification of the passenger flow matrix, and classifies the passenger flow matrix according to, for example, a failure statistical value, a normal time statistical value, and a non-normal time data. The passenger flow matrix ID column 302 records the identification information of the passenger flow matrix, and refers to the passenger flow matrix from the recorded identification information. The entrance station is recorded in the entrance station column 303, and the identification information of the matrix for each time zone is recorded in the matrix ID column 304. The passenger flow matrix 34 may be recorded on a line-by-line basis.

Further, as shown in FIG. 3B, in the passenger number column 305, a numerical value or a mathematical model of the number of passengers between each station is stored in association with each matrix ID 304. For example, the matrix ID (M1) 311 is passenger flow information of a person who arrives at station A (the platform of the line to be boarded at station A) at 7:58, and eight people board from station A to station B and the station. Information on the number of people that 6 people continue to board from B to station C (2 people get off at station B) and 4 people continue to board from station C to station D (2 people get off at station C) is stored.

The matrix ID (M4) 312 is passenger flow information that arrived at station A at 7:59, and the number of passengers is modeled by a mathematical formula.

(Congestion history data)
FIG. 4A is a diagram showing a configuration example of congestion history data 35. The congestion history data 35 is information indicating an actual congestion rate for each predetermined time.

As shown in FIG. 4A, the congestion history data 35 includes a congestion history data classification column 401, a congestion history data ID column 402, a station-to-station column 403, and an average congestion rate column 404. The congestion history data classification column 401 records the classification of the congestion history data, and classifies the data according to, for example, normal statistics and non-normal data. The congestion history data ID column 402 records the identification information of the congestion history data, and refers to the congestion history data from the recorded identification information. In the station-to-station column 403, the distance between stations where trains move is recorded, and in the average congestion rate column 404, the average congestion rate for each time zone is recorded. The congestion history data 35 may be recorded for each route.

(Non-normal information)
FIG. 4B is a diagram showing a configuration example of the non-normal information 36. The non-normal time information 36 is information in which the passenger flow and the congestion rate in the non-normal time are associated with each other.

As shown in FIG. 4B, the non-normal information 36 includes a date column 411, a route column 412, a passenger flow matrix ID column 302, and a congestion history data ID column 402. In the date column 411, the date that was non-normal is recorded, and in the route column 412, the route name determined to be non-normal is recorded. In the passenger flow matrix ID column 302 and the congestion history data ID column 402, the passenger flow matrix ID and the congestion history data ID corresponding to the route name recorded in the route column 412 on the date recorded in the date column 411 are recorded. To.

FIG. 5 is a diagram showing the concept of the mathematical model of the matrix ID (M4) 312 shown in FIG. 3 (B). Each variable of the mathematical model will be described with reference to FIG. 5 with reference to FIG.

On the line shown in FIG. 5, each stop train 321 and the rapid train 322 are operating, and each stop train 321 waits for the rapid train 322 at the station A. In the boarding model 323 of station A, among the entrance passengers 324 including transfer passengers, the detour passengers 325 from other routes, and the entrance passengers 324 including transfer passengers, the detour passengers 326 and transfer passengers to other routes boarding other routes are selected. Of the 324 admission passengers including, the information of the 327 passengers transferring to the high speed to get on the high speed is modeled. Therefore, the boarding model 323 does not include passengers who boarded the local trains 321 at stations before station A. The number of admission passengers 324 including transfer passengers can be calculated from the normal passenger flow (for example, the passenger flow matrix classified as the normal statistical values shown in FIG. 3A), and 10 passengers in the illustrated example. (Constant). Further, the detour passenger 325 from another line was set as the explanatory variable a, the detour passenger 326 to the other line was set as the explanatory variable b, and the number of passengers transferring from each stopped train 321 to the rapid train 322 at the station A was set as the explanatory variable c.

In this mathematical model, the passengers between the next stations are represented by a probabilistic model, and for the passengers who moved from station A to station B, 82% of the passengers who boarded at station A continued to board until station C (18% of passengers). (Gets off at station B), 65% of the passengers who got on at station A continue to get on to station D (17% of passengers get off at station C).

The explanatory variables a and b indicating the detour passengers will be described using an example of two routes (routes X and Y). There are two routes for moving from station X to station Y: a route using line X and a route using line Y. The required time and the number of trains of the two travel routes are almost the same, and passengers select and board each of route X and route Y with a probability of 50%. At this time, assuming that there are 10 visitors in a certain time zone when moving from station X to station Y, 5 people will use line X and the remaining 5 people will use line Y in normal times. At this time, if a transportation failure occurs on the route X and the operation is completely stopped, all the passengers dispersed at 5: 5 use the route Y in order to move to the destination. Therefore, the number of users of Route X is 0, and the number of users of Route Y is 10. This means that the route on which the passengers board varies depending on the transportation capacity of the route X and the transportation capacity of the route Y. The transportation capacity is an index of how many passengers can be transported. If the transportation capacity at the time of abnormality is defined as a comparison with the normal time, the transportation capacity = (the number of trains operated per unit time in the timetable at the time of abnormality × the passenger capacity). ) / (Number of trains operated per unit time in normal timetable x boarding capacity).

FIG. 6 is a diagram showing the relationship between the transportation capacity of the above-mentioned routes X and Y and the number of detour passengers.

The vertical axis is the number of detour passengers to the Y route 331, and the horizontal axis is the transportation capacity 332 of the X route. FIG. 6 shows the number of detour passengers when the transport capacity (PY) 333 of the Y route is 100% and 50%. When the transport capacity of the X route is lower than the transport capacity of the Y route (X1 → Y1, X2 → Y1), the number of detour passengers to the Y route increases. The minimum value of the number of detour passengers is 0 (X1, X2), and the maximum value is the total number of passengers (Y1: constant) in which the detour route exists on the Y route.

Even in the passenger flow matrix generated for one route as described above, the number of passengers can be changed according to the situation by generating a model considering the operation status of other routes. The method of generating the passenger flow matrix will be described later.

(Diamond evaluation result)
Returning to FIG. 1, the explanation will be continued. The diamond evaluation result 37 is information obtained by the diamond evaluation by the diamond evaluation unit 22, and the details thereof will be described later.

(How to generate a passenger flow matrix)
FIG. 7 is a flowchart of a process in which the passenger flow matrix generation unit 21 generates the passenger flow matrix 34.

First, in step S201, the passenger flow matrix generation unit 21 accepts the designation of the extraction conditions. The extraction conditions are, for example, a date and a route name. This can be done even if the passenger flow matrix of the previous day is automatically generated for all routes for the daily transportation user history, or even if the operator of the timetable evaluation device 1 specifies arbitrary conditions and generates it. Good. Specifically, the passenger flow matrix generation unit 21 accepts the input of the date and the route name by the operation of the operator's input device 12. At this time, when generating a passenger flow matrix for a certain day, the dates for one day are specified, and when generating statistical data, the dates for a plurality of days are specified. The case where "January 1, 2014" and "○○ route" are input will be described below. In addition, instead of a date, it may be a specific time zone in a day, or a week or month including a plurality of days. Further, in addition to inputting the line, a section (between specific stations) in the line may be specified.

In step S202, the passenger flow matrix generation unit 21 extracts the target moving pattern. Specifically, using the input "○○ route" as a search key, all ODs including the XX route in the movement route are extracted from the movement pattern information 33.

In step S203, the passenger flow matrix generation unit 21 extracts the target transportation user information. Specifically, the information of the corresponding user is extracted from the transportation user information 31 using the date "January 1, 2014" specified in step S201 and the list of ODs extracted in step S202 as search keys. ..

In step S204, the passenger flow matrix generation unit 21 estimates the arrival time at the target route to be the transfer destination. The arrival time on the target route is, for example, the time when the user (passenger) arrives at the platform where the train on the target route arrives, and is calculated by adding the walking travel time from the ticket gate to the platform to the ticket gate entry time. (For example, if the journey to the platform is 1 minute, add 1 minute).

In the case of a trip that includes transfer of multiple routes, the arrival time is the transfer time to the target route that is the transfer destination. To estimate the transfer time, the standard travel time for the section from the entrance station to the transfer station is extracted from the movement pattern information 33, and at the ticket gate entry time, the walking travel time from the ticket gate to the platform, the standard travel time to the transfer station, and By adding the travel time between platforms at the time of transfer, the arrival time at the target route to be transferred can be estimated. However, even with the same OD, the travel route may differ depending on the passenger. Therefore, the route estimation of passengers will be described later in the extended function.

In step S205, the passenger flow matrix generation unit 21 refers to the transportation user information to which the travel time is added, and totals or models the boarding time of the target route and the number of people traveling between stations for each entrance station. Specifically, the number of passengers arriving at the target line in the same time zone is totaled to which station the passengers have moved to, and the number of passengers moving between stations or the passenger model is obtained.

The aggregation method will be described with reference to a specific example. As a result of estimating the arrival time of the user of line A in step S204, the line designated in step S201 is set as line A, and as a result, eight passengers arrived at station A between 8:00 and 8:01. .. From station A, you can use line A to move to station B, station C, and station D. Of the eight people who arrived, two moved to station B, two moved to station C, and four moved to station D. From this, when the number of passengers between stations is totaled, the number of passengers who have moved from station A to station B is 8, 6 from station B to station C, and 4 from station C to station D.

If the number of passengers between stations A and B, between stations B and C, and between stations C and D _{is defined by the array [NAB} N _BC N _CD ], they arrived at station A from 8:00 to 8:01. Passenger groups can be represented by the sequence [ _NAB N _BC N _CD ] = [8 64]. As a result, the data as shown in the matrix ID (M1) 311 of FIG. 3 (B) can be generated. In other words, the passenger flow matrix can also be calculated as the probability of continuing boarding, with N passengers arriving at station A as N people [ _NAB N _BC N _CD ] = N [1 (3/4) (1/2)). ]It can be expressed as.

In the above-mentioned example, the time width is totaled in 1 minute, but the time width may be totaled in a shorter or longer time width. The operator may arbitrarily set the time width to be totaled, or the operator may set the resolution according to the interval (time interval) of the arrival time of the train. For example, when the minimum time interval of a train is 2 minutes and the time resolution for the time interval is set to 2, the time width in units of 2 minutes / 2 = 1 minute may be totaled. Since the minimum time interval differs depending on the route and time zone, the total time width may be changed according to the route and time zone. The time width of aggregation affects the timetable evaluation processing speed described later. There is a trade-off between accuracy and processing speed. However, since the number of elements of the passenger flow matrix does not change even if the number of passengers increases, the evaluation accuracy and the evaluation processing speed desired by the user can be set by arbitrarily setting the time width. The minimum unit of the time width of aggregation is the minimum unit of admission time information, and if the admission time is known in units of 1 second, aggregation can be performed in units of 1 second at the minimum. In addition, the system may automatically set the resolution according to the interval (time interval) of the arrival time of the train. Further, the system may calculate and set the resolution by accepting the input of the evaluation accuracy and the evaluation processing speed.

In step S206, the passenger flow matrix generation unit 21 generates the passenger flow matrix 34 and stores the data in the diamond evaluation device 1. Specifically, an ID is assigned to each array generated in step S205, a station and time matrix as shown in FIG. 3B is generated, and stored in the auxiliary storage device 16 or the main storage device 15.

(Diamond evaluation method)
FIG. 8 is a flowchart of a process in which the diamond evaluation unit 22 evaluates a diamond.

In step S401, the diamond evaluation unit 22 receives the input of the diamond to be evaluated. Specifically, the diamond evaluation unit 22 receives input or selection of a route for evaluating the diamond and a section for evaluating the diamond (for example, between stations) by operating the input device 12 of the operator (see FIG. 16). It should be noted that the designation of the time range for evaluating the diamond (for example, the diamond evaluation range 912 shown in FIG. 16) may be accepted. The timetable is, for example, transportation operation information 32, such as a past actual timetable, a real-time operation timetable, and a future prediction timetable. The diamond data may be acquired from the external system 2 or the external server 3 by the network 4 via the communication device 14. Further, the input diamond may be stored in the auxiliary storage device 16.

In step S402, the diamond evaluation unit 22 extracts the passenger flow matrix 34 of the received diamond. Specifically, the passenger flow matrix 34 of the route and section input or selected in step S401 is extracted. The passenger flow matrix 34 is generated by the passenger flow matrix generation unit 21 by batch processing or the like, and is stored in the auxiliary storage device 16. The method of selecting the optimum passenger flow matrix 34 when evaluating the diamond in real time will be described in the passenger demand forecasting unit 24 (FIG. 13), and the description thereof will be omitted here.

In step S403, the timetable evaluation unit 22 creates a passenger boarding pattern. The diamond evaluation unit 22 creates a passenger boarding pattern using the input diamond and the extracted passenger flow matrix 34.

FIG. 9 is a diagram showing an example of the input diamond. The data obtained from the input diamond will be described with reference to FIG.

The timetable shown in FIG. 9 is the train operation information of the line A501, and specifically, the operation information of the train A502, the train B503, and the train C504. The train arrival interval at each station (for example, the train arrival interval of train A502 and train B503) is the time interval 505. The time it takes for these trains to arrive at the next station is 506. In the timetable shown in FIG. 9, train A502 is suspended after arriving at station B. If the train is suspended at a station on the way, passengers will transfer to the next train at the suspended station.

FIG. 10 is a diagram showing a configuration example of a passenger boarding pattern. The passenger boarding pattern is information indicating the timing at which passengers board the train. A passenger boarding pattern is created for each train and includes boarding by transfer. For example, the passenger boarding pattern 601 shown in FIG. 10A is a passenger boarding pattern of the train A502 shown in FIG. The passenger boarding pattern 601 includes a stop station 611, a train arrival time 612, a travel time to the next station 613, a time interval 614 from the previous train, a time interval 615 from the next train, a matrix ID 616, and a passenger waiting time 617. This information can be obtained from the input diamond.

The matrix ID 616 is referred to from the passenger flow matrix 34 using the train arrival time 612 and the time interval 614 from the previous train. Specifically, when the train arrival time 612 at station A is 7:59 and the time interval 614 from the previous train is 2 minutes, the matrix ID 304 at station A from 7:58 to 7:59 is referred to. .. FIG. 10A shows an example in which the total time width of the passenger flow matrix 34 is 1 minute, and the matrix ID (M1) at 7:58 at station A and 7:59 at station A. Two of the matrix IDs (M4) of the above are referred to as passenger boarding data at station A of train A502. Further, the matrix ID (M5) at 7:59 at station B and the matrix ID (M8) at 8:00 at station B are referred to as passenger boarding data at station B of train A502.

Since the train A502 has stopped operating at station B, the passenger flow matrix at station C next to station B becomes transfer (N1) 618.

As shown in FIG. 10B, the passenger boarding pattern 602 for passengers transferring from train A502 to train B503 moves to a station ahead of station B with respect to the matrix M1 and M4 of passengers boarding train A from station A. It can be represented by the matrix ID (N1) 621, which is the sum of the passengers who were supposed to do so and the matrices M5 and M8 of the passengers who were supposed to get on the train A from the station B. The matrices M5 and M8 are information stored in the passenger flow matrix 34 shown in FIG. 3B, similarly to the matrices M1 and M4.

The passenger boarding pattern 603 shown in FIG. 10 (C) is a passenger boarding pattern of the train B503 shown in FIG. The passenger boarding pattern 603 includes a stop station 631, a train arrival time 632, a travel time to the next station 633, a time interval 634 from the previous train, a time interval 635 from the next train, a matrix ID 636, and a passenger waiting time 637. As shown in FIG. 10 (C), the passenger boarding pattern 603 of train B503, in which passengers transfer from train A502, board the matrix M7 and M10 of passengers boarding train B from station A and train B from station B. Includes passenger matrices M11, M14 and passenger matrix (N1) 621 transferring from train A502 to train B503 at station B.

The passenger waiting time 637 is a waiting time for each matrix ID, and the waiting time is added to the matrix ID at a time before the train arrival time 632, with the train arrival time 632 as 0 minutes (FIGS. 10A and 10C). ), The passenger flow is totaled in 1-minute units, and the waiting time increases in 1-minute units). As shown in FIG. 9, when train A is suspended, transfer between trains occurs on the same line. When such a transfer occurs, passenger data may be inherited from the previous train (in the example shown in FIG. 10C, the matrix ID (N1) becomes the passenger data for transferring from train A, and the train A which is the previous train Passenger waiting time with the previous train at station B 617 (1 minute (passenger waiting time corresponding to M1) + 1 minute (passenger waiting time corresponding to M5) = 2 minutes) is waiting for passengers corresponding to the matrix ID (N1) It will be taken over at time 637).

As described above, by assigning the matrix ID generated in advance to the input diamond, the passenger boarding pattern can be created, and the processing can be performed at a higher speed than calculating the boarding train for each passenger.

Returning to FIG. 8, the description will be continued. In step S404, the diamond evaluation unit 22 calculates the non-utility value of the passenger. Specifically, first, the first diamond evaluation result (train) 701 shown in FIG. 11 (A) is created, and then the diamond evaluation results (train) 701 are aggregated and shown in FIG. 11 (B). The diamond evaluation result (overall) 711 is calculated. The ineffectiveness is an index showing the degree of dissatisfaction / distress suffered by passengers. For example, when the degree of vehicle congestion is high, the travel time is increased, or the like, the ineffectiveness value becomes large. The utility value is an index for evaluating the train schedule from the user's point of view, and is a value obtained by converting the waiting time, the transfer time, the number of transfers, the congestion rate, etc. as the increase in the boarding time. In this way, the utility value can represent the disadvantage of the passenger. Generally, the utility value is an index showing the impact on passengers, but the index showing the impact on employees and the environment may be defined as the employee utility value and the environmental utility value, respectively.

The first timetable evaluation result (train) 701 uses the passenger boarding pattern created in step S403 to transport transportation 702, between stations 703, number of passengers 704, congestion degree 705, total waiting time 706, and total to the next station. It is generated as an evaluation result including the increased boarding time 707 and the number of transferees 708.

Transport 702 is, for example, a train number. Station-to-station 703 is information between two adjacent stations on which a train operates. The number of passengers 704 is the result of totaling the number of passengers on the table shown in FIG. 3B for all matrix IDs associated with the train between each station.

The congestion degree 705 is the congestion degree of the train, and is, for example, the sum of the number of passengers with respect to the passenger capacity of each vehicle forming the train. For example, if 120 passengers are on a train with a capacity of 1200 passengers, the congestion degree 705 is 10%.

The total waiting time 706 is the total value of the waiting time in the platform from the arrival of passengers on the platform where the route departs and arrives to the boarding of the train. For example, in the case of moving from station A to station B, the total value is calculated by multiplying the passenger waiting time 617 for each entrance time zone and the number of visitors for the passengers who entered at station A.

The total increased boarding time 707 to the next station is the total value of the increase in the passenger's travel time from the reference travel time 125. Specifically, if the standard travel time 125 from station A to station B is 2 minutes, the actual travel time is 3 minutes, and the number of passengers is 60, the total increased boarding time 707 from station A to station B is , (3 minutes-2 minutes) x 60 = 60 minutes (1 hour).

The number of passengers to transfer 708 is, for example, the number of passengers who disembark the suspended train and transfer to the next train when the train is suspended in the middle, as in the train A502 shown in FIG. Specifically, in the train A502 shown in FIG. 9, 360 passengers move from station B to station C, and when train A502 is suspended at station B, 360 passengers move from station B to station C. It is counted as the number of transfer passengers. In addition, transfer to a rapid train or waiting for the next train when the number of passengers exceeds the passenger capacity may be treated as a transfer.

The second timetable evaluation result (overall) 711 includes, for example, a congested section 712, a total waiting time 713, a total increased boarding time 714, a number of transferees 715, and a total utility value 716 that combines these, and the timetable evaluation result ( This is the result of totaling 701 (train).

The congested section 712 is the number of sections in which the passing trains are congested, with the passage of one train as one section in all the operating sections (between adjacent stations) of a certain line. Specifically, it is determined from the congestion degree 705 recorded in the diamond evaluation result (train) 701 whether or not the train is congested when passing through each section. For example, if the congestion rate is 100% or more, it is determined that the area is congested, and the number of sections having a congestion rate of 100% or more is totaled.

The total waiting time 713 is the result of totaling the total waiting time 706 calculated for each train and each section in the timetable evaluation result (train) 701 for all trains and sections.

The total increased boarding time 714 is the result of totaling the total increased boarding time 707 to the next station calculated for each train and section in the timetable evaluation result (train) 701 for all trains and sections.

The number of transferees 715 is the result of totaling the number of passengers 708 calculated for each train and each section in the timetable evaluation result (train) 701 for all trains and sections.

The total non-utility value 716 is a total of the above-mentioned non-utility values as one index. This is the result of multiplying the congested section, the number of people transferring, etc. by a predetermined coefficient to convert the time, and totaling all the utility values as an index of time. Specifically, one section of the congested section is set as an increased boarding time of 1 minute, and one transfer is set as an increased boarding time of 5 minutes, and the time is converted by multiplying by a predetermined coefficient.

The diamond evaluation result (train) 701 and the diamond evaluation result (overall) 711 described above are stored in the auxiliary storage device 16 as the diamond evaluation result 37.

Returning to FIG. 8, the description will be continued. In step S405, the diamond evaluation unit 22 displays the evaluation result on the screen. The contents of the screen will be explained in detail below, and the explanation of the diamond evaluation process ends here.

(Non-normal detection unit)
FIG. 12 is a flowchart of a process in which the non-normal time information detection unit 23 creates non-normal time information, and is assumed to be executed as a batch process after the end of one-day transportation operation.

In step S601, the non-normal information detection unit 23 receives input of train congestion, operation schedule, and transportation user information. Specifically, the non-normal information detection unit 23 acquires one day's worth of train congestion, operation schedule, and transportation user information by operating the operator's input device 12. Here, the external system 2 is, for example, a train operation management system, a system for aggregating load-bearing data (congestion degree of each vehicle), an IC card ticket history aggregating system, and the like. The congestion degree of the train is, for example, congestion history data 35, and the operation schedule is, for example, transportation operation information 32, such as a past actual schedule or a planned schedule. The transportation user information is, for example, transportation user information 31.

In step S602, the non-normal information detection unit 23 determines whether or not a transportation failure has occurred. Specifically, the presence or absence of a transportation failure is determined from the operation schedule acquired in step S601. The operation schedule includes, for example, a planned schedule and an actual schedule for the day. By using the planned timetable and the actual timetable, the delay time can be calculated from the difference between the planned and actual time for the departure and arrival times at each station. In addition, it can be seen that the train was suspended if the planned train was not actually operated. In the event of a transportation failure, suspension or delay will always occur. Therefore, it is possible to determine whether or not a transportation failure has occurred based on a delay of an arbitrary threshold value or more, for example, 10 minutes or more, or the presence or absence of suspension. By comparing the planned timetable and the actual timetable, it is possible to obtain information such as the failure occurrence route, the failure occurrence station, the failure occurrence time, and the failure end time.

When it is determined in step S602 that a transportation failure has occurred, in step S603, the non-normal information detection unit 23 updates the failure statistics value of the passenger flow matrix 34, and the passenger flow at the time of transportation failure. The matrix 34 is used. Specifically, when a transportation failure has occurred, the mathematical model as shown in FIG. 3B is updated by using the transportation user information 31 at the time of the transportation failure.

When it is determined in step S602 that no transportation failure has occurred, in step S604, the non-normal information detection unit 23 determines whether the degree of congestion of the train is at a normal level. Specifically, the normal statistical value of the train congestion degree and the current day value are compared, and if the difference is equal to or more than the threshold value, it is judged as non-normal. That is, if there is no transportation failure but the train congestion is higher than in normal times, it is regarded as non-normal and the subsequent processing is executed. For the normal time statistical value, for example, the median value or the average value of the past data is used and recorded in the congestion history data 35.

When the degree of congestion of the train is determined to be a normal level in step S604, the non-normal information detection unit 23 updates the normal statistical values of the congestion history data 35 and the passenger flow matrix 34 in step S605.

When it is determined in step S604 that the degree of congestion of the train is not at the normal level, in steps S606 to S609, the non-normal information detection unit 23 creates the congestion history data 35 and the non-normal data of the passenger flow matrix 34. Then, it is saved as non-normal information 36.

(Passenger Demand Forecasting Department)
FIG. 13 is a flowchart of a process for predicting passenger demand by the passenger demand forecasting unit 24, which is assumed to be executed in real time.

In step S801, the passenger demand forecasting unit 24 acquires the latest train congestion level and operation schedule at the present time, and determines in step S802 whether a transportation failure has occurred. Since the specific method is the same as S601 and S602 even when it is executed in real time as in this process, the description here will be omitted.

When it is determined in step S802 that no transportation failure has occurred, in steps S803 and S804, the passenger demand forecasting unit 24 compares the latest train congestion degree and congestion history data at the present time in real time, and makes a difference. Is calculated.

FIG. 14 is a diagram showing an example of real-time comparison of train congestion degree and congestion history data. A specific method for real-time comparison will be described with reference to FIG.

In FIG. 14 (A), the vertical axis is the train congestion degree 801 and the horizontal axis is the time 802, and there are three times of the day data 803 (solid line), comparison data A804 (dashed line 1), and comparison data B805 (dashed line 2). An example of comparing series data is shown. Here, the comparison data A804 and the comparison data B805 are, for example, normal statistical values recorded in the congestion history data 35 or past non-normal data. The data before the current time 806 is compared in real time by calculating the difference between the current day data 803 and the comparison data.

FIG. 14B shows an example of the comparison result between the day data 803 and the comparison data. FIG. 14B includes a comparison target column 811, a time zone column 812, a congestion degree difference column 813 with the current day data, and a weight column 814. In the time zone column 812, for example, time interval information divided at intervals of -15 minutes from the current time 806 is recorded. Specifically, when the current time 806 is 8:15, the time zone of the first line is the time section from 8:00 to 8:15. The degree of congestion is compared, for example, by the average degree of congestion from 8:00 to 8:15. To give one example, when the average congestion degree of the day data 803 from 8:00 to 8:15 is 100% and the average congestion degree of the comparison data A804 is 115%, the congestion degree difference 813 from the day data is (115). -100) / 100 = 15%. A weight 814 may be added to the congestion degree difference 813 from the calculated day data. This is because it is assumed that the difference of several minutes ago and the difference of several hours ago have different influences on the prediction. As shown in FIG. 14B, the weight 814 may be increased as the time zone 812 is closer to the current time 806, and the final difference value may be obtained as the weighted average 815 of the congestion degree difference. Then, in step S805, among the comparison data, the comparison data of the day on which the weighted average of the congestion degree difference is the minimum is specified, and the passenger flow matrix corresponding to the specified day is selected.

Returning to FIG. 13, the description will be continued. When it is determined in step S802 that a transportation failure has occurred, in step S806, the passenger demand forecasting unit 24 selects the failure statistical value of the passenger flow matrix 34, and the passenger flow matrix at the time of transportation failure. It is set to 34. Specifically, a mathematical model as shown in FIG. 3B, which models the passenger flow at the time of transportation failure, is selected.

In step S807, the passenger demand forecasting unit 24 acquires the operation reorganization plan schedule. The operation reorganization plan timetable is information on the change schedule of the timetable such as suspension arrangements to be carried out for transportation obstacles and return operation arrangements, and is acquired in a format such as transportation operation information 32, for example. The timetable is changed assuming the scale of the transportation failure in advance. For example, in the case of a personal injury, the operation is planned with an expectation of about 1 hour until recovery.

In step S808, the passenger demand forecasting unit 24 inputs the operation arrangement feature amount into the passenger flow matrix 34. The operation reorganization feature amount is information extracted from the operation reorganization plan timetable, for example, the number of suspensions and delays. By using the operation control feature amount, for example, when the number of suspensions increases and the transportation capacity of the transportation obstruction route decreases, the user can utilize the prediction model of detouring to another route.

In step S809, the passenger demand forecasting unit 24 predicts passenger demand from the selected passenger flow matrix 34. Specifically, in step S402 of the diamond evaluation unit 22, the passenger flow matrix 34 to be extracted is optimized. As described above, it is possible to select an appropriate passenger flow matrix 34 in each of the non-normal times and the time of transportation failure, and it is possible to perform diamond evaluation with high accuracy.

(System screen)
The diamond evaluation device 1 outputs a screen for inputting conditions for the operator to generate a passenger flow matrix and evaluate the timetable, a screen for displaying the timetable evaluation result, and the like. These screens are generated by the passenger flow matrix generation unit 21 and the diamond evaluation unit 22. An example of a specific screen will be described below.

(System screen 1: Menu screen)
FIG. 15 is a diagram showing an example of the menu screen 61.

The menu screen 61 includes an item of the offline menu 901 and an item of the real-time timetable evaluation 906.

The offline menu 901 includes a passenger flow data creation button 902, a timetable registration button 903, a registration timetable evaluation button 904, and an evaluation result confirmation button 905. The operator can select each process by operating each button.

The passenger flow data creation button 902 is a button operated to create the passenger flow matrix 34 (for example, FIG. 3) described above. When the operator operates the passenger flow data creation button 902, the process shown in FIG. 7 or 12 is executed, and a screen (not shown) for selecting a date, a route, or the like is displayed. Since the passenger flow matrix 34 is created from the history of transportation users, the dates that can be selected on the selection screen are past with the history of transportation users. When the date and route are selected, the passenger flow matrix 34 is generated by batch processing and stored in the auxiliary storage device 16.

The diamond registration button 903 is a button operated to register a diamond (for example, FIG. 2B). When the diamond registration button 903 is operated, the diamond data input screen (not shown) is displayed. On the diamond data input screen, the diamond is input in a predetermined format (for example, csv format, text format, etc.), or a diamond stored by the external server 3 connected by the network 4 via the communication device 14 is selected. , May be obtained. When the timetable registration is executed, the timetable (transportation operation information) is stored in the auxiliary storage device 16.

The registration diamond evaluation button 904 is a button operated to evaluate the diamond registered by the operation of the diamond registration button 903. When the registration diamond evaluation button 904 is operated, the process shown in FIG. 8 is executed and the diamond evaluation screen is displayed. The diamond evaluation screen will be described later.

The evaluation result confirmation button 905 is a button operated to display the diamond evaluated in the past. When the evaluation result confirmation button 905 is operated, a screen (not shown) for selecting the diamond evaluation result 37 stored in the auxiliary storage device 16 is displayed. When the diamond evaluation result 37 is selected, the diamond evaluation screen is displayed. The diamond evaluation screen will be described later.

The real-time timetable evaluation 906 is a button operated to select a route to be evaluated and a section for evaluating the timetable. The route to be evaluated is selected in the evaluation route selection column 907. In addition, the section (two stations) to be evaluated in the route selected in the evaluation section selection field 908 is selected. If the entire section of the route is to be evaluated, it is not necessary to provide the evaluation section selection column 908. When the execution button (not shown) is pressed after these are selected, the process of FIG. 13 is executed and the diamond evaluation screen is displayed. The diamond evaluation screen will be described later. In the above description, one process is executed when a certain button is operated, but all the processes shown in FIGS. 7, 8, 12, and 13 are executed, or a part of them is executed. You may.

(System screen 2: Diamond evaluation screen)
FIG. 16 is a diagram showing an example of a diamond evaluation screen.

The diamond evaluation screen 62 is an example of a screen displayed when evaluating a diamond in real time. The input diamond (diamond streak) 911 is displayed on the diamond evaluation screen 62, and by designating the diamond evaluation range 912, the diamond in an arbitrary time range can be evaluated. The diamond evaluation range 912 may not be specified on the diamond evaluation screen 62, and an input field for the diamond evaluation range 912 (evaluation start time, evaluation end time) may be provided on the menu screen 61.

The average train congestion level 913 is a comparison of how much the train congestion level on the day is compared to the normal time, and displays the time-series change of the normal time statistical value 914 and the current day data 915 up to the current time. To do.

The passenger demand forecast result 916 is a passenger demand forecast result predicted using the passenger flow matrix 34, and is, for example, a time-series change in the number of station visitors on a certain line. The time-series changes of the normal statistical value 917 and the predicted value 918 after the current time are displayed.

The diamond evaluation result 919 is the evaluation result of the diamond in the diamond evaluation range 912, and when the diamond evaluation range 912 is changed, the diamond evaluation result is calculated and the diamond evaluation result 919 is updated. Further, for each item of the timetable evaluation result 919, the time series change may be displayed for each station or station-to-station (section). Therefore, the utility value of passengers is totaled for each station and each station.

The screen for displaying the result of evaluating the diamond in real time has been described above, but the evaluation result can be displayed on the same screen when evaluating the diamond registered offline.

(Extended function 1: Improvement of diamond evaluation accuracy)
The passenger flow matrix 34 described above is generated by allocating a movement route to the transportation user information 31 with reference to the movement pattern information 33 and estimating the route to be used. At this time, the accuracy of the movement route allocation affects the accuracy of the diamond evaluation. That is, in order to improve the accuracy of diamond evaluation, it is an issue to improve the accuracy of moving route estimation. The solution to the problem will be described below.

(Extended function 2-1: Estimating the travel route of transportation users)
A method of assigning a movement route from the movement pattern information 33 to the transportation user information 31 when there are a plurality of movement route candidates between stations will be described.

Passengers decide their travel routes according to their priorities based on multiple conditions such as boarding time, number of transfers, train congestion (whether they can sit), and fares. For example, some passengers choose a travel route with few transfers even if the boarding time is 5 minutes longer. Therefore, all the candidates for the travel route expected to be used by the transportation user are registered in advance in the travel pattern information 33, and the registered travel route is appropriately selected to accurately estimate the travel route. Can be done. At this time, information on the time required for each travel route is also registered. Then, the required time of the registered travel route is compared with the actual required time of the transportation user, and the travel route having the smallest difference in required time is assigned.

In addition, the arrival time of the train at the participating station on the assigned travel route (route) is compared with the entry time of the transportation user, and the travel route with a small difference between the arrival time of the train and the entry time is assigned. Therefore, the accuracy of estimation can be further improved.

However, among the movement route candidates, there may be a plurality of movement routes with almost the same required time. The solution to this problem will be described later.

(Extended function 2-2: Creation of transportation user information with weighting factor)
A method of assigning a travel route from the travel pattern information 33 to the transportation user information 31 when there are a plurality of travel route candidates between stations and it is difficult to estimate the travel route from the time required by the transportation user. Will be described.

When the movement route cannot be selected by using the extension function 2-1 described above, the selection probability of the candidate movement route is calculated. The movement route selection probability is the probability that a passenger selects a movement route in a plurality of movement routes. For example, if the travel route using Route A has 30 routes per hour and the travel route using Route B has 20 routes per hour even if the required time is the same, the selection probability of Route A is It can be calculated that 30/50 = 0.6 and the selection probability of the route B is 20/50 = 0.4.

Then, using the calculated movement route selection probability, a plurality of weighted traffic user information having different movement route information is created from the movement history of a certain person.

In the above example, it is estimated that 0.6 people use line A (weight coefficient 0.6) and 0.4 people use line B (weight coefficient 0.4). Therefore, even if the travel route cannot be specified, the passenger's travel route can be assigned without being biased to some of the travel routes, and a passenger flow matrix that matches the actual situation can be generated from a macro perspective.

(Extended function 2-3: Creating a detour model for transportation users)
A method of creating a model of detour passengers from other routes and detour passengers to other routes shown in FIG. 5 will be described.

The presence or absence of a detour route is registered in advance in the movement pattern information 33. For example, for a travel route with the shortest required time, a travel route using another route within 30 minutes of increasing the required time is registered as a detour route. In the case of travel including transfer, register a detour route for each route to be used.

A detour route is a route that passengers use as a substitute for the original travel route when a certain route is suspended due to a transportation obstacle or disaster. Therefore, whether to use the detour route can be created as a model determined by the transportation capacity of the original route and the transportation capacity of the detour destination route, as shown in FIG.

In step S205, the passenger flow matrix generation unit 21 determines whether or not there is a detour route when totaling the number of passengers between each station, and for passengers having a detour route, the detour route (other route) depends on the transportation capacity. Model as a passenger detouring to. In addition, the detour destination route will be modeled in the same way as passengers who have detoured from other routes.

(Effect of Examples)
(1) The timetable evaluation device 1 of this embodiment estimates the arrival time of passengers at the boarding position of the train, and is represented by the number of passengers using between each station for each route, time zone, and entrance station. Is generated, the number of passengers in the transportation system is estimated using the passenger flow matrix and the diamond, and the invalidity value of the passengers is calculated. Therefore, the diamond can be evaluated at high speed in consideration of the invalidity of the passengers. Therefore, it is possible to quickly create a diamond that has little impact on passengers in the event of a transportation failure or disaster.

(2) The timetable evaluation device 1 of the present embodiment uses a probabilistic model consisting of the number of passengers between a certain station, the number of passengers between the stations in front of the station, and the probability that the passengers will continue to board between the next stations. Therefore, even if the number of passengers boarding at a certain station fluctuates due to the modification of the timetable, the invalidity value of the passengers can be calculated at high speed.

(3) The timetable evaluation device 1 of the present embodiment evaluates the transportation capacity of each line and models the detour of passengers with respect to the relative transportation capacity between each route within the elements of the passenger flow matrix. A more accurate passenger flow matrix can be generated by considering the detour. Therefore, diamond evaluation can be performed with high accuracy.

(4) The timetable evaluation device 1 of this embodiment uses the difference between the congestion degree of the train and the congestion history data acquired in real time for the passenger demand fluctuating due to transportation obstacles, events, bad weather, etc. Accurate passenger demand can be predicted by grasping and selecting the passenger flow matrix that is most suitable for the situation of the day. Therefore, diamond evaluation can be performed with high accuracy.

(5) The timetable evaluation device 1 of this embodiment calculates a passenger's invalidity value using at least one of the number of congested sections of the vehicle, the total waiting time of passengers, the increase value of the total boarding time, and the number of transfer passengers. Therefore, the impact on passengers can be evaluated from various perspectives.

(6) The diamond evaluation device 1 of the present embodiment maintains the accuracy of the timetable evaluation by making the time width for totaling the number of passengers variable according to the conditions such as the time interval of the train when the passenger flow matrix is generated. As it is, the diamond can be evaluated at a higher speed.

(7) When the timetable evaluation device 1 of the present embodiment has a plurality of movement routes in one OD, the entrance station, entrance time, entry station, entry time, and required of the transportation user are required when the passenger flow matrix is generated. A more accurate passenger flow matrix can be generated by estimating the route to be used using at least one of the times. Therefore, the diamond can be evaluated with high accuracy.

(8) When there are a plurality of movement routes in one OD, the timetable evaluation device 1 of the present embodiment generates the passenger movement data of each of the multiple movement routes by multiplying the movement data of one passenger by the route selection probability. Therefore, a more accurate passenger flow matrix can be generated. Therefore, the diamond can be evaluated with high accuracy.

The present invention is not limited to the above-described embodiment, and includes various modifications and equivalent configurations within the scope of the attached claims. For example, the above-described examples have been described in detail in order to explain the present invention in an easy-to-understand manner, and the present invention is not necessarily limited to those having all the described configurations. Further, a part of the configuration of one embodiment may be replaced with the configuration of another embodiment. Further, the configuration of another embodiment may be added to the configuration of one embodiment. In addition, other configurations may be added / deleted / replaced with respect to a part of the configurations of each embodiment.

Further, each of the above-described configurations, functions, processing units, processing means, etc. may be realized by hardware by designing a part or all of them by, for example, an integrated circuit, and the processor realizes each function. It may be realized by software by interpreting and executing the program to be executed.

Information such as programs, tables, and files that realize each function can be stored in a memory, a hard disk, a storage device such as an SSD (Solid State Drive), or a recording medium such as an IC card, an SD card, or a DVD.

In addition, the control lines and information lines indicate those that are considered necessary for explanation, and do not necessarily indicate all the control lines and information lines necessary for implementation. In practice, it can be considered that almost all configurations are interconnected.

1 Diamond evaluation device 2 External system 3 External server 4 Network 11 Central control device (control unit)
12 Input device 13 Output device 14 Communication device 15 Main storage device (storage unit)
16 Auxiliary storage device (storage unit)
21 Passenger flow matrix generation unit 22 Diamond evaluation unit 23 Non-normal information detection unit 24 Passenger demand prediction unit 31 Transportation user information 32 Transportation operation information 33 Movement pattern information 34 Passenger flow matrix 35 Congestion history data 36 Non-normal information 37 Diamond evaluation result.

Claims (6)

It is an evaluation system that evaluates railway operation information.
A storage unit that stores transportation user information that records the history of passengers using the railway and travel pattern information that records the travel route between the entrance station and the exit station.
With reference to the transportation user information and the movement pattern information, the arrival time of passengers at the boarding position of the train is estimated, and it is represented by the time zone of the arrival time and the number of passengers using between each station for each entrance station. Passenger flow matrix generator that generates passenger flow matrix
An evaluation unit that estimates the number of passengers on the train using the passenger flow matrix and transportation operation information indicating the operation of the train, and calculates the utility value of the passengers.
With reference to the congestion degree of the train and the transportation operation information, it is determined whether the train is in a transportation failure, a normal time, or a non-normal time in which the train congestion is higher than in the normal time, and it is a transportation failure. If it is determined that it is a transportation failure, a passenger flow matrix is generated, if it is determined that it is normal, a passenger flow matrix is generated in normal time, and if it is determined that it is non-normal, it is non-normal. A non-normal information detector that generates a passenger flow matrix of time,
The occurrence of transportation failure is detected by referring to the congestion degree of the train acquired in real time and the transportation operation information, and when the occurrence of transportation failure is detected, the passenger flow matrix at the time of the transportation failure is selected and the transportation failure occurs. When the occurrence is not detected , the passenger flow matrix on the day when the difference between the congestion degree of the train and the congestion degree indicated by the past congestion history data is the smallest in real time is the passenger flow matrix in normal times or the passengers in non-normal times. Passenger demand forecasting department to select from the flow matrix,
An evaluation system characterized by being equipped with. When the occurrence of the transportation failure is not detected, the passenger demand forecasting unit weights the difference more as it gets closer to the current time, and sets the passenger flow matrix on the day when the weighted average of the weighted difference is the smallest in the normal time. Select from the passenger flow matrix or the non-normal passenger flow matrix,
The evaluation system according to claim 1, wherein the evaluation system is characterized in that. When the delay time obtained from the difference between the planned transportation operation information and the actual result of the transportation operation information is equal to or greater than the threshold value, the passenger flow matrix generation unit determines that the transportation failure has occurred. The delay time obtained from the difference between the planned transportation operation information and the actual result of the transportation operation information is not equal to or greater than the threshold value, and the congestion degree information of the train is equal to or greater than the threshold value as compared with normal times. In some cases, it is judged to be non-normal,
The evaluation system according to claim 1, wherein the evaluation system is characterized in that. It is an evaluation method that evaluates railway operation information using a computer.
The computer has a processor that executes a program and a memory that stores the program.
The memory stores transportation user information in which the history of passengers using the railway is recorded, and travel pattern information in which the travel route between the entrance station and the exit station is recorded.
The method is
The processor estimates the arrival time of the passenger at the boarding position of the train by referring to the transportation user information and the movement pattern information, and the passenger who uses the time zone of the arrival time and between each station for each entrance station. A passenger flow matrix generation step that generates a passenger flow matrix represented by a number and stores it in the memory, and
An evaluation step in which the processor estimates the number of passengers on the train using the passenger flow matrix and transportation operation information indicating the operation of the train, and calculates the utility value of the passengers.
The processor refers to the congestion degree of the train and the transportation operation information, determines whether the train is congested during a transportation failure, in normal times, or in non-normal times when the congestion degree of the train is higher than in normal times, and transports the train. When it is determined that there is a failure, a passenger flow matrix at the time of transportation failure is generated, and when it is determined that it is normal, a passenger flow matrix at normal time is generated, and when it is determined that it is non-normal. In the non-normal information detection step, which generates a non-normal passenger flow matrix,
The processor detects the occurrence of a transportation failure by referring to the congestion degree of the train acquired in real time and the transportation operation information, and when the occurrence of a transportation failure is detected, the passenger flow matrix at the time of the transportation failure is selected. However, when the occurrence of a transportation failure is not detected, the passenger flow matrix on the day when the difference between the congestion degree of the train and the congestion degree indicated by the past congestion history data is the smallest in real time is the passenger flow matrix in normal times or the non-congestion degree. An evaluation method characterized by including a passenger demand prediction step selected from a passenger flow matrix in normal times. In the passenger demand forecasting step, when the occurrence of the transportation failure is not detected, the difference is weighted more as it is closer to the current time, and the passenger flow matrix on the day with the smallest weighted average of the weighted difference is used in the normal time. Select from the passenger flow matrix or the non-normal passenger flow matrix,
The evaluation method according to claim 4, wherein the evaluation method is characterized by the above. In the passenger flow matrix generation step, when the delay time obtained from the difference between the planned transportation operation information and the actual result of the transportation operation information is equal to or greater than the threshold value, it is determined that the transportation failure has occurred. The delay time obtained from the difference between the planned transportation operation information and the actual result of the transportation operation information is not equal to or greater than the threshold value, and the congestion degree information of the train is equal to or greater than the threshold value as compared with normal times. In some cases, it is judged to be non-normal,
The evaluation method according to claim 4, wherein the evaluation method is characterized by the above.

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