JP6364249B2 - Vehicle operation management device, method, and program - Google Patents

Description

  The present invention relates to operation management of a moving body, and more particularly to an operation management apparatus, method, and program for vehicles and trains.

  When the train operation is disturbed due to bad weather or vehicle failure, normal operation of the train on the scheduled schedule may become impossible. At this time, the train operation employs an operation called operation arrangement, and the operation plan is changed to reduce the influence on passengers and return to normal operation.

  Patent Document 1 describes a “driving arrangement support device” for the purpose of “automatically creating a driving arrangement plan and assisting an operator when disturbance occurs in a train operation schedule”. . For more details, “Predict the train operation status for a certain period of time in the future, display it to the operator, monitor the train operation status, make a decision to organize the operation, and check the schedule and current train operation status. In addition, it is described that the purpose is to automatically create an operation arrangement plan and to provide an operation arrangement support device that allows an operator to correct the created operation arrangement plan. As a means for that, “basic operation time of trains between stations, minimum time interval, etc., basic data that stores route data such as the number of lines of each station, planned schedule data that stores train schedules, and , Actual schedule arrival / departure time data storing the actual train station arrival / departure time, arrival / departure time statistics means for obtaining statistical data of the travel time between stations and stop time at the station from the actual record schedule data, the basic data, the plan There is provided a predicting means for predicting the train station arrival and departure times within a predetermined time based on the schedule data, the actual schedule data and the contents of the arrival and departure time statistics means. In addition, the “arrival / departure time statistical processing function 1 performs statistical processing of travel time and station stop time between train stations based on the actual train arrival and departure times. Outputs statistical results of travel time and station stop time. As a method of statistical processing, for example, calculate the average of travel time and station stop time between stations by month and day for each train. ing.

Japanese Unexamined Patent Publication No. 7-52803

  It is necessary to predict and calculate the arrival and departure times of trains within a certain period of time when organizing operations. Therefore, it is important for the inventors of the present application to selectively use an appropriate value at an appropriate timing for a prediction calculation constant such as a running time between stations or a station stop time used for a prediction calculation. I found out.

  For example, April is a season of travel, and the flow of people changes drastically compared to March, and passenger volume during commuting hours may increase. When the passenger volume increases rapidly, the station stop time tends to increase in order to absorb the increase in boarding / alighting at the station. In such a case, the train station in April is unconditionally used as a “prediction calculation constant” using the average value obtained from the actual station stop time of the train in the past month (that is, March). Even if the arrival time is predicted, it will not match the actual situation. As another example, if the summer vacation of many schools in Japan begins in July, the amount of passengers at certain resorts and amusement parks may increase rapidly, and the same phenomenon occurs.

  Therefore, in the apparatus studied by the present inventor studied prior to the present application, the mechanically latest average value for the past month has not been used unconditionally as a “prediction calculation constant”. In other words, the “prediction calculation constant” is a value selected based on empirical rules for past actual values obtained statistically, and normally prepared in the constant table as “constants” that are not normally updated. . The “constant” is used to calculate the predicted arrival / departure time of the train in the operation arrangement, but the prediction of the arrival / departure time of the train using that “constant” is likely to cause a deviation from the actual driving performance. When it became, the “constant” was updated. However, the constant table rewrites the constant table at a specific address that is known only to the vendor who built the system when updating is necessary, and the operator cannot update the constant table.

  One object of the present invention is to call a “constant table” for predicting and calculating a train station arrival / departure time stored in the system from the train operation management system so that it can be easily changed.

  According to an example of the present invention, the vehicle operation management device stores a first storage area that stores performance data that records the travel performance of each vehicle, and a prediction calculation constant that is used for a calculation for predicting the operation of each vehicle. A second storage area to be stored, an arithmetic unit that extracts the actual data of the first period of each vehicle and calculates an analysis value obtained from statistical processing of the actual data in the first period; A graphical user interface including a first area displaying a constant, the analysis value that is a candidate for changing the prediction calculation constant, and a selection unit that selects whether to update the prediction calculation constant to the analysis value. Configure.

  According to the present invention, the maintenance operation of the predicted operation constant of the vehicle is facilitated by the user interface.

An example of the hardware constitutions of a train operation management apparatus is shown. An example of the train operation plan diagram in a train operation management system is shown. It is a flowchart which shows an example of a constant maintenance program. The structural example of performance diamond DB is shown. The structural example of analysis result DB is shown. The structural example of prediction calculation constant DB is shown. Demonstrates a graphical user interface for constant maintenance programs. 10 shows a graphical user interface of a constant maintenance program according to a second embodiment.

  Hereinafter, examples will be described with reference to the drawings. In this embodiment, a railway train operation management system will be described as an example. However, the present invention is not limited to this, and the present invention can also be applied to a moving body, a vehicle operation management system, and the like.

<Description of hardware configuration example>
In FIG. 1, an example of the hardware constitutions of the train operation management apparatus 1 of a present Example is shown. The device 1 includes a computer main body 2 to which an input device 10 such as a keyboard, a mouse, and a touch panel and a display device 20 such as a liquid crystal display are connected as peripheral devices. Further, the computer main body 2 is connected to a plurality of stations 5 via a network 3 in a wide area network 4 in the train operation management system. Thereby, the apparatus 1 can receive the track record of train travel in real time from each station 5 connected to the wide area network 4 via the network 3. Station 5 controls the train based on the timetable.

  The computer main body 2 includes a storage unit 40 that stores programs and data, and an arithmetic unit 30 that accesses the storage unit 40 and executes programs and the like. When the arithmetic unit 30 executes a program or the like and performs data processing, various functions of the train operation management device 1 are realized. The arithmetic unit 30 may be called a CPU (Central Processing Unit). The storage unit 40 includes, for example, an SRAM (Static Radom Access Memory), a DRAM (Dynamic Radom Access Memory), an HDD (Hard Disk Drive), or the like. That is, the storage unit 40 is hierarchically configured in consideration of data access speed, storage characteristics (volatile or nonvolatile), storage capacity, bit cost, and the like.

  In this embodiment, as an example, the storage unit 40 is provided with a record diagram DB 41, a prediction calculation constant DB 42, and an analysis result DB 43. . Here, the record diamond DB means a database (Data Base). Note that the original information such as the record diagram DB is stored in a data server (not shown) connected to the wide area network, and may be configured to be called to the storage unit 40 in the apparatus 1 as necessary.

  The record diagram DB 41 stores a record diagram that is the actual arrival and departure time of the train received from each station 5 at any time. If it generalizes more, the train operation management apparatus 1 will be provided with the 1st storage area which memorize | stores the performance data which recorded the driving performance of each train. The prediction calculation constant DB 42 stores prediction calculation constants that are used when creating a train prediction diagram in operation arrangement. If this is more generalized, the apparatus 1 will be provided with the 2nd storage area which memorize | stores the prediction calculation constant utilized for the calculation for driving | running prediction of a vehicle. The analysis result DB 43 stores an analysis value obtained by analyzing the data of the record diagram DB 41 by a record diagram analysis routine 45 described later. As will be described later, as long as there is data in the record diagram DB, the record diagram DB is obtained by calculation as needed. Therefore, it is usually a database created temporarily only during the processing of this embodiment. . However, when it is necessary to refer to a result calculated in the past, the apparatus 1 may be provided with a third storage area so as to store the analysis value. Details of each DB (database) 41, 42, 43 will be described later with reference to FIGS.

  Further, in this embodiment, the storage unit 40 has a constant maintenance program 44 which is one of the important parts of the present invention. The constant maintenance program 44 further includes a performance diagram analysis routine 45 and a constant difference display processing routine 46. Details of the constant maintenance program 44 will be described later with reference to the flowchart of FIG. It is assumed that the storage unit 40 includes various programs (not shown) including an operating system (OS) and supports the operation of the constant maintenance program 44.

  The record diagram analysis routine 45 analyzes a period to be analyzed (hereinafter, a constant statistics period) from the record diagram DB 41 that stores record diagram information for a certain period, and uses a calculation unit for an analysis value that is a candidate for a predicted calculation constant. Calculate by statistical processing. Then, the calculated analysis value is stored in the analysis result DB 43.

The constant difference display processing routine 46 compares and contrasts the value of the prediction calculation constant before update employed by the current system in the prediction calculation constant DB 42 and the update candidate value of the prediction calculation constant stored in the analysis result DB 43. As described above, the display device 20 has a function of displaying as a graphical user interface (GUI). Further, the constant difference display processing routine 46 sets the value of the prediction calculation constant before update employed by the current system in the prediction calculation constant DB 42 to the update candidate value of the prediction calculation constant stored in the analysis result DB 43 as an input device. Based on the instruction of 10, a GUI for instructing whether or not to change is realized (details will be described later in FIG. 7).
<Explanation of train operation plan diagram>
FIG. 2 shows an example of a train operation plan diagram 200 which is a premise of the present invention. The vertical axis shows the arrangement of stations in one line section, the horizontal axis shows the time, and the diagonal line shows the train travel plan. In the diagram 200, train travel plans of the train Ta and the train Tb are shown. For example, the train Ta indicates that the train is turned back to the E station after stopping at the C station and the D station, and is directed to the A station after stopping at the D station, the C station, and the B station.

  Here, as an example, after the train Ta arrives at the C station with a delay, the estimated arrival time at the D station is examined. The expected arrival time EAT (D) at station D is expressed by the following equation.

EAT (D) = AT0 (C) + ΔT + ΔT (C) + ΔT (CD) (Formula 1)
Here, AT0 (C) is the arrival time of station C in the schedule. ΔT is the arrival delay time (delay time) to station C, ΔT (C) is the stop time of station C, and ΔT (CD) is the train travel time from station C to station D.

  The stop time ΔT (C) and the train travel time ΔT (CD) are representative examples of “prediction calculation constants” used for predicting the train operation time. Such constants are stored in the prediction calculation constant DB 42 of FIG. For accurate operation prediction, the accuracy of these prediction calculation constants is important.

In addition, examples of the prediction calculation constant targeted by this embodiment include a folding time constant, a crossing time constant, a continuation time constant, and an operation time. The turn-back time constant is the time constant required to travel in the direction opposite to the direction in which the train was traveling at the stop station, and the crossing interval constant is the time between trains that use the mutually intersecting routes. It is a constant. Further, the continuation time constant is a time constant between two trains that continue to use the same line, and the operation time is a time constant required when the train travels between stations. Both are values that can be calculated from the actual schedule.

<Explanation of constant maintenance program operation>
Thereafter, the constant maintenance program 44 shown in FIG. 3 (1) processes the data of the record diagram DB 41 shown in FIG. 4 by the calculation unit to obtain the analysis value of the analysis result DB 43 shown in FIG. 5, and (2) the analysis thereof The flow of displaying the data of the result DB 43 and the prediction calculation constant (FIG. 6) currently used in the system on the graphic user interface (FIG. 7) will be described in detail with reference to the drawings.

First, the description starts with an example of the table configuration of the record diagram DB 41 in FIG. As shown in FIG. 4, the record diagram DB 41 is an aggregate of record diagram tables 400 to 440 collected every day. Within the record diagram table 400, record diagram information is managed as a record. Each table has a line section 401, a station 402, a number line 403, a line number 404, a schedule section 405, a train number 406, an arrival time 407, and a departure time 408 as data items. These track schedule information is updated by receiving data from each station 5 as needed. Moreover, as for these performance diagram information, the data of a fixed period (for the past several years) shall be stored in performance diagram DB41.

  FIG. 3 is a flowchart showing an example of the constant maintenance program 44. The constant maintenance program 44 roughly has two steps of a performance diagram analysis routine 45 and a constant difference display processing routine 46.

  The record diagram analysis routine 45 starts from a grouping pattern selection process 301. That is, here, as a premise of the analysis, the prediction calculation constant to be analyzed is selected and the statistical period is set. This is because, as described above with reference to FIG. 2, there are various prediction calculation constants used for predicting train operation, and it is necessary to select which constant to analyze. In this embodiment, the following description will be made assuming that one of the budget calculation constants for analyzing the “stop time stop time”, which is the stop time of a specific station of the train, is selected. The stop time stop time is not recorded as a direct value in the record diagram table 400 of FIG. 4 or the like, but when the arrival time 407 is subtracted from the departure time 408 in the same record, the stop time stop time is obtained. In addition, as described later, any one of the past month or the like is selected based on the working day as described later. It is also possible to select a specific arbitrary period as a reference period. In this embodiment, the following description will be given assuming that three months are selected as an example. The selection of the prediction calculation constant and the period to be analyzed can be realized by a graphical user interface as will be described later with reference to FIG.

  Next, the grouping processing 302 sorts the record diagram DB 41 for each grouping item determined in the grouping pattern selection processing 301 and performs grouping. In the present embodiment, for example, the record number # 1 1302 is identified from among the line section 401, the station 402, the line 404, the diagram classification 405, and the train number 406 in FIG. Group data for.

  The analysis value calculation process 303 calculates an analysis value as a change candidate by statistical processing from the record of the group specified in the preceding 302. This analysis value is a value that may become a prediction calculation constant, but in order to avoid confusion of terms, it is called and distinguished from the analysis value until update is confirmed.

  In the process 303, a process of excluding records that clearly deviate from the threshold determination is further performed. The determination of the threshold value is for excluding exceptions such as train operation delays due to personal injury and the like, and improving the accuracy of the prediction calculation constant. Therefore, the threshold value is set in advance as an upper limit value α and a lower limit value β using constants or the like, and is determined by stopping time stopping time χ <upper limit value α and lower limit value β <stopping time stopping time χ. In this embodiment, when the threshold value is an upper limit value α = 420 seconds and a lower limit value β = 25 seconds, the stop time of the arrival time 407 and the departure time 408 of the # 1 record in the record diagram table 420 (see FIG. 4). Is excluded from the average value calculation for 480 seconds (8: 54-8: 46 = 8 minutes). As an example, it is assumed that the analysis value as the average value is calculated as 75 seconds from the stop time (difference between 408 and 407) of the # 1 record for three months such as the performance diagram tables 410, 430, and 440. In addition, in a simple average calculation, a numerical value in seconds is calculated, but in a train operation, the operation is usually managed in units of 5 seconds or 15 seconds. For this reason, the analysis value is not a mere average value but a numerical value obtained by rounding the average value in units of 5 seconds or 15 seconds.

  The storage process 304 in the analysis result DB 43 stores the analysis value calculated in 303 in the analysis result DB 43. The above is the flow of the performance diagram analysis routine 45.

FIG. 5 shows a configuration example of the analysis result DB 43 calculated by the processing 304 described above. In this embodiment, the analysis result DB 43 matches the analysis result table 500 shown in FIG. The analysis result table 500 is grouped by train number, and includes train numbers 501, line sections 502, stations 503, line-specific 504, time zone classification 505, and stop time 506 as data items. If an example is demonstrated, record # 1 will save 75 seconds of stop time calculated | required from the average value of the three months of A station, an uphill, and a weekday diagram about train number 1302 in this figure. In the following, the stop time 506 from the station A to the line area for the train number 1302 is, as described above, the difference between the arrival time and the departure time in the record diagram table of FIG. Represents the time calculated by (average).

  Returning to the flowchart of FIG. 3 again, processing 311 to 316 of the constant difference display processing routine 46 will be described below. First, in the process 311, data of the analysis result DB 43 and the prediction calculation constant DB 42 are prepared.

  FIG. 6 is used by the system for operation prediction. The structural example of prediction calculation constant DB42 is shown. In this embodiment, the prediction calculation constant DB 42 is an aggregate of the prediction calculation constant tables 600 and 610 in FIG. Here, 600 is a prediction calculation constant table 1, which holds the stop time. Reference numeral 610 denotes a prediction calculation constant table 2, which holds travel time that is another prediction constant. Similarly, the prediction calculation constant DB 42 has prediction calculation constant tables as many as necessary types. The prediction calculation constant table 600 has the same items as the analysis result table 500 of FIG. 5 described above except for the numerical value of the stop time. In the train operation management apparatus of this embodiment, the prediction calculation constant DB 42 is not open to the user and cannot be directly viewed by the user. Therefore, the user cannot directly maintain and manage the predicted calculation constant DB 42.

  FIG. 7 shows the constant maintenance GUI 700 created in the process 312 of the constant difference display process routine 46. In this figure, the constant maintenance GUI 700 has a constant statistical period 701 and a drop-down menu 702 for selecting a prediction calculation constant to be maintained. In this embodiment, three months are selected as the constant statistical period, and the stoppage time of the prediction calculation constant is maintained. Furthermore, 700 has a constant selection table 703, the value of the stop time of the previous table 500 (FIG. 5) is displayed in the item “current value”, and the stop of the table 600 (FIG. 6) is displayed in the item “change candidate”. The time value (analysis value) is displayed. A place where the change candidate is blank in 703 indicates a case where the calculated analysis value is the same as the current value, and it is normally unnecessary to update the value. In addition, 700 displays all the select buttons 704 and 703 for selecting all the check boxes of 703, and the records of the install buttons 706 and 703 for installing the items for which the check boxes are selected by the all cancel buttons 705 and 703 for canceling all the check boxes. And a scroll bar 707 for scrolling to the right.

  When maintaining the constants, the user selects a constant to be installed from the check box of the constant selection table 703 using a mouse or the like. In addition, a text box for "change value input" is prepared in 703, and a numerical value can be input by a keyboard or the like at the user's discretion. If a numerical value is input in this field, the value is given priority. It is replaced with the change candidate value. This “change value input” is an optional function that may be deleted if not required. Also, the change candidate field may be overwritten with a numerical value, and when the user inputs individually by overwriting, the color of the character or the like may be changed so that the history remains. In this way, it is preferable in that a compact display can be achieved. In this embodiment, a check box is used. However, as long as it is a selection means for selecting whether or not to update to an analysis value, a button or the like may be used instead of many alternative means.

  If it is found that it is necessary to change the constant statistics period during maintenance work and update the value of the change candidate to the recalculated value, select another period with the radio button of the constant statistics period 701 To do. When another period is selected in 701, the change candidate is refreshed to the newly calculated value. That is, in FIG. 3, the process returns from the process 313 to 301, the analysis result DB 43 is rewritten (301 to 304), and is displayed again on the GUI (311 to 312). Changing the constant statistic period is effective for maintenance of prediction calculation constants that are required due to the increase in platforms due to renovation of the station and the establishment of new facilities that greatly change the flow of people around the station. In the constant statistical period, as shown in FIG. 8 later, a start date and an end date of an arbitrary period may be input in a text box or the like.

  When the user determines that the review of the change candidate value in the constant selection table 703 and the selection of the check box for instructing the change have been completed, the installation button 706 is pressed. As a result, the item selected in the constant selection table 702 is installed in the prediction calculation constant DB 203. Further, when the install button 706 is pressed, the record selected to be changed is updated on the GUI 700 screen so that the current value and the candidate value indicate the same value. As long as the install button 706 is not pressed, the GUI 700 continues to display the same screen. The above description is the operation of 314 and 315 in FIG. In addition, a so-called UNDO button (instruction withdrawal button) may be added to the side of the install button in case that it is desired to restore the value of the wrong installation.

  As described above, the maintenance of the stop time that is one of the prediction calculation constants is completed. When the maintenance of other prediction calculation constants is continued, another prediction calculation constant is selected from a drop-down menu 702 for selecting a prediction calculation constant. As a result, the process returns to 301 from the branch 316 in FIG. 3 and the series of operations described above is repeated.

  The constant maintenance GUI 700 shown in FIG. 7 can be an initial screen when the constant maintenance program 44 is started up. That is, when 44 rises at such time, the constant statistical period 701 is not selected in FIG. 7, the selection item of the prediction calculation constant 702 is blank, and the constant selection table is blank. Next, when a certain constant type is selected in the prediction calculation constant 702, a constant statistical period is selected, and the “execute” button is pressed, a series of processes from 301 in FIG. 3 can be started. Of course, instead of displaying 700, a dedicated initial screen may be provided to start from the initial input screen for the prediction calculation constant and the constant statistical period.

  Further, FIG. 7 shows a graphical user interface on the screen of the display device 20 such as a liquid crystal screen, and a necessary element can be selected by a mouse or a touch panel, or an input device 30 such as a keyboard or an input screen popped up on the screen. It is possible to input numerical values and character characters. However, this is not limited to a display device such as a liquid crystal screen, and a dedicated user interface may be used.

  According to Example 1, the following representative effects can be obtained. (1) The train operation management apparatus according to the present embodiment enables the user to directly maintain the prediction calculation constant by the constant maintenance program. (2) Furthermore, the apparatus according to the present embodiment displays the analysis value, which is the proposed update candidate for the prediction calculation constant, and the current value on the graphical user interface so that they can be compared, so that the prediction calculation constant can be easily maintained. Become. (3) Further, in the apparatus of this embodiment, update candidates and means for instructing update (check boxes and the like) are displayed on the graphical user interface, so that the efficiency of maintenance work is improved. (4) Furthermore, since the statistical period can be changed when analyzing the update candidate of the prediction calculation constant, the apparatus according to the present embodiment can search for the optimum analysis value according to various situations. (5) Moreover, the apparatus of a present Example can improve the precision of an analysis value by providing the filtering function of performance data, and filtering an abnormal value beforehand, when analyzing the update candidate of a prediction calculation constant. Although the advantages of the present embodiment as an apparatus have been described above, this advantage is the same even if the embodiments are regarded as a method invention or a program invention.

  In the first embodiment, in the grouping pattern selection process 301 of FIG. 3, one preliminary calculation constant is selected as an analysis target, and an analysis value is calculated and stored in the analysis result DB 43. However, in some cases, a prediction calculation constant may not be selected in 301, and an analysis value of all prediction calculation constants that may be targeted in 303 may be calculated at once. In this case, the analysis result table in FIG. 5 is created corresponding to all the tables in the prediction calculation DB in FIG. At this time, the YES branch of 316 in FIG. In this method, if the statistical period is changed frequently and the calculation is performed again, the entire table of the prediction calculation DB is recreated, and the load on the computer increases. However, if the statistical period is constant, the target prediction calculation constant is calculated in advance, so that the display refresh when the type of the prediction calculation constant is changed is made efficient.

  FIG. 8 shows a second graphical user interface (GUI) 800 for performing constant maintenance according to the second embodiment of the present invention. The change in comparison with FIG. 7 is that two constant statistical periods can be set as arbitrary periods in 801 and 802. Further, corresponding to the two constant statistics periods, change candidates (1) in the constant selection table 803, check box (1) for selecting the change, change candidate (2), check box for selecting the change (2) ) Is provided. The other elements 702 and 704 to 707 are the same as those in the first embodiment.

  In the present embodiment, analysis values calculated from two constant statistics periods are compared and displayed on a graphical user interface as change candidates, and the convenience of maintenance of the predicted calculation constants is further improved.

  As an example, let us assume that a large commercial facility opened on June 1 as a facility around B station. At this time, it is considered to perform a prediction calculation constant for use in August at the end of July. At this time, it is conceivable to select May 1 to May 31 before opening the large-scale commercial facility in the constant statistics period (1). On the other hand, it is conceivable to select June 1 to June 30 after opening in the constant statistical period (2). In this embodiment, since the optimum prediction calculation constant can be selected while comparing two analysis values having different factors in such a situation, the maintenance accuracy of the prediction calculation constant can be improved.

  The above-described embodiments of the present invention are examples for explaining the present invention, and are not intended to limit the scope of the present invention only to those embodiments. Those skilled in the art can implement the present invention in various other modes without departing from the gist of the present invention.

DESCRIPTION OF SYMBOLS 1 ... Train operation management apparatus, 2 ... Computer main body, 3 ... Network, 4 ... Wide area network, 5 ... Station, 10 ... Input device, 20 ... Display apparatus, 30 ... Calculation part (CPU), 40 ... Memory | storage part, 41 ... Performance diagram DB, 42 ... Prediction calculation constant DB, 43 ... Analysis result DB, 44 ... Constant maintenance program, 45 ... Performance diagram analysis routine, 46 ... Constant difference display routine, 200 ... Train operation plan diagram,
400 ... Track record table, 401 ... Line section, 402 ... Station, 403 ... Number line, 404 ... Identification, 405 ... Schedule classification, 406 ... Train number, 407 ... Arrival time, 408 ... Departure time, 410-440 ... Track record table ,
500 ... Analysis result table 1 (stop time), 501 ... train number, 502 ... station, 503 ... line section, 504 ... identification, 505 ... diagram classification, 506 ... stop time,
600 ... Prediction calculation constant table 1 (stop time), 601 ... Train number, 602 ... Station, 603 ... Line section, 604 ... Identification, 605 ... Diagram classification, 606 ... Stop time, 610 ... Prediction calculation constant table 2 (travel time) ),
700 ... Graphical user interface (GUI), 701 ... Constant statistical period selection button, 702 ... Prediction calculation constant selection drop-down menu, 703 ... Constant selection table, 704 ... Check all select button, 705 ... Check all cancel button, 706 ... Install button for update constant, 707 ... Scroll bar 800 ... Second graphical user interface (GUI), 801 ... Constant statistical period (1) input area, 802 ... Constant statistical period (2) input area, 803 ... Constant selection table 2

Claims (9)

For each of a plurality of vehicles, a first storage area for storing performance data that records the driving performance of each vehicle;
For each of the plurality of vehicles, a second storage area for storing a prediction calculation constant used for calculation for predicting operation of each vehicle;
For each of the plurality of vehicles, an arithmetic unit that extracts the actual data of the first period of each vehicle and calculates an analysis value obtained from statistical processing of the actual data in the first period;
For each of the plurality of vehicles, a first selection unit that displays the prediction calculation constant, the analysis value that is a candidate for changing the prediction calculation constant, and whether to update the prediction calculation constant to the analysis value is displayed. A graphical user interface with areas;
A vehicle operation management device. In claim 1,
The said graphical user interface is a vehicle operation management apparatus further provided with the 2nd area | region for inputting the setting of the said 1st period. In claim 2,
The said graphical user interface is a vehicle operation management apparatus further provided with the 3rd area | region which inputs the period start date and period end date for setting the said 1st period. In claim 1,
The said operation part is a vehicle operation management apparatus which filters the value within the range of the predetermined | prescribed upper limit value and predetermined | prescribed lower limit value among the said performance data, performs the said statistical process, and calculates the said analysis value. In claim 1,
The calculation unit extracts the performance data of the second period for each of the plurality of vehicles, further calculates a second analysis value obtained by statistical processing in the second period,
The graphical user interface is a vehicle operation management device further displaying second selection means for selecting whether to update the second analysis value and the initial constant to the second analysis value in the first area. In claim 5 ,
The vehicle operation management device, wherein the second period is a period before the previous year of the time when the vehicle operation management device makes an operation plan. In any one of Claim 1 to 6 ,
The vehicle is a railway vehicle;
The statistical processing is an average value of the performance data in the first period,
The said graphical user interface is a vehicle operation management apparatus which has a 4th area | region which displays the means for selecting the kind of the said prediction calculation constant which the said calculating part calculates. A vehicle operation management method performed in a vehicle operation management device,
For each of a plurality of vehicles, prepare performance data that records the performance of each vehicle,
For each of the plurality of vehicles, preparing a prediction calculation constant to be used for calculation for predicting future travel of each vehicle,
For each of the plurality of vehicles, extract the actual data of the first period of each vehicle, calculate an analysis value obtained from statistical processing of the actual data in the first period,
For each of the plurality of vehicles, a first selection unit that displays the prediction calculation constant, the analysis value that is a candidate for changing the prediction calculation constant, and whether to update the prediction calculation constant to the analysis value is displayed. Display the area in a graphical user interface,
A vehicle operation management method having a process. The vehicle operation management program for making the said vehicle operation management apparatus as a computer perform the vehicle operation method of Claim 8 .