Vehicle operation control system, vehicle control system, and vehicle operation control method

Abstract

To improve the risk and cost of railway lines in a vehicle operation control system.SOLUTION: A vehicle operation control system includes a load estimation target calculation unit which acquires a prospective load distribution of multiple railway-formation vehicles, changes a current operational load estimation so as to suppress the load distribution while the load is reaching a target load, calculates a post operational-change load estimation after the operational change, and plans the allocation of the multiple railway-formation vehicles based on the post operational-change load estimation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a vehicle operations management system, a vehicle control system, and a vehicle operations management method. [Background technology]

[0002] Among public transportation systems, railways excel in mass transportation and punctuality, and are used by many people every day. For example, it is said that more than half of the travel demand in Japan is handled by railways. For this reason, if a malfunction occurs in a railway vehicle and operation is suspended, not only will it result in a significant loss of transportation revenue for the railway company, but railway users will also incur a significant economic loss. Therefore, railway companies are concerned about how to prevent malfunctions in their railway vehicles.

[0003] To prevent railway vehicle malfunctions, railway companies have traditionally created and operated train operation plans to distribute the load evenly across train formations. A train formation is a group of multiple train cars joined together; for example, six train cars may be joined together and operated as a single train. This is the type of train formation we see on station platforms today.

[0004] One method for equalizing the load on each train set is described in Patent Document 1 below. Patent Document 1 describes a method in which several routes included in a timetable are combined into a single job, and multiple such jobs are grouped together to create alternating shifts, and a vehicle allocation planning method is presented in which the operations of train sets are planned so that the load on each set is uniform, with the job being assigned to each set on a daily rotation basis based on the alternating shifts.

[0005] Specifically, task 1 is assigned to formation A on the first day, task 2 on the second day, task 3 on the third day, and so on. Similarly, task 2 is assigned to formation B on the first day, task 3 on the second day, and task 4 on the third day, so as not to overlap with tasks already assigned and to even out the workload.

[0006] This method of creating tasks and police boxes and assigning them to each train has basically been passed down even in recent years, and tasks and police boxes are basically created and assigned from the timetable.

[0007] On the other hand, recent advances in IoT technology have led to the development of technologies that estimate the degree of deterioration and load accumulated in vehicles from sensor data. For example, Patent Document 2 can be cited.

[0008] Patent Document 2 proposes a method for estimating the load on a target vehicle based on vehicle driving data and status information on each onboard device while the vehicle is operating. Furthermore, it shows a method for predicting when future repairs will reduce costs by changing the repair threshold value for the load. [Prior art documents] [Patent documents]

[0009] [Patent Document 1] Japanese Patent Application Laid-Open No. 2001-5846 [Patent Document 2] Japanese Patent Application Publication No. 2023-157092 Summary of the Invention [Problem to be solved by the invention]

[0010] Patent Document 2 estimates the load on the target equipment or facility and indicates at what load level repairs should be carried out to improve risk and costs.

[0011] However, in the case of railways, multiple trains are used to operate a single line on a daily basis, so even if repairs are made at a time when risks and costs of individual pieces of equipment can be improved, it is difficult to say that this has resulted in improvements in risks and costs for the line as a whole.

[0012] Furthermore, when repair times are decided for individual pieces of equipment, the repair times for each train set are not taken into consideration, and there is a risk that there will be times when multiple train sets remain at high risk of failure when viewed as a line.

[0013] Thus, an operational management method is needed that will improve risks and costs when viewed as a line.

[0014] An object of the present invention is to improve risks and costs for a railway line in a vehicle operation management system. [Means for solving the problem]

[0015] A vehicle operations management system according to one aspect of the present invention is a vehicle operations management system that manages the operation of a plurality of railway formation vehicles, and is characterized by having: a performance-based load prediction unit that calculates a future current operational load prediction for each of the plurality of railway formation vehicles by referring to performance; a time point load target calculation unit that calculates a load target for each future point in time; and a load prediction target calculation unit that determines a load distribution of the future load of the plurality of railway formation vehicles, calculates a post-operation change load prediction after an operation change by changing the current operation load prediction so that the load reaches the load target while suppressing the load distribution, and plans vehicle allocation for the plurality of railway formation vehicles based on the post-operation change load prediction. [Effects of the Invention]

[0016] According to one aspect of the present invention, in a vehicle operation management system, risks and costs can be improved for a line. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a configuration diagram of a railway system and a vehicle operation management system according to a first embodiment. [Figure 2] FIG. 1 is a diagram illustrating a concept of vehicle operation for reducing the incidence of vehicle breakdowns. [Figure 3] FIG. 2 is a diagram showing an overall processing flow of the vehicle operations management system of the present invention. [Figure 4] FIG. 4 is a conceptual diagram of the first half of the overall processing flow of FIG. 3. [Figure 5]FIG. 2 is a diagram showing an example of data items of the sensing data 111. [Figure 6] FIG. 10 is a diagram showing examples of data items of an inspection history 112. [Figure 7] FIG. 10 is a diagram showing examples of data items of environment information 114. [Figure 8] FIG. 10 is a diagram illustrating a specific example of a data table of load prediction targets. [Figure 9] FIG. 10 is a diagram showing an example of data items storing load policies for each month as a load forecast target 116. [Figure 10] FIG. 10 is a diagram showing examples of data items of a diagram 113. [Figure 11] FIG. 10 is a diagram showing an example of data items of a line-specific load amount 117. [Figure 12] FIG. 10 is a process image diagram of steps 305 and 306. [Figure 13] FIG. 10 is a diagram showing a detailed flow of step 305. [Figure 14] FIG. 10 is a diagram showing an example of a job and a police box. [Figure 15] This is an illustration of creating a job from a diamond. [Figure 16] FIG. 10 is an image of the results assigned by step 306. [Figure 17] FIG. 10 is a diagram showing examples of data items of a vehicle allocation plan 119. [Figure 18] FIG. 10 is a diagram showing a detailed flow of risk and cost evaluation. [Figure 19] 10 is a diagram showing an example of data items for storing load amounts and failure rates for each train formation among the evaluation results 120. FIG. [Figure 20] 10 is a diagram showing an example of data items for storing failure rates and loss costs by line among the evaluation results 120. FIG. [Figure 21] FIG. 20 is a diagram showing an example of an output result comparing the current operation from the present point onward with a proposed change, using the load amounts by formation in FIG. 19. [Figure 22] This figure shows an example of the output results comparing the current operation and proposed changes from the present point onwards using the failure rates by line in Figure 20. [Figure 23]1 is a diagram showing a flow from the formulation of a vehicle allocation plan to actual train control in a railway using the vehicle operation management system of the present invention. [Figure 24] FIG. 10 is a diagram showing a situation in which one or more new car replacement times are set for a period from the present time to a future time in the second embodiment. [Figure 25] This is a diagram showing one proposed operation method to ensure that the vehicle is below the recommended replacement level by the time the vehicle replacement period begins. [Figure 26] This is a diagram showing one proposed operation method to ensure that the vehicle is below the recommended replacement level by the end of the vehicle replacement period. [Figure 27] FIG. 10 is a diagram showing a flow for determining a combination of new car replacement timings and target railway vehicle formations. [Figure 28] FIG. 10 is a configuration diagram of a railway system and a vehicle operation management system according to a second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0018] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. The same reference numerals denote the same components, and the basic configurations and operations are the same. [Example]

[0019] This first embodiment relates to a vehicle operation management method and management system that estimates the load accumulation status of each vehicle based on sensing data sent from each vehicle and the vehicle inspection history, and presents a specific means for formulating a vehicle operation plan based on the load accumulation status to achieve a desirable future load variation.

[0020] 1 is a configuration diagram of a railway system and a vehicle operations management system positioned therein according to Example 1. The vehicle operations management system 101 is configured with a computer device equipped with information processing resources such as an input unit, a storage device (a main storage device and an auxiliary storage device), a processing unit, an output unit (for example, a liquid crystal display device), a communication unit, and a bus that interconnects these units.

[0021] The input unit is composed of, for example, a keyboard, mouse, touch panel, etc., and the maintenance manager issues instructions to execute processes, which are then used to perform load forecasting, load-based shift planning, shift allocation calculations, risk and cost evaluations, etc., as described below. The storage device is equipped with a main storage device 102 (for example, memory) and an auxiliary storage device 103 (for example, HDD).

[0022] The main memory device 102 stores programs that realize each function of the present invention (load prediction target calculation unit 104, each time point load amount target calculation unit 105, load-based shift planning unit 106, shift allocation calculation unit 107, line load amount prediction unit 108 (a line refers to each line that appears on the timetable), performance-based load prediction unit 109, and risk and cost evaluation aggregation unit 110).

[0023] The auxiliary storage device 103 also stores various types of information used in the present invention (sensing data 111, inspection history 112, timetable 113, environmental information 114, estimation model 115, load forecast target 116, load amount by line 117, station information 118, vehicle allocation plan 119, and evaluation results 120). The processing unit is made up of a processor (CPU), and functions as an arithmetic execution unit that executes the processing of the program, thereby enabling the various functions of the present invention to be exerted.

[0024] The output unit can confirm the item setting status and processing results of the present invention. The communication unit has a function to communicate with each system and vehicle described below that is located outside the vehicle operations management system, receives information sent from the above systems and vehicles, and stores it in the auxiliary storage device 103. In addition, based on the results of each program executed in the main storage device by the processing unit, it transmits information to each system and vehicle.

[0025] The bus connects the input unit, output unit, processing unit, communication unit, main storage unit, and auxiliary storage unit, and contributes to realizing the functions of the present invention by transferring information.

[0026] The communication unit is connected via a network to, for example, a transportation plan management system 121, an operation management system 122, and a railway vehicle configuration 123. The transportation plan management system 121 manages the transportation plans of each railway vehicle configuration. It also manages crew allocation plans for operating these railway vehicle configurations.

[0027] The operation plan 122 is a system that actually manages and controls the operation of each railway vehicle formation based on the transportation plan drawn up by the transportation plan management system 121. Furthermore, if some railway vehicle formations are unable to operate as planned, the operation plan 122 cooperates with the transportation plan management system 121 to revise the transportation plan to an appropriate one and continue operation.

[0028] The railway formation vehicle 123 performs train control based on the operation control content or control instructions of the operation control system 122. At this time, the railway formation vehicle 123 senses each device and each apparatus of the railway formation vehicle using the sensor device 124 and transmits the results to each system via the network. The vehicle operations management system 101 receives the sensing data sent by the sensor device 124 and stores it as history in an auxiliary storage device. The number of railway formation vehicles to be managed is arbitrary, and at least one or more railway formation vehicles are targeted.

[0029] FIG. 2 is a diagram illustrating the concept of vehicle operation for reducing the incidence of vehicle breakdowns, which is the basis of the present invention.

[0030] FIG. 2 is expressed as a line graph, with the horizontal axis representing time and the vertical axis representing the load on each railway vehicle. Note that for the sake of explanation, the vertical axis represents the load, but it can be any measured or estimated value that may be a cause of a failure, or it can be thought of as the failure rate. 201 shows the changes in the load on each railway vehicle up to the present time, and here six railway vehicles, consisting of vehicles A, B, ..., and F, are shown as an example. 202 shows the load on each railway vehicle at the present time, with the vertical dotted line 203 representing the current time, and the load on each railway vehicle is conveniently indicated by a gray circle on dotted line 203.

[0031] The dashed line 204 to the right of this current load shows the future load forecast for each railway vehicle. 205, 206, and 207 show the load targets for all railway vehicles on the target line at each point in time.

[0032] Overall, the load increases with the distance traveled and the passage of time, and it is thought that as the load increases, the failure rate will also increase. Therefore, the challenge in vehicle operation is how to operate each train set so that the load does not increase. For example, train sets that have become too loaded must be operated in a way that suppresses the load so that it does not increase in the future. Generally, the higher the load on equipment, the higher the failure rate, so it is natural to suppress the load from the train sets with the highest load.

[0033] On the other hand, in the case of railways, the daily operating volume is determined in advance by a timetable, so if a certain train set is operated to reduce the load, the load on one of the other train sets must be increased accordingly. In this case, increasing the load starting with the train set with the lightest load is thought to be an effective method for reducing the failure rate across the entire line.

[0034] In light of this, in order to reduce the failure rate over the entire line, it is considered desirable to establish a vehicle operation target policy that limits the distribution of loads on each railway vehicle formation for each load target. This idea of ​​limiting the distribution of loads on each railway vehicle formation is the core of this invention.

[0035] Figure 3 shows the overall processing flow of the rolling stock operations management system of the present invention. First, in step 301 of Figure 3, the actual load and future load forecast for each vehicle are calculated based on the actual load. This processing will be explained using the image of the first half of the overall processing flow in Figure 4.

[0036] 201, 204, and 205 in Figure 4 are the same as those in Figure 2. 401 shows the load forecast for the current operation. 402 shows the average load forecast target for achieving the load forecast target 205. 403 shows an example of the load forecast target for each month for achieving the load forecast target 205. The actual load value in step 301 may be a measured value of some kind, or it may be an estimated load value composed of multiple factors. In the case of an estimated load value, it can be expressed as a linear sum composed of several factors, for example. (Equation 1) is an example of a load estimation formula expressed as a linear sum. Such an estimation formula can be found by linear multiple regression from past history.

[0037]

number

[0038] 4 illustrates an image of operation based on a load forecast after an operation change. A load forecast target 205 is set in advance, and a current operation load forecast 401 is corrected to a load forecast 204 after an operation change so that it falls within the range of the load forecast target 205. The specific corrected operation method is shown as 403.

[0039] FIG. 5 shows an example of data items of sensing data. Reference numeral 501 is a train formation ID, which is an identifier for identifying a railway train formation vehicle. Reference numeral 502 is an equipment ID, which is an identifier for identifying the equipment installed on board. Reference numeral 503 indicates the type of equipment installed. Reference numeral 504 indicates the time when the sensing data was acquired. Reference numeral 505 indicates the location where the sensor data was acquired. It is desirable that the acquisition location has information granularity that allows it to be linked to the environmental information 114 described below. In this example, the acquisition location is expressed as an array of [line name, inbound / outbound, distance from starting station], but this is not limited to this item depending on the configuration of the environmental information 114. Reference numeral 506 indicates the actual sensor value for the equipment ID 502.

[0040] FIG. 6 shows an example of data items in the inspection history 112. 601 is the train ID, which indicates the identifier of the train vehicle. 602 is the equipment ID, which is also the identifier of the equipment. 603 indicates the type, which indicates the type of equipment. 604 indicates the inspection time. 605 indicates the inspection result. 606 indicates the values ​​for various inspection items measured during the inspection. In this example, a maximum of k sensor values ​​are used, but since the content and number of inspection items often differ depending on the type of equipment, data items may be stored as different items for each type of equipment. For example, 506 may be represented as a single column, and the values ​​may be stored as an array of (key, value) as [(inspection item 1, value 1), (inspection item 2, value 2), ...].

[0041] Fig. 7 shows an example of data items of the environmental information 114. Fig. 7 is a table showing the shape of the railroad tracks, and is a table of information required to predict the load amount for each line.

[0042] In particular, with regard to environmental information, in order to calculate the load on each train formation for each timetable, which will be described later, it is desirable to model the environmental information 114 including it as an explanatory variable. 701 is a route identifier, and 702 is a type that identifies which alignment on the route. 703 indicates a point, showing the distance from the starting station on the route. 704 indicates the alignment, showing whether it is a straight line or a curve to the left or right (and the curvature if applicable). 705 indicates the gradient. 706 is additional information, which is used to identify, for example, whether the route is on a bridge, inside a station, or inside a tunnel. 707 indicates the speed limit.

[0043] Using this sensing data 111, inspection history 112, and environmental information 114 (after quantification of qualitative data), an estimation formula is obtained by regression or the like, treating them as explanatory variables. Using the thus estimated (Equation 1), in step 301, a load prediction for the current operation 401 is calculated. Specifically, by substituting a future elapsed time for the elapsed time t, it is possible to obtain a load prediction for the case where the current operation is continued. In addition, by storing the estimated model in estimation model 115, it can be used for various prediction calculations described below.

[0044] Next, in step 302, the load forecast target is calculated. The load forecast target is shown as 205 in FIG. 4. The load forecast target is defined as the spread of load amount for a specific period. There are several possible specific methods for calculating this load forecast target, but it may be defined as shown in (Equation 2), for example.

[0045]

number

[0046] As explained in FIG. 2, there may be multiple load forecast targets. Calculations are made for each load forecast target using, for example, (Equation 2). When there are multiple load forecast targets, α and β in Equation 2 above are explained as fixed values, but these may be changed depending on the interval of the load forecast target. For example, calculations may be made once for the current time 203 and the first load forecast target 205 in FIG. 2, and then α and β may be redefined to be different between load forecast target 205 and load forecast target 206, and calculations may be made again.

[0047] Figure 8 shows a specific example of a data table for load forecast targets. The first line shows the data item name, and the second and subsequent lines show the actual data values, with each line representing one record of data. In the following explanation, data examples will be explained in this table format. 801 shows the load forecast target ID, and 802 shows the time period indicating the elapsed time for that load forecast target. The elapsed time can be calculated as the difference between the time period and the current time. 803 shows the average value of the load forecast target, and 804 shows its deviation. Furthermore, from these average values ​​and deviations, the upper limit (average value + deviation) in 805 and the lower limit (average value - deviation) in 806 may be calculated and stored.

[0048] Next, in step 303, a load forecast target for each month that satisfies the load forecast target is calculated for each railway vehicle. 402 in FIG. 4 shows an average load forecast toward the next load forecast target 205. Also, 403 shows the load policy for each month. The load policy for each month is assigned as an initial solution in accordance with this average load forecast 402. Here, the assignment may be automatic so that the load forecast target 205 is reached, but some deviation from the average load forecast 402 is allowed. For example, the load policy may be assigned using random numbers at first, and then automatically recalculated and assigned so that 402 is finally reached.

[0049] Fig. 9 shows an example of data items that store the load policy for each month as the load forecast target 116. This is a table that corresponds to the load forecast target 116 in Fig. 1, and this table is used to determine the current operating line 2101 and the changed operating lines 2102, 2103, and 2104 in Fig. 21.

[0050] 901 indicates the train formation ID. 902 indicates the measurement date. 903 indicates the load amount on the measurement date for the relevant railway train formation. 904 indicates the type of stored information. Records marked "Proposed change" indicate records assigned as the initial solution. Records marked "Current operation" indicate the case where the current operation remains unchanged. 905 indicates a predicted change in the load amount. Here, the predicted change data can be expressed, for example, as an array of (time, load policy, expected load amount). By expressing it in this way, future load changes in relation to the load policy can be stored in a single record.

[0051] Next, in step 304, the load on each train set is estimated for each train in the timetable.

[0052] Figure 10 shows an example of data items for the timetable 113. It shows a timetable table, with one record representing one line. These lines are used to predict the load for each line.

[0053] 1001 is the route ID. 1002 is the line ID, which indicates an identifier that identifies each line (called a line) in the timetable. 1003 indicates the direction of travel of this line. 1004 indicates the starting point, and 1005 indicates the ending point. 1006 indicates the arrival and departure stations and times, which in this example are represented as an array of (station name, time, arrival / departure or passing type). For each line in this timetable, the load amount is estimated for each railway vehicle. This load amount is calculated using the estimation formula shown in (Equation 1).

[0054] Fig. 11 shows an example of data items for the load amount by train 117. By checking the increased load amount, a train with an increased load amount that is close to the load at the time of alternate assignment is assigned to the train formation.

[0055] 1101 indicates the route ID. 1102 indicates the route ID. 1103 is the train formation ID, which stores the identifier of the railway formation vehicle. 1104 indicates the increased load amount. This makes it possible to store the increased load amount for each railway formation vehicle for each route.

[0056] Before explaining steps 305 and 306, an image of the processing will be explained using Figure 12. Through the processing up to step 303 described above, the load policy for each month is determined for each railway vehicle formation as shown in 1201. In accordance with this load policy, work (jobs) are allocated on a daily basis for each load policy from multiple shift boxes with different loads. 1202 shows an image of how jobs are allocated from low-load shift box X 1203 to a railway vehicle formation that has a low-load policy in the third month.

[0057] For example, low load 1 is assigned job 1 in station X 1203. Similarly, daily jobs are assigned for other load policies. 1204 is an example of the assignment result for a high-load train set, which is the result of assignment from station Z in 1205.

[0058] In normal vehicle allocation, there is generally only one alternate station, but in this invention, multiple alternate stations with different loads are automatically created and allocated according to the load policy, aiming to operate in a way that minimizes the distribution of loads on each railway vehicle across the entire line. How this processing is realized will be explained in steps 305 and 306.

[0059] In step 305, multiple alternating stops with different loads are calculated from the timetable. This process will be explained in detail using the detailed flow in Figure 13. In step 1301, the load forecast target for each railway vehicle for a certain month is obtained. Specifically, the load policy and expected load amount for each railway vehicle for the target month are obtained from the change forecast in 905 in Figure 9.

[0060] Next, in step 1302, the number of shifts with different loads to be created, the shift size, and the expected load amount for each shift are calculated from the load forecast target. A shift is a list of the jobs to which each railway vehicle is assigned, and a job represents the operation content in which one railway vehicle is operated by the same crew and train composition.

[0061] Figure 14 shows an example of a job and a police box. 1401 indicates the job ID, and 1402 indicates its contents. One record indicates one job. 1403 is an extracted version of the job content of 1402, and 1404 shows the data representation of 1403. In 1304, one piece of data (a, 08:50, c, 10:30) indicates that the first train departs from station a at 08:50 and arrives at station c at 10:30, and furthermore, one piece of data (c, 12:10, a, 13:50) indicates that the first train departs from station c at 12:50 and arrives at station a at 13:50.

[0062] Since it is considered one job, it can be understood that 1403 is one job from departing from station a to returning to station a. The list of jobs combining 1401 and 1402 is called a police box. The police box size is the size (number of records) of this list of jobs. Police box information 118 is composed of and stored in two columns: the job ID in 1401 and the data representation of the job content in 1404.

[0063] As mentioned above, the present invention aims to reduce the distribution of loads across the entire line by creating multiple substations and assigning tasks to substations according to the load policy. To achieve this, if the load policy acquired in step 1302 above is one of three types (low load, normal load, high load), it is determined that three substations will be created. At this time, the number of train cars for each load policy becomes the desired substation size. Furthermore, for each load policy, the average expected load is calculated and used as the expected load for the substation.

[0064] Next, in step 1303, for each station, a line that is close to the expected load is entered into the station as a job. At this time, if there is a combination of multiple lines that results in a job that is close to the expected load, that job is entered into the station. At this time, the station size is also taken into consideration, and jobs are added so that each station is close to the station size.

[0065] An image of this task creation is shown in Figure 15. 1501 shows an example of a timetable, and in this example it is a timetable from station a to station g. Each diagonal line in the figure represents a timetable line. At this time, an example of connecting timetable lines to create a task is shown in 1502. There are two thick lines in the figure, each of which represents one task. For example, the line in the upper left has been created as a task, starting from station a, terminating at station c, then turning around and returning to station a again. At this time, if you want to put the created task into a station with a different expected load, you can put it into a different station by reconnecting it with another line as in 1503 and making it a task.

[0066] Next, in step 1204, it is determined whether there are any unassigned police boxes or lines remaining. If there are, in step 1205, lines that have already been assigned as jobs are combined with unassigned lines and reassigned as new jobs. Also, lines that have already been assigned as jobs are reverted to their original unassigned lines so that they can be combined with other lines.

[0067] Next, in step 1306, it is determined whether allocation has been completed for all months, and if there are any unallocated months, the target month is updated in step 1307 and the process returns to step 1301. If allocation has been completed for all months, the process ends and proceeds to step 306 in FIG.

[0068] Returning to the explanation of Figure 3, in step 306, vehicle allocation is planned in accordance with the load forecast target for each railway vehicle formation. As shown by the relationship between 1202 and 1203 and the relationship between 1204 and 1205 in Figure 12, the original problem of vehicle allocation was to simultaneously allocate vehicles to all railway vehicle formations in accordance with the load policy, but it can be seen that the processing flow of the present invention in Figure 3 can be transformed into a problem of allocating work from one shift with one expected load of the same level to a group of railway vehicle formations with the same load policy.

[0069] Therefore, the vehicle allocation problem can be solved by using existing methods, such as by applying a genetic algorithm (GA), as described in the aforementioned Patent Document 1.

[0070] 16 shows an image of the results of allocation in step 306. Reference numeral 1601 indicates the train set ID. Reference numeral 1602 indicates the future vehicle allocation result for the corresponding train set.

[0071] For example, for the first month, train set 10001 will be used for light duty, with light duty 1 assigned on the first day, light duty 2 on the second day, and light duty 3 on the third day.

[0072] Figure 17 shows examples of data items in the vehicle allocation plan 119, and shows an example of the data representation of Figure 16. 1701 shows the train set ID. 1702 shows the time period, which indicates the time period of the operation details described below. 1703 shows the increased load. 1704 shows the cumulative load. 1705 shows the operation details, which in this example are expressed as an array of (operation date, work). For example, for train set 10001, the operation details are shown as light operation 1 on the first day of 2023 / 2, light operation 2 on the second day, etc.

[0073] In step 307, it is determined whether the solution is feasible. In the present invention, the load policy may change with each month, and the assigned shifts will also change accordingly. Therefore, when the load policy changes, it is determined whether the last job of the previous month and the first job of the next month are connected. If the jobs are not connected and are not too far apart, it is possible to connect them by creating a forwarding route.

[0074] If a solution that cannot be physically connected is found, a search for a solution begins again in step 308. Here, it is necessary to avoid arriving at the same solution. In the present invention, this can be addressed by changing the load policy. For example, within the same railway vehicle, the load policy set for each month can be replaced with the load policy for a certain month. This does not change the overall achievement of the load target, but by changing the load policy for that month, it becomes possible to derive a different solution.

[0075] Within the same month: When the load policy of one railway vehicle is changed, the load policy of another railway vehicle is changed so that the total overall load does not change. For example, when the load policy of one railway vehicle is changed from normal to low load, the load policy of another railway vehicle is changed, for example, from normal to high load, so that the total overall load does not change. This is a process that is performed because the amount of railway traffic does not change, as described in the vehicle operation concept in Figure 2.

[0076] If it is determined in step 307 that the solution is feasible, a risk and cost assessment is performed on the vehicle allocation plan in step 309. Figure 18 shows a detailed flow of this risk and cost assessment. In step 1801, a certain railway vehicle is selected. Next, in step 1802, the vehicle allocation plan is referenced for that railway vehicle, and the allocated load amount for each day is obtained.

[0077] Specifically, the increased load amount 1708 in the vehicle allocation plan 119 in Fig. 17 is referenced. In step 1803, the cumulative load amount is calculated. In step 1804, the failure probability at each time point is calculated from the cumulative load amount. One method for calculating the failure probability from the load amount is the Weibull distribution. The Weibull distribution is defined in the form of (Equation 3).

[0078]

number

[0079]

number

[0080] Next, in step 1805, it is determined whether the process has been carried out for all railway vehicle formations, and if there are any that have not been carried out, the process returns to 1801 and processes the railway vehicle formations that have not been carried out. If not, in step 1806, the failure probability for the entire target line is calculated. The failure probability for the entire target line can be calculated using the failure rate of each railway vehicle formation using equation 5.

[0081]

number

[0082]

number

[0083] Figure 19 shows an example of data items that store the load and failure rate for each train set from the evaluation results 120. 1901 indicates the train set ID. 1902 indicates the time period, allowing the failure rate from the present point onwards to be expressed. 1903 indicates the type, indicating whether it is the failure rate under current operation or the failure rate under the proposed change. 1904 indicates the increased load on the relevant railway train set, and 1905 indicates the cumulative increase. 1906 stores the failure rate calculated in step 1804 of Figure 18.

[0084] Figure 20 shows an example of data items that store the failure rate and loss cost by route from the evaluation result 120. 2001 indicates the route ID, which is the route identifier. 2002 indicates the time period, and like 1902, it allows the failure rate and loss cost from the present point onwards to be understood. 2003 is the type, indicating whether it is the current operation or a proposed change. 2004 stores the failure rate of the route calculated in step 1806 of Figure 18. 2005 similarly stores the expected loss calculated in 1807.

[0085] Figure 21 shows an example of the output results comparing the current operation and proposed changes from the present time onwards, using the load amounts by unit of load shown in Figure 19. Note that explanations of the same parts as in Figure 2 are omitted. The dotted line in 2101 indicates the load amount if the current operation remains unchanged. Comparing the current operation and proposed changes in this example, it is easy to see that the distribution of future load amounts will be narrower. 2102 shows the average and deviation of the load amount for each unit of load at the current time. Continuing, 2103 shows the average and deviation of the load amount for each unit of load at the next load forecast target. Similarly, 2104 shows the average and deviation at the next load forecast target. By visualizing the load amount forecast for the current operation plan and the proposed changes in this way, it is possible to visually confirm that the proposed changes are expected to fall well within the load forecast target range.

[0086] Figure 22 shows an example of the output results comparing the current operation from the present point in time with the proposed changes, using the failure rates by line in Figure 20. It shows the change in failure rate when viewed as a line, rather than as a train set. It shows how the failure rate on a line can be reduced by operating in a way that suppresses the load variation of train sets.

[0087] 2201 shows the line failure rate up to the present time. 2202 shows the line failure rate under current operation. 2203 shows the line failure rate under the proposed change. By visualizing the change in failure rate from the present time in this way, the effect of the present invention can be quantified. Furthermore, by changing the vertical axis of Figure 22 from the line failure rate to the expected loss and displaying the expected loss of 2005 in Figure 20, it is possible to grasp how much loss is likely to occur in monetary terms.

[0088] Figure 23 shows the flow from the formulation of a vehicle allocation plan to the actual train control in a railway using the vehicle operation management system of the present invention. Note that the function names and data names are basically the same as in Figure 1, and refer to the same things as in Figure 1.

[0089] In the calculation of the operational target 2301, starting from an execution instruction from the maintenance manager, the sensing data 111, the inspection history, and the environmental information are used to perform a performance-based load prediction, a load prediction target calculation, and a calculation of the load amount at each point in time.

[0090] Next, in the planning step 2302, load prediction for each route, load-based shift planning, shift allocation calculation, and risk and cost aggregation are performed, and the load for each route, shift information, vehicle allocation plan 119, and evaluation results are output.These output results can also be confirmed by the maintenance manager.The details of the processing up to this point are as explained in Figure 3.

[0091] Next, we will move on to an explanation of the traffic management of 2303. The transportation planning management system 121 uses the vehicle allocation plan 119 sent from the rolling stock operation management system 101 to create related plans. For example, based on the vehicle allocation plan 119, it creates plans for maintenance work and crew allocation plans. The plans created by the transportation planning management system 121 and the vehicle allocation plan 119 are then sent to the traffic management system 122 and used for traffic management. The traffic management system 122 uses the series of plans to issue operation instructions and control operations for each railway vehicle formation. The railway vehicle formation 123 receives the operation instructions and operation control information sent from the traffic management system and performs actual train control.

[0092] In this way, train control is carried out based on the vehicle allocation plan formulated by the vehicle operation system. Finally, a sensor device 124 mounted on the railway vehicle set 123 measures train status information and control information, and the sensing data is stored in the sensing data 111 of the vehicle management system via a network or the like. This sensing data is used when formulating the next vehicle allocation plan.

[0093] By going through the cycle of Plan (planning) → Do (train operation) → See (sensing) in this way, it is possible to reduce the distribution of load on each train car in the medium to long term, enabling more stable train operation with fewer breakdowns. [Example]

[0094] In addition to the first embodiment, the second embodiment relates to a vehicle operation management method and management system that presents a specific means for formulating a vehicle allocation plan that will result in an appropriate load amount by the time of replacement with a new vehicle.

[0095] FIG. 28 is a configuration diagram of a railway system according to the second embodiment and a rolling stock operation management system positioned therein.

[0096] The vehicle operations management system of the second embodiment differs from the vehicle operations management system of the first embodiment shown in Fig. 1 in that a new introduction timing determination unit 240 is newly added. The remaining configuration is the same as that of the vehicle operations management system of the first embodiment shown in Fig. 1, and therefore a description thereof will be omitted.

[0097] Figure 24 shows a situation where one or more new car replacement dates have been set for a period from the present to some future time. Note that explanations of parts that are the same as in Figure 2 will be omitted. 2401 shows the first new car replacement date, and 2402 shows the second new car replacement date. Also, 2403 shows the replacement guideline for the load. A single railway train requires several cars, and since multiple trains are needed on a single line, especially on major routes, it is rare to replace the entire train at once; rather, the replacement dates are often divided into several phases. Of course, operations must be carried out in a way that prevents breakdowns from occurring before the new car replacement date, so the challenge is to devise a car allocation plan that will use up all the cars by the time the new cars are replaced while suppressing the line's breakdown rate.

[0098] Figure 25 shows one proposed operation method for keeping the load below the replacement guideline by the start of the rolling stock replacement period. 2501 is the reference line, which is set in parallel with the replacement guideline so that it is below the replacement guideline. For railway train sets that are due for new car replacement, if they are operated so that the load reaches (or falls below) the new car replacement period, appropriate replacement will be possible as long as new railway train sets are prepared at least at the start of the new car replacement period.

[0099] Figure 26 shows one proposed operation method to keep the vehicle load below the recommended replacement level by the end of the vehicle replacement period. 2601 is the reference line, which is set so that the load remains below the recommended replacement level until the end of the new vehicle replacement period. If the operation is carried out so that this reference line is reached during the new vehicle replacement period, it is possible to operate the vehicle so that the recommended replacement level is not reached at any stage during the new vehicle replacement period, and appropriate replacement will be possible.

[0100] By setting a baseline in this way and formulating a plan, it becomes possible to make a vehicle allocation plan that reduces the failure rate until the time for new vehicle replacement.The next problem is which train set of vehicles to allocate to which new vehicle replacement period.

[0101] FIG. 27 shows a flow for determining a combination of new car replacement times and target railway vehicle formations when one or more new car replacement times have been set.

[0102] In step 2701, the earliest possible replacement timing for new rolling stock is selected from among the unallocated rolling stock. In step 2702, for each unallocated railway formation, a load forecast for each month corresponding to the replacement timing in the current operation is obtained. By referring to this load forecast, it becomes easier to select railway formations that currently have a small load but will have a large load in the future.

[0103] In step 2703, the unassigned railway vehicle set with the highest load is set as the relevant replacement period. In step 2704, it is determined whether replacement has been performed at all new car replacement periods, and if not, the process proceeds to step 2705, where the next new car replacement period is selected. The process then returns to step 2702, and processing continues. If replacement has been performed at all new car replacement periods in step 2704, the process ends.

[0104] By performing the process of Figure 27, the combination of target railway vehicle formations for each new vehicle replacement period is determined. It is then found that one combination of each (new vehicle replacement period, target railway vehicle formation) is exactly the same as the case of the first embodiment in Figure 2.

[0105] Therefore, by performing the processing in Figure 3 for the number of combinations output in Figure 27, it is possible to create a vehicle allocation plan for each new vehicle replacement period so that the load distribution within the target railway vehicle formation is small (and so that the failure rate on the route is small).

[0106] In the above embodiment, in order to present guidelines for vehicle management that will result in risk and cost improvements when viewed as a line, the load on each vehicle is estimated from sensor information, future load changes are predicted, and a vehicle operation plan is created that reduces the load variation (variance) of each vehicle while reaching the future load target.

[0107] According to the above embodiment, by suppressing the distribution of loads across the entire railway formation while reaching future load targets, it is possible to reduce the failure probability without disproportionately distributing the load to some railway formations. Furthermore, it is possible to operate the railway formation so that it uses up the stock appropriately while suppressing the failure probability until the time for the introduction of new cars. [Explanation of symbols]

[0108] 101 Vehicle Operation Management System 102 Main storage 103 Auxiliary storage device 104 Load forecast target calculation unit 105 Time-point load target calculation section 106 Load-Based Station Planning Department 107 Station Allocation Calculation Unit 108 Load Prediction Section 109 Performance-based Load Forecasting Section 110 Risk & Cost Assessment Aggregation Department 121 Transportation Planning and Management System 122 Traffic Management System 123 Railway train 124 Sensor Device 240 New launch timing determination department

Claims

1. A vehicle operations management system that manages the operation of a plurality of railway vehicle formations, a performance-based load prediction unit that calculates a future current operational load prediction for each of the plurality of railway formations by referring to performance data; a time-point load target calculation unit that calculates a future load target at each time point; a load forecast target calculation unit that calculates a load distribution of future load amounts of the plurality of railway formation vehicles, changes the current operational load forecast so that the load amount reaches the load target while suppressing the load distribution, calculates a post-operation change load forecast after the operation change, and plans vehicle allocation of the plurality of railway formation vehicles based on the post-operation change load forecast; A vehicle operation management system comprising:

2. The performance-based load prediction unit 2. The vehicle operations management system according to claim 1, wherein the current operational load forecast is calculated by referring to sensor information from the railway vehicle formation as the performance data.

3. The load prediction target calculation unit 2. The vehicle operations management system according to claim 1, wherein the load distribution is determined by predicting future changes in the load amount, and the post-operation change load prediction is calculated so as to reduce the variance, which is the variation in the load distribution.

4. The load prediction target calculation unit 2. The vehicle operations management system according to claim 1, wherein an operation policy for the load of the railway vehicle formation for each month is determined based on the post-operation change load prediction, and is planned as the vehicle allocation.

5. The each time point load amount target calculation unit calculating the loading target for each of a plurality of future phases; The load prediction target calculation unit determining the load distribution for each of the plurality of phases; 2. The vehicle operations management system according to claim 1, wherein the post-operation change load forecast is calculated so that the load distribution is gradually suppressed while the load amount reaches a plurality of the load amount targets.

6. The load prediction target calculation unit 6. The vehicle operations management system according to claim 5, wherein the post-operation change load prediction is calculated so that the train set load deviation of the future load amounts of the plurality of railway train sets is reduced in stages.

7. 2. The vehicle operation management system according to claim 1, further comprising a timetable load prediction unit for predicting future loads for a route timetable.

8. The line load prediction unit The vehicle operations management system according to claim 7, characterized in that it predicts the load amount of the railway vehicle when it runs according to the schedule of the schedule by referring to the schedule and environmental information of the route.

9. a load-based shift planning unit that creates a shift according to the load amount and calculates the number of shifts, the shift size, and the estimated load amount of the shifts according to the load distribution of the railway formation; a shift allocation calculation unit that allocates work within a shift including a group of railway cars divided according to a load operation policy and the estimated load amount so as to suppress the load distribution; 2. The vehicle operation management system according to claim 1, further comprising:

10. The alternating allocation calculation unit assigning the railway vehicle set to the station closest to the expected load amount based on an operation policy for the load within a specific period; 10. The vehicle operation management system according to claim 9, wherein the vehicle allocation is performed for a combination of the police box and the railway vehicle set allocated to the police box.

11. an evaluation and aggregation unit that evaluates and aggregates risks and costs based on the post-operation change load prediction; an output unit that displays the evaluation results evaluated by the evaluation aggregation unit; 2. The vehicle operation management system according to claim 1, further comprising:

12. 2. The vehicle operations management system according to claim 1, further comprising a new vehicle introduction timing determination unit that determines a railway formation of vehicles to be replaced at a predetermined new vehicle introduction timing based on the predicted amount of load.

13. The new car introduction timing determination unit 13. The vehicle operations management system according to claim 12, wherein the vehicle allocation is planned so as to suppress the load distribution of the railway vehicle formation at the time of introduction of the new vehicles and reduce the failure rate of the line.

14. A vehicle operation management system comprising the vehicle operation management system according to claim 1, a transportation plan management system, and an operation management system, The transportation planning management system includes: Formulating a transportation plan based on the vehicle allocation plan for the vehicle allocation sent from the vehicle operation management system; The operation management system includes: A vehicle control system that controls the operation of the railway vehicle formation based on the transportation plan sent from the transportation plan management system.

15. A vehicle operations management method for managing the operation of a plurality of railway vehicle formations, a performance-based load prediction step of calculating, by a performance-based load prediction unit, a future current operation load prediction for each of the plurality of railway formations by referring to performance data; a time-point load quantity target calculation step in which a time-point load quantity target calculation unit calculates a load quantity target for each future time point; a load forecast target calculation step of calculating a load distribution of future load amounts of the plurality of railway formation vehicles by a load forecast target calculation unit, changing the current operational load forecast so as to suppress the load distribution while the load amount reaches the load amount target, and calculating a post-operation change load forecast after the operation change, and planning vehicle allocation of the plurality of railway formation vehicles based on the post-operation change load forecast; A vehicle operation management method comprising:

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