WO2015063823A1 - Train schedule preparation assist system, and train schedule preparation assist method - Google Patents
Train schedule preparation assist system, and train schedule preparation assist method Download PDFInfo
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- WO2015063823A1 WO2015063823A1 PCT/JP2013/079083 JP2013079083W WO2015063823A1 WO 2015063823 A1 WO2015063823 A1 WO 2015063823A1 JP 2013079083 W JP2013079083 W JP 2013079083W WO 2015063823 A1 WO2015063823 A1 WO 2015063823A1
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
- B61L27/10—Operations, e.g. scheduling or time tables
- B61L27/12—Preparing schedules
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
- B61L27/10—Operations, e.g. scheduling or time tables
- B61L27/16—Trackside optimisation of vehicle or train operation
Definitions
- the present invention relates to a train diagram creation support system and a train diagram creation support method.
- Patent Document 1 Japanese Patent Laid-Open No. 9-123913
- Patent Document 1 states that “the appropriate number of trains, departure delay time, and departure shortening time are determined based on the predicted demand number for each time zone of the representative section of the target line section. It is calculated, and train entry / exit settings are performed at the entry / exit station based on the calculated departure delay time and departure shortening time ”. In the following description, when simply described as “diamond”, it means “train diamond”.
- the train schedule once determined when the train schedule is revised is basically not changed until the next schedule revision. Even if it can be predicted that the demand will change due to factors such as weather and events, it is difficult to change the diagram flexibly in response to short-term demand fluctuations due to problems with the operation of vehicles and crew. As a result, a situation may arise where the transportation demand and the supply transportation capacity do not match. If the supply and transport capacity is excessive, the railway operator will use extra resources such as electricity and vehicles. On the other hand, if the transportation capacity is insufficient with respect to the transportation demand, the passengers will be congested and the convenience of the user may be impaired.
- Patent Document 1 an appropriate number of trains is calculated based on the demand on the day, and the entry / exit of the train on the day is adjusted. However, the entry / exit on and after the next day is not changed. In addition, if the diamond is changed on the day, there is a concern that the burden on the crew and the vehicle base staff will increase because preparations cannot be made in advance.
- One object of the present invention is to provide a train diagram creation support system and a train diagram creation support method that enable a train diagram to be appropriately changed in advance according to a predicted demand fluctuation.
- one aspect of the present invention is a train diagram creation support system for supporting a plan of a train diagram corresponding to demand fluctuation prediction, and is a station belonging to a managed line area
- the number of people getting on and off at each station is calculated from the flow measurement results collected for each station, and stored as flow measurement / external factor result information in association with external information that is information about external factors that cause demand fluctuations.
- the external information related to the external factor that is scheduled to occur in the future is stored as external factor prediction information, and when a change is detected in the external information of the external factor that is scheduled in the future, the external information is changed according to the changed external information.
- the person flow prediction information corresponding to the date and time of occurrence of the change is acquired based on the information, and driving between the stations should be performed based on the number of passengers between the stations of the line section where the external information change included in the person flow prediction information occurs.
- a train number calculation unit for calculating the number of trains.
- the train schedule can be appropriately changed in advance according to the predicted demand fluctuation.
- FIG. 1 is a configuration example of a train diagram creation support system 1 according to an embodiment of the present invention.
- FIG. 2 is a flowchart showing a processing example of the entire train diagram creation support system.
- FIG. 3 is a flowchart showing an example of normalization processing for converting the human flow measurement result into a useful data format.
- FIG. 4 is a flowchart showing an example of normalization processing for converting external information into a useful data format.
- FIG. 5 is a flowchart illustrating an example of a passenger number variation calculation process in each line section and each station.
- FIG. 6 is a flowchart illustrating an example of a human flow predicted value calculation process.
- FIG. 7 is a flowchart illustrating an example of a train number calculation process.
- FIG. 1 is a configuration example of a train diagram creation support system 1 according to an embodiment of the present invention.
- FIG. 2 is a flowchart showing a processing example of the entire train diagram creation support system.
- FIG. 3 is a
- FIG. 8 is a flowchart illustrating an example of a calculation process of the number of trains between stations.
- FIG. 9 is a flowchart illustrating an example of a process for calculating the number of trains for distinguishing lines.
- FIG. 10 is a flowchart illustrating an example of a process for calculating the optimum number of trains.
- FIG. 11 is an example of an output screen in the train diagram creation support system 1.
- FIG. 12 is a flowchart illustrating a train plan, vehicle operation plan, and crew member operation plan processing example.
- FIG. 13A is a configuration example of the human flow measurement result / external factor result table 107.
- FIG. 13B is a configuration example of the human flow measurement result / external factor result table 107.
- FIG. 14 is a configuration example of the external factor prediction table 108.
- FIG. 15 is a configuration example of the human flow prediction table 111.
- FIG. 16 is a configuration example of the number of passengers vs. number of trains table 114.
- FIG. 17 is a configuration example of the train number prediction table 115.
- FIG. 18 is a configuration example of the train number allowable range table 116.
- FIG. 19 is a configuration example of the basic number table 117.
- FIG. 20 is a configuration example of the alternative route table 118.
- FIG. 1 is a diagram illustrating a configuration example of a diagram creation support system 1 according to the present embodiment.
- the diagram creation support system 1 includes a human flow measurement / prediction device 100 (human flow prediction unit), a diagram creation device 200, a human flow prediction database 300, an input / output device 400, an external information receiving device 600, and a human flow measurement device 700.
- the components are communicably connected by a communication network.
- the human flow measurement / prediction device 100 has, for each railway line section for which the schedule is created, the human flow measurement data at each station received from the flow measurement device 700, the calendar data received from the external information reception device 600, and the weather prediction. It has a function of correlating external information data such as data and event holding data with each other, predicting the number of train passengers at each station in the target line section, and storing the result in the human flow prediction database 300.
- the human flow measuring device 700 is configured as a computer having a function of calculating human flow measurement data from a human flow monitoring device such as a camera and a sensor installed in each station, and having a communication function.
- Human flow measurement can be carried out by a known method, and for example, related technology is described in Japanese Patent Application Laid-Open No. 2013-116677.
- the external information receiving device 600 receives external information that may cause fluctuations in the flow of people at each station, such as current and future weather and events, from an information server provided at a railway company or the like where the system 1 is installed. It is an apparatus which acquires and transmits to the human flow measurement / prediction apparatus 104, and can be configured as a computer having a communication function.
- the external information receiving device 600 includes an external information receiving unit 601 and an external information raw data table 602.
- the external information receiving unit 601 receives external information from an information server or the like not shown.
- the external information raw data table 602 stores the raw data received from the external information receiving unit 601.
- the external information raw data means a group of data that has been acquired from an information server or the like and has not been processed, and this external information raw data is used in the human flow measurement / prediction device 100 as described later. It is processed into a data format suitable for processing in
- the human flow measurement / prediction device 100 includes a processor 110, a memory 120, and a data input / output unit 130.
- the processor 110 is an arithmetic device such as a CPU (Central Processing Unit) or an MPU (MicroProcessing Unit), and executes a program that realizes functions of the human flow measurement / prediction device 100 described later.
- the memory 120 has storage devices such as a ROM (Read Only Memory) and a RAM (Random Access Memory), and stores programs for realizing the functions of the human flow measurement / prediction device 100 described later, and data used by these programs. ing. Although not shown, the memory 120 is also mounted with system software (operating system, OS) that is the execution base of the program.
- OS operating system
- the human flow measurement / prediction device 100 includes an auxiliary storage device such as a hard disk drive and a semiconductor drive in addition to the memory 120, and reads the program and data from the auxiliary storage device to the memory 120 as necessary. Also good.
- the data input / output unit 130 has an interface function for executing data transmission / reception processing with another device via a communication network, and an input / output processing function for the data.
- the human flow processing unit 121 has a function of converting a human flow measurement result received from the human flow measurement device 700 into a data format suitable for processing in the human flow measurement / prediction device 100.
- the external information processing unit 122 has a function of converting raw external information data received from the external information receiving device 600 into a data format suitable for processing in the human flow measurement / prediction device 100.
- the change amount calculation unit 123 has a function of calculating the change amount of the number of passengers between the stations in the line area that is the processing target of the human flow measurement / prediction apparatus 100.
- the human flow calculation unit 124 has a function of predicting future human flow fluctuations in the line segment to be processed based on the data processed by the human flow processing unit 121 and the external information processing unit 122. Details of the processing by these programs will be described later using a processing flow example.
- a human flow measurement / external factor result table 125 (human flow measurement external factor result information) storing data used by the program for realizing the function
- an external factor prediction table 126 external factor prediction information
- the human flow measurement / external factor result table 125 stores the human flow measurement results associated with each other and the external factors that cause the fluctuation of the human flow extracted from the external information.
- the external factor prediction table 126 stores future external factor prediction values. Specific configuration examples of the human flow measurement / external factor result table 125 and the external factor prediction table 126 will be described later together with a processing flow example of a related program.
- the diagram creation device 200 is configured as a computer having a communication function including a processor 210, a memory 220, and a data input / output unit 240, and is calculated by the human flow measurement / prediction device 100. It has the function of calculating the number of future trains according to the demand from the forecast and realizing support for the train plan, vehicle operation plan, and crew member operation plan.
- the train schedule creation device 200 may include an auxiliary storage device such as a hard disk drive or a semiconductor drive in addition to the memory 220.
- the memory 220 stores a program for realizing the function of the diagram creation device 200.
- the number-of-trains calculation unit 221 has a function of calculating the number of trains that run on the target line according to the predicted human flow.
- the diagram creation calculation unit 222 has a function of providing data such as the number of trains set corresponding to the demand forecast to these planning departments in order to execute a future train plan, a vehicle operation plan, and a crew member operation plan. Have.
- the memory 220 is also mounted with system software (operating system, OS) as an execution base of the program.
- Number of passengers vs. train number table 223 (number of passengers vs. number of trains information), number of trains prediction table 224 (train number prediction information), number of trains allowed
- a range table 225 (overall and inter-station train number allowable range information), a basic number table 226 (basic number information), and an alternative route table 227 (alternative route information) are stored.
- the number of passengers vs. number of trains table 223 stores information for calculating the number of trains according to the number of passengers at each station in the target line area.
- the train number prediction table 224 stores an appropriate number of future trains according to the flow of people at each station.
- the basic number table 226 stores a range of the number of trains that can travel between each line section and each station.
- the basic number table 226 stores the basic number of trains traveling on the route for each day of the week and each time zone.
- the alternative route table 227 stores data of other routes that can be substituted when the number of trains corresponding to the human flow prediction cannot be set in each section of the target line section. Configuration examples of these tables will be described later with reference to a processing flow example of a program that uses them.
- a train plan table 228 that stores a future train plan, a vehicle operation plan, and a crew plan created in the memory 220 based on the number of trains in the target line section set by the train schedule creation device 200 from the demand prediction
- a vehicle operation plan table 229 and a crew member operation plan table 230 are stored.
- the contents of these tables are, for example, result data created in each planning department in order to specifically realize the number of trains based on the demand prediction calculated by the train diagram creation device 200 for the target line section.
- the input / output device 400 is calculated by the input device such as a keyboard, a mouse, and a touch panel for giving data input and operation command input to the human flow measurement / prediction device 100 and the diagram creation device 200 and the diagram creation device 200. It includes output devices such as liquid crystal displays, LED panels, and printers for displaying information such as the number of trains and the resulting diagram.
- the human flow prediction database 300 is a database used in common by the human flow measurement / prediction device 100 and the diagram creation device 200, and can be configured as a computer having a communication function.
- the human flow prediction database 300 stores future human flow prediction values generated by the human flow measurement / prediction device 100. Since the human flow prediction database 300 is used in common by the human flow measurement / prediction device 100 and the diagram creation device 200, accurate and prompt information transmission to both devices is possible.
- the human flow prediction database 300 may be provided as independent hardware as shown in FIG. 1 or may be provided integrally with any component of the system 1 such as the human flow measurement / prediction device 100.
- the related device 500 is a terminal device installed in an in-service department, for example, a vehicle base, a crew base, etc., for specifically realizing the plan diagram calculated by the diagram creation device 200.
- the diagram creation support system 1 of the present embodiment the diagram creation support processing based on human flow prediction is realized by the configuration as described above. This makes it possible to flexibly create future diagrams that meet demand.
- the operation management system etc. which actually carry out operation control of the train by the diamond created by the support function of this system 1 can also be assumed.
- FIG. 2 is a flowchart showing a processing example of the entire diagram creation support system 1 of the present embodiment.
- the system 1 starts the entire process according to a process execution schedule set in the human flow measurement / prediction apparatus 100 or the like in advance or by a process start input from the input / output device 400 (S10). Thereafter, the human flow measurement / prediction apparatus 100 first receives the current flow measurement result from the human flow measurement apparatus 700 and simultaneously receives external information from the external information apparatus 600 that causes fluctuations in the human flow (S11).
- the human flow measurement / prediction apparatus 100 extracts information to be used in the system 1 from the received human flow measurement result and the raw data of the external information, and converts the data into a data format that can be stored in the memory 120 (hereinafter, “ (Referred to as “normalization”) (S12).
- the human flow measurement / prediction device 100 calculates the amount of change in the number of passengers in each line section and each station, which is the management target of the present system 1, from the normalized data, and stores it in the memory 120 (S13).
- the human flow measurement / prediction apparatus 100 determines that the external factor prediction has changed when normalizing the external information in the future, the external factor prediction is changed from the external factor prediction and the stored passenger amount change amount.
- the human flow prediction process is executed at the date and time (S14).
- the calculated human flow prediction result data is stored in the human flow prediction database 300 and shared with the diagram creation device 200.
- the diagram creating apparatus 200 calculates the number of trains that can be handled based on the human flow prediction result data stored in the human flow prediction database 300, and outputs it to the display of the input / output device 400 (S15).
- a train schedule is created in each section of the train plan, vehicle operation plan, and crew operation plan, and a future schedule is formed by formulating the vehicle operation plan and crew operation plan (S16). ).
- the planned future diagram is transmitted to the related device 500 by the diagram creation device 200, and this process is terminated (S17, S18).
- a diagram that can provide a transportation capacity in accordance with a change in transportation demand is planned. be able to.
- FIG. 3 is a flowchart illustrating an example of a normalization process of human flow measurement results.
- the human flow processing unit 121 of the human flow measurement / prediction device 100 receives the current flow measurement result from the flow measurement device 700 and first converts the data into hourly flow data (S101).
- the human flow processing unit 121 converts the data into human flow measurement data for each management target line section (S102), calculates the number of passengers before the train arrives at each station, and the number of passengers after departure (S103).
- the data is stored as data converted (normalized) in the measurement / external factor result table 125, and the process is terminated (S104, S105).
- the human flow measurement / external factor result table 125 stores the daily flow measurement result and the external factor result of the day.
- the human flow measurement result includes the date, start time, end time, The following items are recorded: line area, up / down classification, station, number of passengers (before arrival), number of passengers (after departure), and change amount.
- the human flow measurement / external factor result table 125 stores daily human flow measurement results and external factor results for a predetermined period retroactive from the current day.
- the start time and end time indicate the measurement time zone of the corresponding human flow measurement result in 24-hour notation.
- the name of the route such as “AA line” is stored in the line section.
- the operation direction in the line section is stored for the train subject to human flow measurement.
- the station the name of the station subject to human flow measurement is stored.
- the number of passengers (before arrival) and the number of passengers (after departure) store the number of passengers at the time of departure when arriving at the station.
- a value obtained by subtracting the value of the number of passengers (before departure) from the number of passengers (after departure) calculated in the passenger number variation calculation process (FIG. 5) described later is stored.
- the number of people getting on and off at each station records the state of getting on and off of the trains coming and going to each home by means of measuring devices such as cameras and human sensors installed at each station, The number of passengers can be extracted and determined. As for the amount of change, for example, when “+500” is recorded, it indicates that the number of passengers exceeds the number of people getting off in the corresponding time zone at the corresponding station.
- the number of people getting on and off each station based on the current flow measurement result on the day can be grasped for each time zone.
- FIG. 4 is a flowchart illustrating an example of normalization processing of external information.
- the external information processing unit 122 of the human flow measurement / prediction device 100 starts processing in S200, it receives external information from the external information raw data table 602 of the external information receiving device 600, and first converts the external information into date and hourly external information. Data conversion is performed (S201).
- the external information includes raw data such as weather along the railway, temperature, wind speed, precipitation, operation information on other railway lines, other companies' lines, bus routes, etc., event information along the railway, and calendar information.
- Raw data means that the data stored in the information server of the railroad company that is the recipient of external information is handled unprocessed, and the data format depends on the system configuration of the recipient, etc. Can take various forms.
- the external information processing unit 122 extracts external information as an external factor that influences the flow of people and performs data conversion (normalization) (S202). It is determined whether the normalized external factor information is information of the current day (S203). If it is determined that the information is current day information (S203: current day), it is registered in the human flow measurement / external factor result table 125 and processed. Is finished (S204, S208).
- the external information processing unit 122 stores the normalized data in the external factor prediction table 126 (S206). Next, the external information processing unit 122 transmits the change information to the human flow calculation unit 124 if the external factor predicted value of the date and time stored in the past is changed, and ends the processing (S207, S208). When it is determined in S205 that there is no change in the external factor prediction, the external information processing unit 122 ends the process as it is (S208).
- factors related to fluctuations in human flow can be extracted from external information acquired from an information server of a railway company and used in a format suitable for processing by the human flow measurement / prediction apparatus 100.
- FIG. 13B shows a configuration example of the human flow measurement / external factor result table 125.
- items such as a day of the week, a holiday (for example, record the symbol if it falls on a holiday), weather, temperature, wind speed, precipitation amount, operation information, and event are associated with the corresponding date. It is extracted from the information and recorded.
- operation information items such as a target company, line section, and event (construction, etc.) are stored when the operation of other line sections or other companies is different from the normal operation.
- the event when the event is held along the line, items such as the presence / absence of the event, the venue, and the number of customers are stored.
- FIG. 14 shows a configuration example of the external factor prediction table 126 that stores future external factor prediction values.
- future external factor information that affects the human flow normalized by the external information processing unit 122, is stored from the next day to one week later.
- the external information processing unit 122 changes the corresponding data (S205: Yes, S206 in FIG. 4).
- the items recorded in the external factor prediction table 126 are the same as those in the human flow measurement / external factor result table 125 in FIGS.
- FIG. 5 is a flowchart illustrating an example of a passenger number variation calculation process in each line section and each station that is the management target of the system 1.
- the change amount calculation unit 123 of the human flow measurement / prediction apparatus 100 starts the process in S300, it acquires information on the current day from the human flow measurement result stored in the human flow measurement / external factor result table 125 in FIG. 13A (S301), By subtracting the value of the number of passengers before arrival from the value of the subsequent number of passengers, the amount of change in the number of passengers in each line section and each station is calculated (S302).
- the change amount calculation unit 123 updates the human flow measurement / external factor result table 125 with the calculated change amount, and ends the processing (S303, S304).
- the number of passengers in each line section and each station is updated on a daily basis based on the result of the measurement of people flow.
- FIG. 6 is a flowchart illustrating an example of a human flow predicted value calculation process.
- the flow calculation unit 124 of the flow measurement / prediction apparatus 100 starts processing in S400, it receives the change information transmitted from the external information processing unit 122 (S401), and from the external factor prediction table 126 based on the change information.
- the external factor prediction item of the date and time when the change is made is acquired (S402).
- the start timing of this processing flow can be determined every predetermined time, and the processing may be started upon reception of change information.
- the human flow calculation unit 124 acquires all of the human flow measurement / external factor results of the same external factor condition as the acquired external factor prediction of the acquired date / time from the human flow measurement / external factor result table 125 (S403). That is, in the external factor prediction table 126, all records that record the day of the week, holidays, weather, temperature, wind speed, precipitation, operation information, or events that match the change information are acquired. Next, the human flow calculation unit 124 extracts only the human flow measurement / external factor result of the target line section for creating a diagram from the acquired human flow measurement / external factor result (S404).
- the human flow calculation unit 124 designates one station included in the corresponding line section, extracts the human flow measurement / external factor result of the station (S405), and extracts the current flow measurement / external factor result from the extracted human flow measurement / external factor result.
- the average value of the change in the number of passengers recorded for the station is calculated (S406). This is because when a certain external factor is changed, the results of changes corresponding to the relevant external factor are averaged to obtain a more reliable prediction result.
- the flow calculation unit 124 determines whether or not the average amount of change for all stations has been calculated in S407 (S407), and repeats the processing of S405 and S406 until it is determined that all stations have been calculated (S407: Yes). Calculate the average change in the number of passengers at each station from the first station to the last station in the ward. Next, when the calculation is completed at all stations, the person flow calculation unit 124 calculates the number of passengers between the stations from the first station to the last station by adding the calculated average values in order from the first station (S408). The inter-station passenger count calculation result is stored in the human flow prediction database 300 (S409). The human flow calculation unit 124 transmits the external factor change information to the diagram creating apparatus 200 and ends the process (S410, S411).
- FIG. 15 is a configuration example of the human flow prediction database 300 that stores the results of the human flow prediction calculated from the records of the future external factor prediction table 126 and the past human flow measurement / external factor result table 125.
- the number of passengers by station is recorded for each station from the first station to the last station of the corresponding line section in association with the time zone, line section, and up / down section.
- “In the time zone from 8:00 to 9:00 on October 2, 2013, 1300 people get on the section from the first station to the second station (next station) on the AA line. "Yes" is recorded. Since this human flow prediction database 300 is also shared by the diagram creation apparatus 200, accurate and quick information transmission in the system 1 is possible.
- the human flow calculation unit 124 it is possible to predict a future human flow from past human flow measurement values and future external factor predictions. Thereby, the transportation demand required to determine the number of trains operated can be calculated. Since the accuracy of the expected value increases as the accumulation of statistical values increases, the average value (expected value) of the amount of change in the number of passengers is gradually increased by accumulating daily flow measurement / external factor result table 125 records. The calculation accuracy of is expected to improve. In addition, since the accuracy of external factor prediction improves as it approaches the day, it is expected that the accuracy of the human flow prediction calculated based on it will also improve as it approaches the day.
- FIG. 7 is a flowchart illustrating an example of a train number calculation process.
- the number-of-trains calculation process the number of trains that can correspond to the transportation demand is calculated based on the results of the prediction of the flow of people stored in the prediction database 300 of the flow of people.
- the train number calculation unit 221 of the diagram creation apparatus 200 When the train number calculation unit 221 of the diagram creation apparatus 200 starts the process in S500, the train number calculation unit 221 receives the change information transmitted from the human flow calculation unit 124 of the human flow measurement / prediction apparatus 100 (S501), and receives the change information from the human flow prediction database 300. Human flow prediction data for a certain date and time is acquired (S502). As shown in FIG. 15, since it is possible to obtain the number of passengers between stations from the human flow prediction data, the train number calculation unit 221 determines the number of trains between stations according to demand from the number of passengers between stations. The number is calculated (S503).
- the train number calculation unit 221 calculates the number of trains traveling in the entire line area from the calculated number of trains between stations (S504). Next, the train number calculation unit 221 determines whether the calculated number of trains between the stations and the number of trains in the entire line area are appropriate. The number of trains is calculated (S505). Next, the train number calculation unit 221 outputs the calculated result to an output device such as a display of the input / output device 400 (S506). In addition, the train number calculation unit 221 transmits change information regarding the number of trains to the diagram creation calculation unit 222 and ends the process (S507, S508). Through the above processing, information related to the number of trains calculated according to changes in external factors is presented to the person in charge implementing the train plan. As the minimum configuration of the present invention, the processing up to the presentation of the number of trains between stations calculated in S503 in FIG. 7 is executed, and the subsequent matching with the specific train setting conditions is performed in another configuration. It is also possible.
- FIG. 8 is a flowchart illustrating a calculation process example (S503) of the number of trains by station.
- the train number calculation unit 221 first uses the number of passengers between stations acquired from the human flow prediction database 300 to determine the number of trains between the line segment and the stations from the number of passengers vs. train number table 223. Obtain (S601), and calculate the appropriate number of inter-station trains for the number of passengers based on the prediction of human flow (S602).
- FIG. 16 shows a configuration example of the number of passengers vs. number of trains table 223.
- the number of passengers vs. number of trains table 223 is a table in which an appropriate number of trains for the number of passengers between stations in a specific line section is set in advance based on the boarding capacity, and is associated with the line section and section (between stations). Each item of passenger number lower limit value, passenger number upper limit value, and number is recorded. Referring to the example of FIG. 16, in the section AB of the line section AA, it is shown that the appropriate number of trains is 9 when the number of passengers is estimated to be 1000 to 1200. The number of trains according to demand can be determined from the number of passengers vs. number of trains table 223. Next, the train number calculation unit 221 registers the calculated number of trains between stations in the train number prediction table 224 and ends the process (S603, S604).
- FIG. 17 is a configuration example of the train number prediction table 223.
- the number-of-trains prediction table 223 is a table for storing the number of future trains according to the demand prediction. Corresponding to the corresponding date, day of the week, start time, end time, line section, up / down classification, number of trains , Basic number, and difference items are recorded. Describing items peculiar to the table 223, the number of trains to be run calculated in S602 of FIG. 8 is stored in the number of trains between each station from the start station to the end station of the corresponding line section.
- the basic number stores the basic number of trains that travel on the entire line section at the time of the day and time acquired in S707 of the line-differentiated train number calculation process described later.
- the difference stores the difference between the number of trains traveling in the entire line section and the basic number calculated in S708. Note that the processing of S601 to S603 in FIG. 8 is performed for each station in the corresponding line section, and appropriate calculation of the number of trains and registration in the train number prediction table 223 are performed for all stations in the line section. .
- FIG. 9 is a flowchart illustrating a calculation processing example (S504) of the number of line-differentiated trains illustrated in FIG.
- the train number calculation unit 221 starts processing in S700, first, the train number prediction value for each station in the relevant line section is obtained from the train number prediction table 223 (S701), and the obtained train number for each station in the relevant line section is obtained. In step S702, the minimum number of trains is calculated. Next, the train number calculation unit 221 obtains the allowable range of the number of trains that can run in the entire line section and between the stations from the train number allowable range table 225 (S703), and calculates the number of trains for each station calculated in advance.
- the train number calculation unit 221 sets the minimum value as the total number of traveling lines (S705). If it is determined in S704 that the minimum value of the number of trains between stations is not within the allowable range (S704: No), the train number calculation unit 221 sets the lower limit value when the minimum value falls below the lower limit value of the allowable range. When the minimum value exceeds the upper limit value of the allowable range, the upper limit value is set as the number of traveling lines for the entire line section (S706).
- FIG. 18 shows a configuration example of the train number allowable range table 225.
- the train number allowable range table 225 is set in advance before the operation of the system 1, and stores the lower limit value and the upper limit value of the number of trains that can run per hour between each line section and each station. For the ward, the total number lower limit value, the total number upper limit value, and the train number upper limit value and the lower limit value between each station from the starting station to the end station of the corresponding line ward are recorded.
- the overall number lower limit value and the overall number upper limit value store the lower limit value and the upper limit value of the number of trains that can travel the entire line section.
- the lower limit value and the upper limit value of the number of trains between the stations store the lower limit value and the upper limit value of the number of trains that can travel between the stations. From this table, it is possible to determine whether the number of trains between stations and the number of trains in the entire line area are appropriate.
- the train number calculation unit 221 obtains the basic number of the day of the week and the date / time from the basic number table 226 (S707), calculates the difference between the basic number and the total number of traveling lines (S708), and the number of trains
- the prediction table 223 is updated, and the process ends (S709, S710).
- FIG. 19 shows a configuration example of the basic number table 226.
- the basic number table 226 stores the basic number of trains traveling in the relevant line section for each day of the week and each time zone, and is set based on the basic demand forecast of each line section before the operation of the system 1 is started.
- FIG. 10 is a flowchart showing an example of the optimum train number calculation process (S505) shown in FIG.
- the train number calculation unit 221 starts the process in S800, the train number calculation unit 221 first acquires the number of trains for each station in the train line registered in the train number prediction table 223 (S801). The allowable range of the number of trains that can be traveled between the stations is acquired (S802), and the number of trains for each station recorded in the train number prediction table 223 and between the stations recorded in the train number tolerance table 225 The allowable number of trains is compared (S803).
- the train number calculation unit 221 sets the lower limit value as the optimum value of the number of stations, and ends the process. (S804, S809).
- the train number calculation unit 221 uses the number of trains between stations acquired from the train number prediction table 223 as the optimum number of trains between the stations. The value is set as a value and the process is terminated (S805, S809).
- the train number calculation unit 221 sets the upper limit value as the optimum value of the number of trains between the stations (S806).
- the difference between the number of trains between stations acquired from the train number prediction table 223 (the number of trains calculated from demand based on human flow prediction) and the upper limit value is set as an ideal value (S807).
- the train number calculation unit 221 refers to the alternative route table 227 and checks whether there is an alternative route candidate for the corresponding section. If there is a registration, the candidate is acquired and the processing is terminated (S808, S809). It should be noted that the processing of S803 to S808 is performed for each station in the target line section, thereby setting the optimum number of trains between all stations in the line section.
- FIG. 20 shows a configuration example of the alternative route table 227.
- the alternative route table 227 stores substitutable route data relating to each station for the management target line section of the system 1 and is set in advance before the operation of the system 1 is started.
- the alternative route table 227 stores the items of line sections, between stations, and alternative companies / routes in association with each other.
- FIG. 11 is an example of an output screen obtained by the train number calculation process of the diagram creation device 200.
- This output screen can be displayed when the train number calculation processing by the human flow measurement / prediction device 100 and the diagram creation device 200 is completed in accordance with the change of the external information.
- the date, day of the week, line section, time zone, up / down section, the total number of trains set for the line section, and the number of stations between stations are displayed on the output screen.
- the display item data relating to the number of trains is stored in the memory 220 of the diagram creating apparatus 200 in a suitable output format, which is calculated up to the time when the optimum train number calculation process (S505 in FIG. 7) is completed. It can be configured to perform display output or the like.
- the display item is provided with an input means such as a pull-down menu so that the person in charge of diagram creation can specify data to be viewed, the usability as the system 1 can be further improved. For example, if the date, day of the week, line section, time zone, and up / down classification can be specified on the screen, the set number of trains in the desired line section, time zone, etc. can be called and confirmed.
- This output screen enables the person in charge of diagram creation to check the number of trains according to demand for each line section.
- the schedule for making the number of trains according to demand run at an appropriate time can be planned.
- FIG. 12 shows a flow example of a train plan, vehicle operation plan, and crew member operation plan process to which the diagram creation support system 1 of the present embodiment is applied.
- the schedule creation calculation unit 222 receives the change information transmitted from the train number calculation unit 221 (S901). Are acquired from the train plan table 228, the vehicle operation plan table 229, and the crew member operation plan table 230 (S902).
- the person in charge of diamond creation confirms the necessary number of trains with reference to the output screen illustrated in FIG. 11, and creates or changes a train plan (S903).
- a change request is made to the person in charge of the diagram creation in the other line section or a diamond change request is made to another company.
- the person in charge of creating a diagram formulates a vehicle operation plan (S904) and a crew operation plan for the train (S905).
- the person in charge of schedule creation registers the change schedule in the train plan table 228, the vehicle operation plan table 229, and the crew member operation plan table 230, and ends the processing (S906, S907).
- the diagram creation support system 1 of the present embodiment it becomes possible to create and change a future diagram flexibly according to demand.
- resources such as electric power and vehicles can be used efficiently, which can contribute to stabilization of management.
- schedule changes can be planned in advance based on future demand forecasts, so crew officers and vehicle base managers can prepare in advance operations changes that accompany schedule changes, reducing the burden of schedule changes It becomes possible to make it.
- this invention is not limited to the above-mentioned Example, Various modifications are included.
- the above-described embodiments have been described in detail for easy understanding of the present invention, and are not necessarily limited to those having all the configurations described.
- a part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of one embodiment.
- Diamond creation support system 100 Human flow measurement / prediction device 110 Processor 120 Memory 121 Human flow processing unit 122 External information processing unit 123 Change calculation unit 124 Human flow calculation unit 125 Human flow measurement / external factor result table 126 External factor prediction table 200
- Diamond creation device 210 Processor 220 Memory 221 Train number calculation unit 222 Diagram creation calculation unit 223 Number of passengers vs. train number table 224 Train number prediction table 225 Train number allowable range table 226 Basic number table 227 Alternative route table 228 Train plan table 229 Vehicle operation plan table 230 Crew member operation plan table 300 Human flow prediction database 400 Input / output device 500 Related device 600 External information receiving device 601 External information receiving unit 602 External information raw data table 700 Human flow Measuring apparatus
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Abstract
In order to enable proper prior modification of a train schedule in accordance with predicted demand variation, a train schedule preparation assist system (1) is mainly provided with a human flow measurement/prediction device (100) and a schedule preparation device (200). In the human flow measurement/prediction device (100), a human flow measurement result from a human flow measurement device (700) and information about an external factor that may have an influence on transport demand received from an external information reception device (600) are stored in association with each other. Upon detection of a modification in the external factor, the human flow measurement/prediction device predicts the number of passengers getting aboard between stations in a target track section on the basis of a past human flow measurement result associated with the modified external factor, and stores the predicted number in a human flow prediction database (300). The schedule preparation device (200) acquires the predicted value of the number of passengers getting aboard between the stations from the human flow prediction database (300) with regard to the track section that has experienced the modification in the external factor, and extracts the corresponding number of trains between the stations with reference to an on-board passenger number versus train number table (223) that is retained in advance for the relevant track section.
Description
本発明は、列車ダイヤ作成支援システム及び列車ダイヤ作成支援方法に関する。
The present invention relates to a train diagram creation support system and a train diagram creation support method.
従来の鉄道輸送計画では、輸送需要、運営主体の経営方針等を基に、基本的な列車ダイヤ策定を行う。そして、このように策定された列車ダイヤは比較的長期間に渡って運用される。そのため、需要の変化がその直前に予測できても、設定されている列車ダイヤを柔軟に変更することは難しい。この点、特開平9-123913号公報(特許文献1)には、「対象線区の代表区間の各時間帯ごとの予測需要数に基づいて適正な列車本数、出発遅延時間、出発短縮時間を算出し、算出した出発遅延時間や出発短縮時間に基づいて入出庫駅で列車の入庫や出庫の設定を行う」と記載されている。なお、以下、単に「ダイヤ」と表記した場合、「列車ダイヤ」を意味するものとする。
In the conventional rail transport plan, basic train schedules are formulated based on transport demand and management policy of the management entity. The train schedule formulated in this way is operated for a relatively long period of time. Therefore, even if a change in demand can be predicted immediately before, it is difficult to flexibly change the set train schedule. In this regard, Japanese Patent Laid-Open No. 9-123913 (Patent Document 1) states that “the appropriate number of trains, departure delay time, and departure shortening time are determined based on the predicted demand number for each time zone of the representative section of the target line section. It is calculated, and train entry / exit settings are performed at the entry / exit station based on the calculated departure delay time and departure shortening time ”. In the following description, when simply described as “diamond”, it means “train diamond”.
前記のように、現行の輸送計画ではいったん列車ダイヤ改正時に決定された列車ダイヤは、次回のダイヤ改正まで基本的には変更されない。天候やイベント等の要因で需要が変わると予測できたとしても、車両、乗務員の運用の問題もあり、ダイヤを短期的な需要変動に応じて機動的に変更することは難しい。そのため、結果として輸送需要と供給輸送力が見合わない状況が生じることがある。供給輸送力が過大であれば、鉄道運営主体は電力や車両など余分なリソースを使用することになる。逆に、輸送需要に対して輸送力が不足した場合には、乗客に混雑を強いることとなって、利用者の利便性を損なうおそれがある。特許文献1では、当日の需要によって適正な列車本数を算出し、当日の列車の入出庫を加減することとしているが、翌日以降の入出庫については変更する対応を行わない。また、当日にダイヤを変更した場合、事前に準備することができないので乗務員や車両基地担当者への負担が大きくなる懸念もある。
本発明の一つの目的は、予測される需要変動に応じて列車ダイヤをあらかじめ適切に変更することを可能とする列車ダイヤ作成支援システム及び列車ダイヤ作成支援方法を提供することである。 As described above, in the current transportation plan, the train schedule once determined when the train schedule is revised is basically not changed until the next schedule revision. Even if it can be predicted that the demand will change due to factors such as weather and events, it is difficult to change the diagram flexibly in response to short-term demand fluctuations due to problems with the operation of vehicles and crew. As a result, a situation may arise where the transportation demand and the supply transportation capacity do not match. If the supply and transport capacity is excessive, the railway operator will use extra resources such as electricity and vehicles. On the other hand, if the transportation capacity is insufficient with respect to the transportation demand, the passengers will be congested and the convenience of the user may be impaired. InPatent Document 1, an appropriate number of trains is calculated based on the demand on the day, and the entry / exit of the train on the day is adjusted. However, the entry / exit on and after the next day is not changed. In addition, if the diamond is changed on the day, there is a concern that the burden on the crew and the vehicle base staff will increase because preparations cannot be made in advance.
One object of the present invention is to provide a train diagram creation support system and a train diagram creation support method that enable a train diagram to be appropriately changed in advance according to a predicted demand fluctuation.
本発明の一つの目的は、予測される需要変動に応じて列車ダイヤをあらかじめ適切に変更することを可能とする列車ダイヤ作成支援システム及び列車ダイヤ作成支援方法を提供することである。 As described above, in the current transportation plan, the train schedule once determined when the train schedule is revised is basically not changed until the next schedule revision. Even if it can be predicted that the demand will change due to factors such as weather and events, it is difficult to change the diagram flexibly in response to short-term demand fluctuations due to problems with the operation of vehicles and crew. As a result, a situation may arise where the transportation demand and the supply transportation capacity do not match. If the supply and transport capacity is excessive, the railway operator will use extra resources such as electricity and vehicles. On the other hand, if the transportation capacity is insufficient with respect to the transportation demand, the passengers will be congested and the convenience of the user may be impaired. In
One object of the present invention is to provide a train diagram creation support system and a train diagram creation support method that enable a train diagram to be appropriately changed in advance according to a predicted demand fluctuation.
上記の目的等を達成するために、本発明の一態様は、需要変動予測に対応した列車ダイヤの計画を支援するための列車ダイヤ作成支援システムであって、管理対象である線区に属する駅ごとに収集された人流計測結果から各駅での単位時間あたりの乗降人数を算出し、需要変動の要因となる外部要因に関する情報である外部情報と関連付けて人流計測・外部要因結果情報として格納し、将来発生が予定されている外部要因に関する前記外部情報を外部要因予測情報として格納し、将来予定されている前記外部要因の前記外部情報について変更を検知した場合、変更された当該外部情報により前記外部要因予測情報を更新するとともに、変更された前記外部情報と一致する前記外部情報を含む前記人流計測・外部要因結果情報を取得し、当該人流計測・外部要因結果情報から前記外部情報変更が発生する線区の各駅間の乗車人数を算出し、人流予測情報として格納する人流予測部と、前記人流予測部から受信した前記外部情報の変更に基づいて、当該変更発生日時に対応する前記人流予測情報を取得し、当該人流予測情報に含まれる前記外部情報変更が発生する線区の各駅間の乗車人数に基づいて各駅間で運転すべき列車本数を算出する列車本数計算部とを備えている。
In order to achieve the above object and the like, one aspect of the present invention is a train diagram creation support system for supporting a plan of a train diagram corresponding to demand fluctuation prediction, and is a station belonging to a managed line area The number of people getting on and off at each station is calculated from the flow measurement results collected for each station, and stored as flow measurement / external factor result information in association with external information that is information about external factors that cause demand fluctuations. The external information related to the external factor that is scheduled to occur in the future is stored as external factor prediction information, and when a change is detected in the external information of the external factor that is scheduled in the future, the external information is changed according to the changed external information. Update the factor prediction information and acquire the human flow measurement / external factor result information including the external information that matches the changed external information, and Calculate the number of passengers between each station in the line section where the external information change occurs from the flow measurement / external factor result information, and store the current flow prediction information and the change of the external information received from the flow prediction unit The person flow prediction information corresponding to the date and time of occurrence of the change is acquired based on the information, and driving between the stations should be performed based on the number of passengers between the stations of the line section where the external information change included in the person flow prediction information occurs. A train number calculation unit for calculating the number of trains.
本発明の一態様によれば、予測される需要変動に応じて列車ダイヤをあらかじめ適切に変更することが可能となる。
According to one aspect of the present invention, the train schedule can be appropriately changed in advance according to the predicted demand fluctuation.
以下、本発明の実施形態について、添付図面を用いて説明する。図1は、本実施形態におけるダイヤ作成支援システム1の構成例を示す図である。ダイヤ作成支援システム1は、人流計測・予測装置100(人流予測部)、ダイヤ作成装置200、人流予測データベース300、入出力装置400、外部情報受信装置600、及び人流計測装置700を備え、これらの構成要素は通信ネットワークによって通信可能に接続されている。
Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. FIG. 1 is a diagram illustrating a configuration example of a diagram creation support system 1 according to the present embodiment. The diagram creation support system 1 includes a human flow measurement / prediction device 100 (human flow prediction unit), a diagram creation device 200, a human flow prediction database 300, an input / output device 400, an external information receiving device 600, and a human flow measurement device 700. The components are communicably connected by a communication network.
人流計測・予測装置100は、ダイヤ作成の対象となっている鉄道の線区ごとに、人流計測装置700から受信する各駅における人流計測データ、及び外部情報受信装置600から受信するカレンダーデータ、天候予測データ、イベント開催データ等の外部情報データを互いに関連付けて、対象線区の各駅における列車乗降人数の予測を行ってその結果を人流予測データベース300に格納する機能を有する。
The human flow measurement / prediction device 100 has, for each railway line section for which the schedule is created, the human flow measurement data at each station received from the flow measurement device 700, the calendar data received from the external information reception device 600, and the weather prediction. It has a function of correlating external information data such as data and event holding data with each other, predicting the number of train passengers at each station in the target line section, and storing the result in the human flow prediction database 300.
ここで、人流計測装置700は、各駅の構内に設置されたカメラ、センサ等の人流モニタ装置から人流計測データを算出する機能を有し、通信機能を備えるコンピュータとして構成される。人流計測は公知の手法によって実施することができ、例えば特開2013-116677号公報に関連技術が記載されている。
Here, the human flow measuring device 700 is configured as a computer having a function of calculating human flow measurement data from a human flow monitoring device such as a camera and a sensor installed in each station, and having a communication function. Human flow measurement can be carried out by a known method, and for example, related technology is described in Japanese Patent Application Laid-Open No. 2013-116677.
外部情報受信装置600は、本システム1が設置される鉄道会社等に設けられている情報サーバ等から、当日及び将来の天候やイベント等といった、各駅における人流変動の要因となるような外部情報を取得し、人流計測・予測装置104に送信する装置であり、通信機能を有するコンピュータとして構成することができる。外部情報受信装置600は、外部情報受信部601と、外部情報生データテーブル602とを備える。外部情報受信部601は、図外の情報サーバ等から外部情報を受信する。外部情報生データテーブル602は、外部情報受信部601から受信した生データを格納する。外部情報生データとは、情報サーバ等から取得したままなにも加工処理がされていないデータ群を意味し、この外部情報生データは、後述するように、人流計測・予測装置100において当該装置での処理に適したデータ形式に加工される。
The external information receiving device 600 receives external information that may cause fluctuations in the flow of people at each station, such as current and future weather and events, from an information server provided at a railway company or the like where the system 1 is installed. It is an apparatus which acquires and transmits to the human flow measurement / prediction apparatus 104, and can be configured as a computer having a communication function. The external information receiving device 600 includes an external information receiving unit 601 and an external information raw data table 602. The external information receiving unit 601 receives external information from an information server or the like not shown. The external information raw data table 602 stores the raw data received from the external information receiving unit 601. The external information raw data means a group of data that has been acquired from an information server or the like and has not been processed, and this external information raw data is used in the human flow measurement / prediction device 100 as described later. It is processed into a data format suitable for processing in
人流計測・予測装置100は、プロセッサ110、メモリ120、及びデータ入出力部130を有する。プロセッサ110は、CPU(Central Processing Unit)、MPU(MicroProcessing Unit)等の演算装置であり、後述する人流計測・予測装置100の機能を実現するプログラムを実行する。メモリ120は、ROM(Read Only Memory)、RAM(Random Access Memory)等の記憶デバイスを有し、後述する人流計測・予測装置100の機能を実現するプログラム、それらのプログラムが使用するデータを格納している。また、図示を省略するが、メモリ120には、前記プログラムの実行基盤となるシステムソフトウェア(オペレーティングシステム、OS)も実装されている。なお、人流計測・予測装置100は、メモリ120の他に、ハードディスクドライブ、半導体ドライブ等の補助記憶装置を備え、前記プログラム、データを、必要に応じて補助記憶装置からメモリ120に読み出すようにしてもよい。データ入出力部130は、通信ネットワークを介して他の装置との間でのデータ送受信処理を実行するインターフェース機能と、当該データの入出力処理機能とを有する。
The human flow measurement / prediction device 100 includes a processor 110, a memory 120, and a data input / output unit 130. The processor 110 is an arithmetic device such as a CPU (Central Processing Unit) or an MPU (MicroProcessing Unit), and executes a program that realizes functions of the human flow measurement / prediction device 100 described later. The memory 120 has storage devices such as a ROM (Read Only Memory) and a RAM (Random Access Memory), and stores programs for realizing the functions of the human flow measurement / prediction device 100 described later, and data used by these programs. ing. Although not shown, the memory 120 is also mounted with system software (operating system, OS) that is the execution base of the program. The human flow measurement / prediction device 100 includes an auxiliary storage device such as a hard disk drive and a semiconductor drive in addition to the memory 120, and reads the program and data from the auxiliary storage device to the memory 120 as necessary. Also good. The data input / output unit 130 has an interface function for executing data transmission / reception processing with another device via a communication network, and an input / output processing function for the data.
人流計測・予測装置100には、人流処理部121、外部情報処理部122、変化量計算部123、及び人流計算部124の各プログラムが実装されている。人流処理部121は、人流計測装置700から受信する人流計測結果を、人流計測・予測装置100での処理に適したデータ形式へデータ変換する機能を有する。外部情報処理部122は、外部情報受信装置600から受信する外部情報生データを、人流計測・予測装置100での処理に適したデータ形式へデータ変換する機能を有する。変化量計算部123は、人流計測・予測装置100の処理対象となる線区の各駅間の乗車人数の変化量を算出する機能を有する。人流計算部124は、人流処理部121、外部情報処理部122で処理されたデータに基づいて、処理対象の線区における将来の人流変動を予測する機能を有する。これらのプログラムによる具体的な処理の内容は、処理フロー例を用いて後述する。
In the human flow measurement / prediction apparatus 100, programs of a human flow processing unit 121, an external information processing unit 122, a change amount calculation unit 123, and a human flow calculation unit 124 are installed. The human flow processing unit 121 has a function of converting a human flow measurement result received from the human flow measurement device 700 into a data format suitable for processing in the human flow measurement / prediction device 100. The external information processing unit 122 has a function of converting raw external information data received from the external information receiving device 600 into a data format suitable for processing in the human flow measurement / prediction device 100. The change amount calculation unit 123 has a function of calculating the change amount of the number of passengers between the stations in the line area that is the processing target of the human flow measurement / prediction apparatus 100. The human flow calculation unit 124 has a function of predicting future human flow fluctuations in the line segment to be processed based on the data processed by the human flow processing unit 121 and the external information processing unit 122. Details of the processing by these programs will be described later using a processing flow example.
人流計測・予測装置100のメモリ120には、その機能を実現する前記プログラムが使用するデータを格納している人流計測・外部要因結果テーブル125(人流計測外部要因結果情報)と、外部要因予測テーブル126(外部要因予測情報)とが保持されている。人流計測・外部要因結果テーブル125は、互いに関連付けられた人流計測結果と、外部情報とから抽出した人流変動の要因となる外部要因とを記憶している。外部要因予測テーブル126は、将来の外部要因予測値を記憶している。人流計測・外部要因結果テーブル125及び外部要因予測テーブル126の具体的な構成例は、関連プログラムの処理フロー例とともに後述する。
In the memory 120 of the human flow measurement / prediction apparatus 100, a human flow measurement / external factor result table 125 (human flow measurement external factor result information) storing data used by the program for realizing the function, and an external factor prediction table 126 (external factor prediction information) is held. The human flow measurement / external factor result table 125 stores the human flow measurement results associated with each other and the external factors that cause the fluctuation of the human flow extracted from the external information. The external factor prediction table 126 stores future external factor prediction values. Specific configuration examples of the human flow measurement / external factor result table 125 and the external factor prediction table 126 will be described later together with a processing flow example of a related program.
ダイヤ作成装置200は、人流計測・予測装置100と同様に、プロセッサ210、メモリ220、及びデータ入出力部240を備えた通信機能を有するコンピュータとして構成され、人流計測・予測装置100が算出する人流予測から、需要に応じた将来の列車本数を算出し、列車計画、車両運用計画、及び乗務員運用計画の支援を実現する機能を有する。なお、列車ダイヤ作成装置200は、メモリ220の他に、ハードディスクドライブ、半導体ドライブ等の補助記憶装置を備えていてもよい。
Similar to the human flow measurement / prediction device 100, the diagram creation device 200 is configured as a computer having a communication function including a processor 210, a memory 220, and a data input / output unit 240, and is calculated by the human flow measurement / prediction device 100. It has the function of calculating the number of future trains according to the demand from the forecast and realizing support for the train plan, vehicle operation plan, and crew member operation plan. The train schedule creation device 200 may include an auxiliary storage device such as a hard disk drive or a semiconductor drive in addition to the memory 220.
メモリ220には、ダイヤ作成装置200の機能を実現するためのプログラムが格納されている。列車本数計算部221は、人流予測結果に応じて対象線区に走行させる列車の本数を算出する機能を有する。ダイヤ作成計算部222は、将来の列車計画、車両運用計画、及び乗務員運用計画を実施させるため、これらの計画部門に対して需要予測に対応して設定した列車本数等のデータを提供する機能を有する。図示を省略するが、メモリ220には、前記プログラムの実行基盤となるシステムソフトウェア(オペレーティングシステム、OS)も実装されている。
The memory 220 stores a program for realizing the function of the diagram creation device 200. The number-of-trains calculation unit 221 has a function of calculating the number of trains that run on the target line according to the predicted human flow. The diagram creation calculation unit 222 has a function of providing data such as the number of trains set corresponding to the demand forecast to these planning departments in order to execute a future train plan, a vehicle operation plan, and a crew member operation plan. Have. Although not shown in the figure, the memory 220 is also mounted with system software (operating system, OS) as an execution base of the program.
メモリ220には、前記プログラム実行時に使用する各種データを記録している、乗車人数対列車本数テーブル223(乗車人数対列車本数情報)、列車本数予測テーブル224(列車本数予測情報)、列車本数許容範囲テーブル225(全体及び駅間列車本数許容範囲情報)、基本本数テーブル226(基本本数情報)、及び代替路線テーブル227(代替路線情報)が格納されている。乗車人数対列車本数テーブル223は、対象線区の各駅における乗車人数に応じた列車本数を算出するための情報を格納している。列車本数予測テーブル224は、各駅での人流に応じた適切な将来の列車本数を格納している。基本本数テーブル226は、各線区及び各駅間で走行可能な列車本数の範囲を格納している。基本本数テーブル226は、各曜日、各時間帯における当該路線の基本の列車走行本数を格納している。代替路線テーブル227は、対象線区の各区間において、人流予測に応じた数の列車が設定できない場合に代替可能な他の路線のデータを格納している。これらのテーブルの構成例は、それらを使用するプログラムの処理フロー例に関連して後述する。
In the memory 220, various data used when the program is executed are recorded. Number of passengers vs. train number table 223 (number of passengers vs. number of trains information), number of trains prediction table 224 (train number prediction information), number of trains allowed A range table 225 (overall and inter-station train number allowable range information), a basic number table 226 (basic number information), and an alternative route table 227 (alternative route information) are stored. The number of passengers vs. number of trains table 223 stores information for calculating the number of trains according to the number of passengers at each station in the target line area. The train number prediction table 224 stores an appropriate number of future trains according to the flow of people at each station. The basic number table 226 stores a range of the number of trains that can travel between each line section and each station. The basic number table 226 stores the basic number of trains traveling on the route for each day of the week and each time zone. The alternative route table 227 stores data of other routes that can be substituted when the number of trains corresponding to the human flow prediction cannot be set in each section of the target line section. Configuration examples of these tables will be described later with reference to a processing flow example of a program that uses them.
また、メモリ220には、列車ダイヤ作成装置200が需要予測から設定した対象線区の列車本数に基づいて作成される将来の列車計画、車両運用計画、乗務員計画をそれぞれ格納する列車計画テーブル228、車両運用計画テーブル229、及び乗務員運用計画テーブル230が格納されている。これらのテーブルの内容は、例えば、列車ダイヤ作成装置200が対象線区について算出した需要予測に基づく列車本数を具体的に実現するために、各計画部門において作成された結果データである。
In addition, a train plan table 228 that stores a future train plan, a vehicle operation plan, and a crew plan created in the memory 220 based on the number of trains in the target line section set by the train schedule creation device 200 from the demand prediction, A vehicle operation plan table 229 and a crew member operation plan table 230 are stored. The contents of these tables are, for example, result data created in each planning department in order to specifically realize the number of trains based on the demand prediction calculated by the train diagram creation device 200 for the target line section.
入出力装置400は、人流計測・予測装置100、及びダイヤ作成装置200に対してデータ入力、操作命令入力を与えるためのキーボード、マウス、タッチパネル等の入力デバイスと、ダイヤ作成装置200で算出された列車本数等の情報及びその結果作成されたダイヤを表示するための液晶ディスプレイ、LEDパネル、プリンタ等の出力デバイスを含む。
The input / output device 400 is calculated by the input device such as a keyboard, a mouse, and a touch panel for giving data input and operation command input to the human flow measurement / prediction device 100 and the diagram creation device 200 and the diagram creation device 200. It includes output devices such as liquid crystal displays, LED panels, and printers for displaying information such as the number of trains and the resulting diagram.
人流予測データベース300は、人流計測・予測装置100とダイヤ作成装置200とで共通に使用されるデータベースであり、通信機能を有するコンピュータとして構成することができる。人流予測データベース300には、人流計測・予測装置100によって生成された将来の人流予測値が記憶される。人流予測データベース300は、人流計測・予測装置100とダイヤ作成装置200とで共通に使用されることから、両装置に対して正確かつ迅速な情報の伝達が可能となる。なお、人流予測データベース300は、図1に示すように独立したハードウェアとして設ける他、人流計測・予測装置100等の本システム1のいずれかの構成要素と一体に設けるようにすることもできる。
The human flow prediction database 300 is a database used in common by the human flow measurement / prediction device 100 and the diagram creation device 200, and can be configured as a computer having a communication function. The human flow prediction database 300 stores future human flow prediction values generated by the human flow measurement / prediction device 100. Since the human flow prediction database 300 is used in common by the human flow measurement / prediction device 100 and the diagram creation device 200, accurate and prompt information transmission to both devices is possible. The human flow prediction database 300 may be provided as independent hardware as shown in FIG. 1 or may be provided integrally with any component of the system 1 such as the human flow measurement / prediction device 100.
関連装置500は、ダイヤ作成装置200で算出された計画ダイヤを具体的に実現するための現業部署、例えば車両基地、乗務員基地等に設置される端末装置などである。本実施形態のダイヤ作成支援システム1では、以上のような構成により、人流予測によるダイヤ作成支援処理を実現する。これにより需要に即した将来のダイヤを機動的に作成することが可能となる。なお、関連装置500としては、本システム1の支援機能によって作成されたダイヤにより実際に列車を運行制御する運行管理システム等を想定することもできる。
The related device 500 is a terminal device installed in an in-service department, for example, a vehicle base, a crew base, etc., for specifically realizing the plan diagram calculated by the diagram creation device 200. In the diagram creation support system 1 of the present embodiment, the diagram creation support processing based on human flow prediction is realized by the configuration as described above. This makes it possible to flexibly create future diagrams that meet demand. In addition, as the related apparatus 500, the operation management system etc. which actually carry out operation control of the train by the diamond created by the support function of this system 1 can also be assumed.
次に、前記の構成を有する本実施形態のダイヤ作成支援システム1全体によって実行される処理について説明する。図2は、本実施形態のダイヤ作成支援システム1全体の処理例を示すフローチャートである。システム1は、あらかじめ人流計測・予測装置100等に設定された処理実行スケジュールに従って、あるいは入出力装置400からの処理開始入力により、全体処理を開始する(S10)。その後、まず人流計測・予測装置100は、人流計測装置700から当日の人流計測結果を受信し、また同時に外部情報装置600から人流変動の要因となる外部情報を受信する(S11)。次に、人流計測・予測装置100は、受信した人流計測結果と外部情報の生データとからシステム1で使用する情報を抽出し、メモリ120に格納できるデータ形式へのデータ変換処理(以下、「正規化」と表記する)を実施する(S12)。次に、人流計測・予測装置100は、正規化したデータから、本システム1による管理対象である各線区、各駅における乗車人数変化量を算出し、メモリ120に格納する(S13)。
Next, processing executed by the entire diagram creation support system 1 of the present embodiment having the above-described configuration will be described. FIG. 2 is a flowchart showing a processing example of the entire diagram creation support system 1 of the present embodiment. The system 1 starts the entire process according to a process execution schedule set in the human flow measurement / prediction apparatus 100 or the like in advance or by a process start input from the input / output device 400 (S10). Thereafter, the human flow measurement / prediction apparatus 100 first receives the current flow measurement result from the human flow measurement apparatus 700 and simultaneously receives external information from the external information apparatus 600 that causes fluctuations in the human flow (S11). Next, the human flow measurement / prediction apparatus 100 extracts information to be used in the system 1 from the received human flow measurement result and the raw data of the external information, and converts the data into a data format that can be stored in the memory 120 (hereinafter, “ (Referred to as “normalization”) (S12). Next, the human flow measurement / prediction device 100 calculates the amount of change in the number of passengers in each line section and each station, which is the management target of the present system 1, from the normalized data, and stores it in the memory 120 (S13).
次いで、人流計測・予測装置100は、将来の外部情報を正規化した際に外部要因予測に変更があったと判定した場合、外部要因予測と格納された乗車人数変化量とから外部要因予測が変更された日時において人流予測処理を実行する(S14)。算出された人流予測結果データは、人流予測データベース300に格納され、ダイヤ作成装置200と共有される。ダイヤ作成装置200は、人流予測データベース300に格納されている人流予測結果データに基づいてそれに対応することができる列車本数を算出して入出力装置400のディスプレイ等に出力する(S15)。算出された列車本数に基づいて、列車計画、車両運用計画、乗務員運用計画の各部署で、列車ダイヤを作成し、また車両運用計画、乗務員運用計画を策定して将来のダイヤを編成する(S16)。計画された将来のダイヤは、ダイヤ作成装置200により関連装置500に伝達され、本処理を終了する(S17、S18)。本実施形態によれば、外部要因の変化による人流変動に基づく人流予測から適切な列車本数を算出してダイヤを作成するため、輸送需要の変動に即した輸送力を提供しうるダイヤを計画することができる。
Next, when the human flow measurement / prediction apparatus 100 determines that the external factor prediction has changed when normalizing the external information in the future, the external factor prediction is changed from the external factor prediction and the stored passenger amount change amount. The human flow prediction process is executed at the date and time (S14). The calculated human flow prediction result data is stored in the human flow prediction database 300 and shared with the diagram creation device 200. The diagram creating apparatus 200 calculates the number of trains that can be handled based on the human flow prediction result data stored in the human flow prediction database 300, and outputs it to the display of the input / output device 400 (S15). Based on the calculated number of trains, a train schedule is created in each section of the train plan, vehicle operation plan, and crew operation plan, and a future schedule is formed by formulating the vehicle operation plan and crew operation plan (S16). ). The planned future diagram is transmitted to the related device 500 by the diagram creation device 200, and this process is terminated (S17, S18). According to the present embodiment, in order to create a diagram by calculating an appropriate number of trains based on the prediction of the flow of people based on changes in the flow of people due to changes in external factors, a diagram that can provide a transportation capacity in accordance with a change in transportation demand is planned. be able to.
次に、図2の全体処理の中で実行される個々の処理ステップについて、処理フロー例によって説明する。まず、図2のS12における人流計測結果の正規化処理について説明する。図3は、人流計測結果の正規化処理例を示すフローチャートである。人流計測・予測装置100の人流処理部121は、処理を開始すると(S100)、人流計測装置700から当日の人流計測結果を受信し、まず1時間ごとの人流データにデータ変換する(S101)。次に、人流処理部121は、管理対象線区ごとの人流計測データにデータ変換し(S102)、列車が各駅に到着する前の乗車人数及び出発後の乗車人数を算出し(S103)、人流計測・外部要因結果テーブル125にデータ変換(正規化)されたデータとして格納して処理を終了する(S104、S105)。
Next, each processing step executed in the overall processing of FIG. 2 will be described with a processing flow example. First, the normalization process of the human flow measurement result in S12 of FIG. 2 will be described. FIG. 3 is a flowchart illustrating an example of a normalization process of human flow measurement results. When the processing starts (S100), the human flow processing unit 121 of the human flow measurement / prediction device 100 receives the current flow measurement result from the flow measurement device 700 and first converts the data into hourly flow data (S101). Next, the human flow processing unit 121 converts the data into human flow measurement data for each management target line section (S102), calculates the number of passengers before the train arrives at each station, and the number of passengers after departure (S103). The data is stored as data converted (normalized) in the measurement / external factor result table 125, and the process is terminated (S104, S105).
図13A、13Bに、正規化された人流計測値と外部要因とを関連付けて格納する人流計測・外部要因結果テーブル125の構成例を、二分割して示している。人流計測・外部要因結果テーブル125には、当日の人流計測結果及び外部要因結果が日々格納され、図13Aに示すように、人流計測結果に関しては、対応するレコードの日付、開始時刻、終了時刻、線区、上り/下り区分、駅、乗車人数(到着前)、乗車人数(出発後)、及び変化量の各項目が記録される。すなわち、人流計測・外部要因結果テーブル125には、当日から遡る所定期間について、日々の人流計測結果及び外部要因結果が格納されている。
13A and 13B show a configuration example of the human flow measurement / external factor result table 125 that stores normalized human flow measurement values and external factors in association with each other. The human flow measurement / external factor result table 125 stores the daily flow measurement result and the external factor result of the day. As shown in FIG. 13A, the human flow measurement result includes the date, start time, end time, The following items are recorded: line area, up / down classification, station, number of passengers (before arrival), number of passengers (after departure), and change amount. In other words, the human flow measurement / external factor result table 125 stores daily human flow measurement results and external factor results for a predetermined period retroactive from the current day.
開始時刻及び終了時刻は、対応する人流計測結果の計測時間帯を、24時間表記で示している。線区には、例えば「AA線」といった路線の名称が格納される。上り/下り区分には、人流計測対象列車について当該線区での運行方向が格納される。駅には、人流計測対象の駅名が格納される。乗車人数(到着前)、乗車人数(出発後)には、当該の駅に到着する際、出発した際の乗客の人数が格納される。変化量には、後述する乗車人数変化量算出処理(図5)において算出される乗車人数(出発後)から乗車人数(到着前)の値を減算して得た値を格納する。各駅での乗降人数は、人流計測装置700に関して述べたように、各駅に設置したカメラ、人感センサ等の計測デバイスによって各ホームに発着する列車の乗降状況を記録し、それに基づいて降車人数、乗車人数を抽出して求めることができる。変化量については、例えば「+500」と記録されていた場合、該当駅での該当時間帯において、乗車人数が降車人数を500人上回ったことを示している。以上の人流計測結果の正規化処理により、当日の人流計測結果による各駅の乗降人数が、時間帯ごとに把握できることになる。
The start time and end time indicate the measurement time zone of the corresponding human flow measurement result in 24-hour notation. For example, the name of the route such as “AA line” is stored in the line section. In the up / down section, the operation direction in the line section is stored for the train subject to human flow measurement. In the station, the name of the station subject to human flow measurement is stored. The number of passengers (before arrival) and the number of passengers (after departure) store the number of passengers at the time of departure when arriving at the station. In the amount of change, a value obtained by subtracting the value of the number of passengers (before departure) from the number of passengers (after departure) calculated in the passenger number variation calculation process (FIG. 5) described later is stored. The number of people getting on and off at each station, as described with respect to the human flow measuring device 700, records the state of getting on and off of the trains coming and going to each home by means of measuring devices such as cameras and human sensors installed at each station, The number of passengers can be extracted and determined. As for the amount of change, for example, when “+500” is recorded, it indicates that the number of passengers exceeds the number of people getting off in the corresponding time zone at the corresponding station. By the above normalization processing of the human flow measurement result, the number of people getting on and off each station based on the current flow measurement result on the day can be grasped for each time zone.
次に、外部情報の正規化処理について説明する。図4は、外部情報の正規化処理例を示すフローチャートである。人流計測・予測装置100の外部情報処理部122は、S200で処理を開始すると、外部情報受信装置600の外部情報生データテーブル602から外部情報を受信し、まず日付および1時間ごとの外部情報にデータ変換する(S201)。ここで、外部情報としては、例えば沿線の天気、気温、風速、降水量、他の線区、他社線やバス路線等の運行情報、沿線のイベント情報、カレンダー情報等の生データが含まれる。なお、生データとは、外部情報の受信元である鉄道会社の情報サーバなどに格納されているデータを未加工のまま扱っていることを示し、そのデータ形式は受信元のシステム構成等に応じて種々の形式をとりうる。次に、外部情報処理部122は、外部情報を、人流を左右する外部要因として抽出し、データ変換(正規化)する(S202)。正規化された外部要因情報は、当日の情報であるか判定され(S203)、当日の情報であると判定された場合(S203:当日)、人流計測・外部要因結果テーブル125に登録されて処理を終了する(S204、S208)。
Next, the external information normalization process will be described. FIG. 4 is a flowchart illustrating an example of normalization processing of external information. When the external information processing unit 122 of the human flow measurement / prediction device 100 starts processing in S200, it receives external information from the external information raw data table 602 of the external information receiving device 600, and first converts the external information into date and hourly external information. Data conversion is performed (S201). Here, the external information includes raw data such as weather along the railway, temperature, wind speed, precipitation, operation information on other railway lines, other companies' lines, bus routes, etc., event information along the railway, and calendar information. Raw data means that the data stored in the information server of the railroad company that is the recipient of external information is handled unprocessed, and the data format depends on the system configuration of the recipient, etc. Can take various forms. Next, the external information processing unit 122 extracts external information as an external factor that influences the flow of people and performs data conversion (normalization) (S202). It is determined whether the normalized external factor information is information of the current day (S203). If it is determined that the information is current day information (S203: current day), it is registered in the human flow measurement / external factor result table 125 and processed. Is finished (S204, S208).
正規化された外部要因情報が将来の(翌日以降の)予測情報であり、かつ過去に格納された当該日時の外部要因予測値と比較して変更があると判定した場合(S205:Yes)、外部情報処理部122は、外部要因予測テーブル126に正規化されたデータを格納する(S206)。次いで、外部情報処理部122は、過去に格納された当該日時の外部要因予測値と変更があったものはその変更情報を人流計算部124に送信して処理を終了する(S207、S208)。S205で外部要因予測に変更がないと判定した場合、外部情報処理部122はそのまま処理を終了する(S208)。以上の処理により、鉄道会社の情報サーバ等から取得した外部情報から人流変動に関する要因を抽出し、人流計測・予測装置100にて処理するのに適した形式で利用することができるようになる。
When it is determined that the normalized external factor information is prediction information in the future (after the next day) and there is a change compared to the external factor prediction value of the date and time stored in the past (S205: Yes), The external information processing unit 122 stores the normalized data in the external factor prediction table 126 (S206). Next, the external information processing unit 122 transmits the change information to the human flow calculation unit 124 if the external factor predicted value of the date and time stored in the past is changed, and ends the processing (S207, S208). When it is determined in S205 that there is no change in the external factor prediction, the external information processing unit 122 ends the process as it is (S208). Through the above processing, factors related to fluctuations in human flow can be extracted from external information acquired from an information server of a railway company and used in a format suitable for processing by the human flow measurement / prediction apparatus 100.
図13Bに、人流計測・外部要因結果テーブル125の構成例を示している。図13Bの例では、該当する日付に関連付けて、曜日、祝日(例えば祝日に該当する場合はその記号を記録)、天気、気温、風速、降水量、運行情報、イベントといった項目が、受信した外部情報から抽出されて記録されている。ここで、運行情報については、対象となる会社、線区、事象(工事等)といった項目が、他線区あるいは他社の運行が通常時とは異なる場合に格納される。イベントについては、当該線区の沿線でイベントが開催される場合、開催有無、会場、集客人数といった項目が格納される。
FIG. 13B shows a configuration example of the human flow measurement / external factor result table 125. In the example of FIG. 13B, items such as a day of the week, a holiday (for example, record the symbol if it falls on a holiday), weather, temperature, wind speed, precipitation amount, operation information, and event are associated with the corresponding date. It is extracted from the information and recorded. Here, as for operation information, items such as a target company, line section, and event (construction, etc.) are stored when the operation of other line sections or other companies is different from the normal operation. As for the event, when the event is held along the line, items such as the presence / absence of the event, the venue, and the number of customers are stored.
図14は、将来の外部要因予測値を記憶する外部要因予測テーブル126の構成例を示している。図14の例では、翌日から1週間後までについて、外部情報処理部122で正規化された、人流に影響を与える将来の外部要因情報が格納されている。いったん登録後、その要因の発生時前に外部要因に変更があった場合は、外部情報処理部122が該当するデータを変更する(図4のS205:Yes、S206)。なお、外部要因予測テーブル126に記録される項目は、図13A、13Bの人流計測・外部要因結果テーブル125と同様のため説明を省略する。
FIG. 14 shows a configuration example of the external factor prediction table 126 that stores future external factor prediction values. In the example of FIG. 14, future external factor information that affects the human flow, normalized by the external information processing unit 122, is stored from the next day to one week later. Once the external factor is changed after the registration, the external information processing unit 122 changes the corresponding data (S205: Yes, S206 in FIG. 4). The items recorded in the external factor prediction table 126 are the same as those in the human flow measurement / external factor result table 125 in FIGS.
次に、図2の全体処理フロー例における乗車人数変化算出処理(S13)について説明する。図5は、本システム1の管理対象である各線区、各駅における乗車人数変化量算出処理例を示すフローチャートである。人流計測・予測装置100の変化量計算部123はS300で処理を開始すると、図13Aの人流計測・外部要因結果テーブル125に格納された人流計測結果から当日の情報を取得し(S301)、発車後の乗車人数の値から到着前の乗車人数の値を減算することで、各線区、各駅における乗車人数の変化量を算出する(S302)。次に、変化量計算部123は、算出された変化量によって人流計測・外部要因結果テーブル125を更新して処理を終了する(S303、S304)。以上の処理によって、各線区、各駅における乗降客数が人流計測結果に基づいて日ごとに更新される。
Next, the passenger number change calculation process (S13) in the overall process flow example of FIG. 2 will be described. FIG. 5 is a flowchart illustrating an example of a passenger number variation calculation process in each line section and each station that is the management target of the system 1. When the change amount calculation unit 123 of the human flow measurement / prediction apparatus 100 starts the process in S300, it acquires information on the current day from the human flow measurement result stored in the human flow measurement / external factor result table 125 in FIG. 13A (S301), By subtracting the value of the number of passengers before arrival from the value of the subsequent number of passengers, the amount of change in the number of passengers in each line section and each station is calculated (S302). Next, the change amount calculation unit 123 updates the human flow measurement / external factor result table 125 with the calculated change amount, and ends the processing (S303, S304). Through the above processing, the number of passengers in each line section and each station is updated on a daily basis based on the result of the measurement of people flow.
次に、図2の全体処理フロー例における人流予測値算出処理(S14)について説明する。図6は、人流予測値算出処理例を示すフローチャートである。人流計測・予測装置100の人流計算部124はS400で処理を開始すると、外部情報処理部122から送信された変更情報を受信し(S401)、その変更情報に基づいて、外部要因予測テーブル126から変更のあった日時の外部要因予測項目を取得する(S402)。なお、本処理フローの開始契機は、所定時刻ごとに定めることができ、また変更情報の受信を契機として処理を開始するようにしてもよい。次に、人流計算部124は、取得した当該日時の外部要因予測と同じ外部要因条件の人流計測・外部要因結果を、人流計測・外部要因結果テーブル125から全て取得する(S403)。すなわち、外部要因予測テーブル126において、変更情報と一致する曜日、祝日、天気、気温、風速、降水量、運行情報、又はイベントが記録されているレコードがすべて取得される。次いで、人流計算部124は、取得された人流計測・外部要因結果から、ダイヤを作成する対象線区の人流計測・外部要因結果のみを抽出する(S404)。さらに、人流計算部124は、該当の線区に含まれる一の駅を指定し、その駅の人流計測・外部要因結果を抽出し(S405)、抽出された人流計測・外部要因結果から、当該駅について記録されている乗車人数変化量の平均値を算出する(S406)。これは、ある外部要因が変更された場合、該当する外部要因に対応する変化量の実績について平均化し、より信頼性の高い予測結果を得るためである。
Next, the human flow predicted value calculation process (S14) in the overall process flow example of FIG. 2 will be described. FIG. 6 is a flowchart illustrating an example of a human flow predicted value calculation process. When the flow calculation unit 124 of the flow measurement / prediction apparatus 100 starts processing in S400, it receives the change information transmitted from the external information processing unit 122 (S401), and from the external factor prediction table 126 based on the change information. The external factor prediction item of the date and time when the change is made is acquired (S402). The start timing of this processing flow can be determined every predetermined time, and the processing may be started upon reception of change information. Next, the human flow calculation unit 124 acquires all of the human flow measurement / external factor results of the same external factor condition as the acquired external factor prediction of the acquired date / time from the human flow measurement / external factor result table 125 (S403). That is, in the external factor prediction table 126, all records that record the day of the week, holidays, weather, temperature, wind speed, precipitation, operation information, or events that match the change information are acquired. Next, the human flow calculation unit 124 extracts only the human flow measurement / external factor result of the target line section for creating a diagram from the acquired human flow measurement / external factor result (S404). Further, the human flow calculation unit 124 designates one station included in the corresponding line section, extracts the human flow measurement / external factor result of the station (S405), and extracts the current flow measurement / external factor result from the extracted human flow measurement / external factor result. The average value of the change in the number of passengers recorded for the station is calculated (S406). This is because when a certain external factor is changed, the results of changes corresponding to the relevant external factor are averaged to obtain a more reliable prediction result.
人流計算部124は、S407で全駅の変化量平均値を算出したか判定し(S407)、全駅について算出されたと判定するまで(S407:Yes)、S405及びS406の処理を繰り返し、当該線区の始発駅から終着駅までの各駅の乗車人数の変化量の平均値を算出する。次いで、人流計算部124は、全ての駅において算出が完了した場合、算出された平均値を始発駅から順に加算することで始発駅から終着駅までの駅間の乗車人数を算出し(S408)、駅間乗車人数算出結果を人流予測データベース300に格納する(S409)。人流計算部124は、外部要因の変更情報をダイヤ作成装置200に送信して処理を終了する(S410、S411)。図15は、将来の外部要因予測テーブル126と過去の人流計測・外部要因結果テーブル125の記録から算出した、人流予測の結果を格納する人流予測データベース300の構成例である。図15の例では、日付ごとに、時間帯、線区、上り/下り区分に関連付けて、駅間別乗車人数が該当線区の始発駅から終着駅までそれぞれの駅間について記録される。例えば、図15の例では、「2013年10月2日の8時~9時の時間帯において、AA線区の上りでは、始発駅から第2駅(次駅)の区間で1300人が乗車する」ことが記録されている。この人流予測データベース300はダイヤ作成装置200によっても共有されるため、本システム1内における正確かつ迅速な情報の伝達が可能となる。
The flow calculation unit 124 determines whether or not the average amount of change for all stations has been calculated in S407 (S407), and repeats the processing of S405 and S406 until it is determined that all stations have been calculated (S407: Yes). Calculate the average change in the number of passengers at each station from the first station to the last station in the ward. Next, when the calculation is completed at all stations, the person flow calculation unit 124 calculates the number of passengers between the stations from the first station to the last station by adding the calculated average values in order from the first station (S408). The inter-station passenger count calculation result is stored in the human flow prediction database 300 (S409). The human flow calculation unit 124 transmits the external factor change information to the diagram creating apparatus 200 and ends the process (S410, S411). FIG. 15 is a configuration example of the human flow prediction database 300 that stores the results of the human flow prediction calculated from the records of the future external factor prediction table 126 and the past human flow measurement / external factor result table 125. In the example of FIG. 15, for each date, the number of passengers by station is recorded for each station from the first station to the last station of the corresponding line section in association with the time zone, line section, and up / down section. For example, in the example of FIG. 15, “In the time zone from 8:00 to 9:00 on October 2, 2013, 1300 people get on the section from the first station to the second station (next station) on the AA line. "Yes" is recorded. Since this human flow prediction database 300 is also shared by the diagram creation apparatus 200, accurate and quick information transmission in the system 1 is possible.
以上の人流計算部124での処理により、過去の人流計測値と将来の外部要因予測から将来の人流を予測することが可能である。これにより、列車の運転本数を決定するのに必要な輸送需要を算出できる。なお、統計値の蓄積が大きくなるほど期待値の精度は向上するため、人流計測・外部要因結果テーブル125の記録が日々蓄積されることにより、徐々に乗車人数の変化量の平均値(期待値)の算出精度は向上することが期待される。また、外部要因予測も当日に近づくにつれ精度が向上するため、それをもとに算出する人流予測も当日に近づくにつれ精度が向上することが期待される。
Through the above-described processing by the human flow calculation unit 124, it is possible to predict a future human flow from past human flow measurement values and future external factor predictions. Thereby, the transportation demand required to determine the number of trains operated can be calculated. Since the accuracy of the expected value increases as the accumulation of statistical values increases, the average value (expected value) of the amount of change in the number of passengers is gradually increased by accumulating daily flow measurement / external factor result table 125 records. The calculation accuracy of is expected to improve. In addition, since the accuracy of external factor prediction improves as it approaches the day, it is expected that the accuracy of the human flow prediction calculated based on it will also improve as it approaches the day.
次に、以上の人流計測・予測装置100による処理に引き続いてダイヤ作成装置200で実行される処理について説明する。まず、ダイヤ作成装置200の列車本数計算部221によって実行される列車本数算出処理について説明する。図7は、列車本数算出処理例を示すフローチャートである。列車本数算出処理では、人流予測データベース300に格納されている人流予測結果に基づいて、その輸送需要に対応可能な列車本数が算出される。ダイヤ作成装置200の列車本数計算部221は、S500で処理を開始すると、人流計測・予測装置100の人流計算部124から送信された変更情報を受信し(S501)、人流予測データベース300から変更のあった日時の人流予測データを取得する(S502)。図15に示したように、人流予測データからは、駅間乗車人数を取得することが可能であるため、列車本数計算部221は、この駅間乗車人数から、需要に応じた各駅間の列車本数を算出する(S503)。
Next, processing executed by the diagram creation device 200 subsequent to the processing by the human flow measurement / prediction device 100 will be described. First, the train number calculation process executed by the train number calculation unit 221 of the diagram creation apparatus 200 will be described. FIG. 7 is a flowchart illustrating an example of a train number calculation process. In the number-of-trains calculation process, the number of trains that can correspond to the transportation demand is calculated based on the results of the prediction of the flow of people stored in the prediction database 300 of the flow of people. When the train number calculation unit 221 of the diagram creation apparatus 200 starts the process in S500, the train number calculation unit 221 receives the change information transmitted from the human flow calculation unit 124 of the human flow measurement / prediction apparatus 100 (S501), and receives the change information from the human flow prediction database 300. Human flow prediction data for a certain date and time is acquired (S502). As shown in FIG. 15, since it is possible to obtain the number of passengers between stations from the human flow prediction data, the train number calculation unit 221 determines the number of trains between stations according to demand from the number of passengers between stations. The number is calculated (S503).
次いで、列車本数計算部221は、算出された駅間の列車本数から、線区全体を走行する列車の本数を算出する(S504)。次に、列車本数計算部221は、算出された駅間の列車本数と線区全体の列車本数が適当であるか判断し、適当でなければ本数の微調整を実施し需要に対して最適な列車本数を算出する(S505)。次いで、列車本数計算部221は、算出された結果を入出力装置400のディスプレイ等の出力デバイスに出力する(S506)。また、列車本数計算部221は、列車本数に関する変更情報をダイヤ作成計算部222に送信して処理を終了する(S507、S508)。以上の処理により、外部要因の変化に応じて算出された列車本数に関する情報が列車計画を実施する担当者に提示される。なお、本発明の最小構成としては、図7のS503で算出される駅間別列車本数を提示するまでの処理を実行し、その後の具体的な列車設定条件との整合は別の構成で行うことも考えられる。
Next, the train number calculation unit 221 calculates the number of trains traveling in the entire line area from the calculated number of trains between stations (S504). Next, the train number calculation unit 221 determines whether the calculated number of trains between the stations and the number of trains in the entire line area are appropriate. The number of trains is calculated (S505). Next, the train number calculation unit 221 outputs the calculated result to an output device such as a display of the input / output device 400 (S506). In addition, the train number calculation unit 221 transmits change information regarding the number of trains to the diagram creation calculation unit 222 and ends the process (S507, S508). Through the above processing, information related to the number of trains calculated according to changes in external factors is presented to the person in charge implementing the train plan. As the minimum configuration of the present invention, the processing up to the presentation of the number of trains between stations calculated in S503 in FIG. 7 is executed, and the subsequent matching with the specific train setting conditions is performed in another configuration. It is also possible.
以下、図7の列車本数算出処理における主要な処理ステップについて順次説明する。まず、駅間別列車本数算出処理(図7のS503)について説明する。図8は、駅間別列車本数の算出処理例(S503)を示すフローチャートである。列車本数計算部221は、S600で処理を開始すると、まず人流予測データベース300から取得した駅間乗車人数を用いて、当該線区及び当該駅間の列車本数を、乗車人数対列車本数テーブル223から取得し(S601)、人流予測による乗車人数に対する適切な駅間列車本数を算出する(S602)。
Hereinafter, the main processing steps in the train number calculation process of FIG. 7 will be described sequentially. First, the train number calculation processing for each station (S503 in FIG. 7) will be described. FIG. 8 is a flowchart illustrating a calculation process example (S503) of the number of trains by station. When the processing starts in S600, the train number calculation unit 221 first uses the number of passengers between stations acquired from the human flow prediction database 300 to determine the number of trains between the line segment and the stations from the number of passengers vs. train number table 223. Obtain (S601), and calculate the appropriate number of inter-station trains for the number of passengers based on the prediction of human flow (S602).
図16に、乗車人数対列車本数テーブル223の構成例を示している。乗車人数対列車本数テーブル223は、特定線区の各駅間の乗車人数に対する適切な列車本数を、乗車定員当に基づいてあらかじめ設定したテーブルであり、線区、区間(駅間)に関連付けて、乗車人数下限値、乗車人数上限値、本数の各項目が記録されている。図16の例を参照すると、線区AAの区間A-Bにおいて、乗車人数が1000~1200人見込まれる場合、適切な列車本数は9本であることが示されている。この乗車人数対列車本数テーブル223により、需要に応じた列車本数を判断することができる。次いで、列車本数計算部221は、算出された駅間列車本数を列車本数予測テーブル224へ登録して処理を終了する(S603、S604)。
FIG. 16 shows a configuration example of the number of passengers vs. number of trains table 223. The number of passengers vs. number of trains table 223 is a table in which an appropriate number of trains for the number of passengers between stations in a specific line section is set in advance based on the boarding capacity, and is associated with the line section and section (between stations). Each item of passenger number lower limit value, passenger number upper limit value, and number is recorded. Referring to the example of FIG. 16, in the section AB of the line section AA, it is shown that the appropriate number of trains is 9 when the number of passengers is estimated to be 1000 to 1200. The number of trains according to demand can be determined from the number of passengers vs. number of trains table 223. Next, the train number calculation unit 221 registers the calculated number of trains between stations in the train number prediction table 224 and ends the process (S603, S604).
図17は、列車本数予測テーブル223の構成例である。列車本数予測テーブル223は、需要予測に応じた将来の列車本数を格納するためのテーブルであり、該当日付に対応させて、曜日、開始時刻、終了時刻、線区、上り/下り区分、列車本数、基本本数、及び差分の各項目が記録されている。本テーブル223に特有の項目について述べると、列車本数には、該当線区の始発駅から終着駅までの各駅間について、図8のS602で算出した走行させるべき列車の本数が格納される。基本本数には、後述する線区別列車本数算出処理のS707で取得される当該曜日、当該時刻に当該線区全体を走行する列車の基本的な本数が格納される。差分には、同じくS708で算出した当該線区全体を走行する列車の本数と基本本数との差分が格納される。なお、図8のS601~S603の処理は該当線区の駅間ごとに実施され、適切な列車本数の算出及び列車本数予測テーブル223への登録は、当該線区の全駅間について実施される。
FIG. 17 is a configuration example of the train number prediction table 223. The number-of-trains prediction table 223 is a table for storing the number of future trains according to the demand prediction. Corresponding to the corresponding date, day of the week, start time, end time, line section, up / down classification, number of trains , Basic number, and difference items are recorded. Describing items peculiar to the table 223, the number of trains to be run calculated in S602 of FIG. 8 is stored in the number of trains between each station from the start station to the end station of the corresponding line section. The basic number stores the basic number of trains that travel on the entire line section at the time of the day and time acquired in S707 of the line-differentiated train number calculation process described later. Similarly, the difference stores the difference between the number of trains traveling in the entire line section and the basic number calculated in S708. Note that the processing of S601 to S603 in FIG. 8 is performed for each station in the corresponding line section, and appropriate calculation of the number of trains and registration in the train number prediction table 223 are performed for all stations in the line section. .
次に、線区別列車本数算出処理について説明する。図9は、図7で示した線区別列車本数の算出処理例(S504)を示すフローチャートである。列車本数計算部221はS700で処理を開始すると、まず当該線区の駅間別の列車本数予測値を列車本数予測テーブル223から取得し(S701)、取得した当該線区の駅間別列車本数において、最小の列車本数を算出する(S702)。次に、列車本数計算部221は、列車本数許容範囲テーブル225から、線区全体及び各駅間における、走行可能な列車本数の許容範囲を取得し(S703)、事前に算出した駅間別列車本数の最小値が線区全体の列車本数の許容範囲内であるか確認する(S704)。駅間別列車本数の最小値が許容範囲内であると判定した場合(S704:Yes)、列車本数計算部221は、その最小値を線区全体の走行本数として設定する(S705)。S704で駅間別列車本数の最小値が許容範囲内でないと判定した場合(S704:No)、列車本数計算部221は、その最小値が許容範囲の下限値を下回った場合は下限値を、その最小値が許容範囲の上限値を上回った場合は上限値を、線区全体の走行本数として設定する(S706)。
Next, a process for calculating the number of trains for distinguishing lines will be described. FIG. 9 is a flowchart illustrating a calculation processing example (S504) of the number of line-differentiated trains illustrated in FIG. When the train number calculation unit 221 starts processing in S700, first, the train number prediction value for each station in the relevant line section is obtained from the train number prediction table 223 (S701), and the obtained train number for each station in the relevant line section is obtained. In step S702, the minimum number of trains is calculated. Next, the train number calculation unit 221 obtains the allowable range of the number of trains that can run in the entire line section and between the stations from the train number allowable range table 225 (S703), and calculates the number of trains for each station calculated in advance. It is confirmed whether the minimum value is within the allowable range of the number of trains in the entire line section (S704). If it is determined that the minimum value of the number of trains between stations is within the allowable range (S704: Yes), the train number calculation unit 221 sets the minimum value as the total number of traveling lines (S705). If it is determined in S704 that the minimum value of the number of trains between stations is not within the allowable range (S704: No), the train number calculation unit 221 sets the lower limit value when the minimum value falls below the lower limit value of the allowable range. When the minimum value exceeds the upper limit value of the allowable range, the upper limit value is set as the number of traveling lines for the entire line section (S706).
図18に、列車本数許容範囲テーブル225の構成例を示している。列車本数許容範囲テーブル225は本システム1の運用前にあらかじめ設定されているもので、各線区及び各駅間で1時間当りに走行可能な列車本数の下限値、上限値を格納しており、各線区について、全体本数下限値、全体本数上限値、及び該当線区の始発駅から終着駅までの各駅間における列車本数上限値、下限値の各項目を記録している。全体本数下限値及び全体本数上限値には、当該線区全体を走行可能な列車本数の下限値及び上限値が格納されている。各駅間の列車本数下限値及び上限値には、各駅間を走行可能な列車本数の下限値及び上限値が格納されている。このテーブルにより、駅間の列車本数と線区全体の列車本数が適当であるか判断することができる。
FIG. 18 shows a configuration example of the train number allowable range table 225. The train number allowable range table 225 is set in advance before the operation of the system 1, and stores the lower limit value and the upper limit value of the number of trains that can run per hour between each line section and each station. For the ward, the total number lower limit value, the total number upper limit value, and the train number upper limit value and the lower limit value between each station from the starting station to the end station of the corresponding line ward are recorded. The overall number lower limit value and the overall number upper limit value store the lower limit value and the upper limit value of the number of trains that can travel the entire line section. The lower limit value and the upper limit value of the number of trains between the stations store the lower limit value and the upper limit value of the number of trains that can travel between the stations. From this table, it is possible to determine whether the number of trains between stations and the number of trains in the entire line area are appropriate.
次に、列車本数計算部221は、基本本数テーブル226から該当の曜日及び日時の基本本数を取得し(S707)、基本本数と線区全体の走行本数の差分を算出し(S708)、列車本数予測テーブル223を更新して処理を終了する(S709、S710)。図19に、基本本数テーブル226の構成例を示している。基本本数テーブル226は、各曜日、各時間帯における当該線区の基本の列車走行本数を格納しており、本システム1の運用開始前に、各線区の基本的な需要予測に基づいて設定される。基本本数テーブル226は、曜日、開始時刻、終了時刻、線区、上り/下り区分、基本本数の各項目が、関連付けて記録されている。このテーブルにより、当該の曜日、当該の時間帯における特定線区の基本列車本数を確認することができる。
Next, the train number calculation unit 221 obtains the basic number of the day of the week and the date / time from the basic number table 226 (S707), calculates the difference between the basic number and the total number of traveling lines (S708), and the number of trains The prediction table 223 is updated, and the process ends (S709, S710). FIG. 19 shows a configuration example of the basic number table 226. The basic number table 226 stores the basic number of trains traveling in the relevant line section for each day of the week and each time zone, and is set based on the basic demand forecast of each line section before the operation of the system 1 is started. The In the basic number table 226, items of day of the week, start time, end time, line section, up / down division, and basic number are recorded in association with each other. With this table, it is possible to confirm the number of basic trains in the specific line section in the time zone and the day of the week.
次に、最適列車本数算出処理について説明する。図10は、図7で示した最適列車本数の算出処理例(S505)を示すフローチャートである。列車本数計算部221はS800で処理を開始すると、まず列車本数予測テーブル223に登録されている当該線区の駅間別列車本数を取得し(S801)、列車本数許容範囲テーブル225から当該線区の各駅間における走行可能な列車本数の許容範囲を取得し(S802)、列車本数予測テーブル223に記録されている駅間別列車本数と、列車本数許容範囲テーブル225に記録されている各駅間での列車本数許容範囲とを比較する(S803)。
Next, the optimal number of trains calculation process will be described. FIG. 10 is a flowchart showing an example of the optimum train number calculation process (S505) shown in FIG. When the train number calculation unit 221 starts the process in S800, the train number calculation unit 221 first acquires the number of trains for each station in the train line registered in the train number prediction table 223 (S801). The allowable range of the number of trains that can be traveled between the stations is acquired (S802), and the number of trains for each station recorded in the train number prediction table 223 and between the stations recorded in the train number tolerance table 225 The allowable number of trains is compared (S803).
駅間別列車本数が当該駅間の許容範囲の下限値を下回っていると判定した場合、列車本数計算部221は、その下限値を当該駅間本数の最適値として設定して処理を終了する(S804、S809)。駅間別列車本数が当該駅間の列車本数の許容範囲内であると判定した場合、列車本数計算部221は、列車本数予測テーブル223から取得した駅間列車本数を、当該駅間本数の最適値として設定して処理を終了する(S805、S809)。駅間別列車本数が当該駅間の列車本数の上限値を上回っていると判定した場合、列車本数計算部221は、その上限値を当該駅間本数の最適値として設定し(S806)、また列車本数予測テーブル223から取得した駅間別列車本数(人流予測に基づく需要から算出した列車本数)と上限値との差を理想値として設定する(S807)。
When it is determined that the number of trains between stations is lower than the lower limit value of the allowable range between the stations, the train number calculation unit 221 sets the lower limit value as the optimum value of the number of stations, and ends the process. (S804, S809). When it is determined that the number of trains between stations is within the allowable range of the number of trains between the stations, the train number calculation unit 221 uses the number of trains between stations acquired from the train number prediction table 223 as the optimum number of trains between the stations. The value is set as a value and the process is terminated (S805, S809). When it is determined that the number of trains between stations exceeds the upper limit value of the number of trains between the stations, the train number calculation unit 221 sets the upper limit value as the optimum value of the number of trains between the stations (S806). The difference between the number of trains between stations acquired from the train number prediction table 223 (the number of trains calculated from demand based on human flow prediction) and the upper limit value is set as an ideal value (S807).
次に、列車本数計算部221は、代替路線テーブル227を参照して、該当区間の代替路線候補があるか調べ、登録があればその候補を取得して処理を終了する(S808、S809)。なお、S803~S808の処理は対象線区の駅間ごとに実施され、それにより当該線区の全駅間の列車本数最適値が設定される。図20に、代替路線テーブル227の構成例を示している。代替路線テーブル227は、本システム1の管理対象線区について、各駅間に関する代替可能な路線データを格納しており、本システム1の運用開始前にあらかじめ設定される。代替路線テーブル227は、線区、駅間、及び代替会社・路線の各項目を関連付けて格納している。このテーブルにより、ある駅間で走行可能な列車本数以上の乗客数が見込まれる場合(人流予測に基づく必要列車本数が列車本数許容範囲上限値を上回っている場合)、振替輸送を依頼できる他の鉄道会社や路線があるか確認することができる。
Next, the train number calculation unit 221 refers to the alternative route table 227 and checks whether there is an alternative route candidate for the corresponding section. If there is a registration, the candidate is acquired and the processing is terminated (S808, S809). It should be noted that the processing of S803 to S808 is performed for each station in the target line section, thereby setting the optimum number of trains between all stations in the line section. FIG. 20 shows a configuration example of the alternative route table 227. The alternative route table 227 stores substitutable route data relating to each station for the management target line section of the system 1 and is set in advance before the operation of the system 1 is started. The alternative route table 227 stores the items of line sections, between stations, and alternative companies / routes in association with each other. With this table, if the number of passengers more than the number of trains that can run between certain stations is expected (if the required number of trains based on human flow prediction exceeds the upper limit of the allowable number of trains), you can request another transfer You can check if there is a railway company or route.
以上説明した本実施形態によるダイヤ作成支援システム1の処理によって得られる出力データについて説明する。図11は、ダイヤ作成装置200の列車本数算出処理によって得られる出力画面例である。この出力画面は、外部情報の変更にともなって、人流計測・予測装置100及びダイヤ作成装置200による列車本数算出処理が終了したときに表示することができる。図11に示すように、出力画面には、日付、曜日、線区、時間帯、上り/下り区分の各項目と、当該線区について設定された列車の全体本数、及び駅間本数が表示される。
The output data obtained by the processing of the diagram creation support system 1 according to this embodiment described above will be described. FIG. 11 is an example of an output screen obtained by the train number calculation process of the diagram creation device 200. This output screen can be displayed when the train number calculation processing by the human flow measurement / prediction device 100 and the diagram creation device 200 is completed in accordance with the change of the external information. As shown in FIG. 11, the date, day of the week, line section, time zone, up / down section, the total number of trains set for the line section, and the number of stations between stations are displayed on the output screen. The
駅間本数については、対応する駅間ごとに、列車本数の最適値、理想値、駅間許容範囲の上限超過の有無、及び代替路線候補が表示される。列車本数に関する表示項目のデータは、最適列車本数算出処理(図7のS505)が終了した時点までに算出されたデータを、ダイヤ作成装置200のメモリ220に格納しておき、適宜の出力フォーマットで表示出力等させるように構成することができる。なお、表示項目について、プルダウンメニュー等の入力手段を設けておき、ダイヤ作成担当者が閲覧したいデータを指定することができるように構成すれば、システム1としての使い勝手をより向上させることができる。例えば、日付、曜日、線区、時間帯、上り/下り区分について画面上で指定可能としておけば、所望の線区、時間帯等における設定列車本数を呼び出して確認することが可能となる。
For the number of stations, the optimum number of trains, the ideal value, whether there is an excess of the upper limit of the allowable range between stations, and alternative route candidates are displayed for each corresponding station. The display item data relating to the number of trains is stored in the memory 220 of the diagram creating apparatus 200 in a suitable output format, which is calculated up to the time when the optimum train number calculation process (S505 in FIG. 7) is completed. It can be configured to perform display output or the like. In addition, if the display item is provided with an input means such as a pull-down menu so that the person in charge of diagram creation can specify data to be viewed, the usability as the system 1 can be further improved. For example, if the date, day of the week, line section, time zone, and up / down classification can be specified on the screen, the set number of trains in the desired line section, time zone, etc. can be called and confirmed.
この出力画面により、ダイヤ作成担当者は、線区ごとに需要に応じた列車本数を確認することが可能となる。また、駅間ごとに最適な走行本数を表示するため、駅間単位に列車本数を微調整することが可能である。これにより、需要に応じた列車本数を適当な時刻に走行させるためのダイヤを計画することができる。さらに、いずれかの駅間で需要予測に見合った本数を走行させることができない場合、自社あるいは他社の代替路線候補を表示することができるため、必要に応じて自社の他線区の走行本数を追加する、代替路線を有する他社へ列車増発の依頼を行う等の対応により、混雑緩和を図ることが可能となる。これにより、将来の需要変動に対応した適切なダイヤを編成することが可能となり、乗客に対する利便性も向上する。
This output screen enables the person in charge of diagram creation to check the number of trains according to demand for each line section. In addition, since the optimum number of trains is displayed for each station, it is possible to finely adjust the number of trains for each station. Thereby, the schedule for making the number of trains according to demand run at an appropriate time can be planned. In addition, if it is not possible to drive the number that matches the demand forecast between any of the stations, you can display the alternative route candidates of your company or other companies, so if necessary, you can set the number of traveling in your other lines Congestion can be alleviated by adding additional trains to other companies that have alternative routes. As a result, it is possible to form an appropriate timetable corresponding to future demand fluctuations and to improve convenience for passengers.
図12に、本実施形態のダイヤ作成支援システム1を適用した列車計画、車両運用計画及び乗務員運用計画処理のフロー例を示している。ダイヤ作成装置200のダイヤ作成計算部222はS900で処理を開始すると、列車本数計算部221から送信された変更情報を受信し(S901)、変更が必要な当該日時及び当該線区の将来のダイヤを列車計画テーブル228、車両運用計画テーブル229、及び乗務員運用計画テーブル230から取得する(S902)。ダイヤ作成担当者は、図11に例示した出力画面を参照して必要な列車本数を確認し、列車計画の作成または変更を実施する(S903)。この際、必要であれば、他線区のダイヤ作成担当者に変更依頼または他社にダイヤ変更依頼を実施する。次に、ダイヤ作成担当者は、当該の列車に対して車両運用の計画(S904)、さらに乗務員運用の計画を策定する(S905)。
FIG. 12 shows a flow example of a train plan, vehicle operation plan, and crew member operation plan process to which the diagram creation support system 1 of the present embodiment is applied. When the diagram creation calculation unit 222 of the diagram creation device 200 starts the process in S900, the schedule creation calculation unit 222 receives the change information transmitted from the train number calculation unit 221 (S901). Are acquired from the train plan table 228, the vehicle operation plan table 229, and the crew member operation plan table 230 (S902). The person in charge of diamond creation confirms the necessary number of trains with reference to the output screen illustrated in FIG. 11, and creates or changes a train plan (S903). At this time, if necessary, a change request is made to the person in charge of the diagram creation in the other line section or a diamond change request is made to another company. Next, the person in charge of creating a diagram formulates a vehicle operation plan (S904) and a crew operation plan for the train (S905).
ダイヤ作成または変更が完了後、ダイヤ作成担当者は、列車計画テーブル228、車両運用計画テーブル229、乗務員運用計画テーブル230に当該変更ダイヤを登録して処理を終了する(S906、S907)。このように、本実施形態のダイヤ作成支援システム1を適用することにより、需要に即して将来のダイヤを機動的に作成、変更することが可能となる。これにより、例えば天候やイベント等で変化する輸送需要に応じた将来のダイヤを機動的に作成することが可能となる。また、鉄道会社は需要に即して列車を走行させることが可能になるため、電力や車両といったリソースを効率的に利用することができ、経営の安定化に資することができる。さらに、将来の需要予測により前もってダイヤ変更を計画することができるため、乗務員運用担当者、車両基地担当者は、ダイヤ変更に伴う運用変更を予め準備することが可能となり、ダイヤ変更による負担を軽減させることが可能となる。
After the schedule creation or change is completed, the person in charge of schedule creation registers the change schedule in the train plan table 228, the vehicle operation plan table 229, and the crew member operation plan table 230, and ends the processing (S906, S907). Thus, by applying the diagram creation support system 1 of the present embodiment, it becomes possible to create and change a future diagram flexibly according to demand. Thereby, for example, it becomes possible to flexibly create a future diagram corresponding to a transportation demand that changes due to, for example, weather or an event. In addition, since a railway company can run a train in accordance with demand, resources such as electric power and vehicles can be used efficiently, which can contribute to stabilization of management. In addition, schedule changes can be planned in advance based on future demand forecasts, so crew officers and vehicle base managers can prepare in advance operations changes that accompany schedule changes, reducing the burden of schedule changes It becomes possible to make it.
なお、本発明は上記した実施例に限定されるものではなく、様々な変形例が含まれる。例えば、上記した実施例は本発明を分かりやすく説明するために詳細に説明したものであり、必ずしも説明した全ての構成を備えるものに限定されるものではない。また、ある実施例の構成の一部を他の実施例の構成に置き換えることが可能であり、また、ある実施例の構成に他の実施例の構成を加えることも可能である。また、各実施例の構成の一部について、他の構成の追加・削除・置換をすることが可能である。
In addition, this invention is not limited to the above-mentioned Example, Various modifications are included. For example, the above-described embodiments have been described in detail for easy understanding of the present invention, and are not necessarily limited to those having all the configurations described. Further, a part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of one embodiment. Further, it is possible to add, delete, and replace other configurations for a part of the configuration of each embodiment.
1 ダイヤ作成支援システム
100 人流計測・予測装置
110 プロセッサ 120 メモリ
121 人流処理部
122 外部情報処理部
123 変化量計算部
124 人流計算部
125 人流計測・外部要因結果テーブル
126 外部要因予測テーブル
200 ダイヤ作成装置
210 プロセッサ 220メモリ
221 列車本数計算部
222 ダイヤ作成計算部
223 乗車人数対列車本数テーブル
224 列車本数予測テーブル
225 列車本数許容範囲テーブル
226 基本本数テーブル
227 代替路線テーブル
228 列車計画テーブル
229 車両運用計画テーブル
230 乗務員運用計画テーブル
300 人流予測データベース
400 入出力装置
500 関連装置
600 外部情報受信装置
601 外部情報受信部
602 外部情報生データテーブル
700 人流計測装置 1 Diamondcreation support system 100 Human flow measurement / prediction device 110 Processor 120 Memory 121 Human flow processing unit 122 External information processing unit 123 Change calculation unit 124 Human flow calculation unit 125 Human flow measurement / external factor result table 126 External factor prediction table 200 Diamond creation device 210 Processor 220 Memory 221 Train number calculation unit 222 Diagram creation calculation unit 223 Number of passengers vs. train number table 224 Train number prediction table 225 Train number allowable range table 226 Basic number table 227 Alternative route table 228 Train plan table 229 Vehicle operation plan table 230 Crew member operation plan table 300 Human flow prediction database 400 Input / output device 500 Related device 600 External information receiving device 601 External information receiving unit 602 External information raw data table 700 Human flow Measuring apparatus
100 人流計測・予測装置
110 プロセッサ 120 メモリ
121 人流処理部
122 外部情報処理部
123 変化量計算部
124 人流計算部
125 人流計測・外部要因結果テーブル
126 外部要因予測テーブル
200 ダイヤ作成装置
210 プロセッサ 220メモリ
221 列車本数計算部
222 ダイヤ作成計算部
223 乗車人数対列車本数テーブル
224 列車本数予測テーブル
225 列車本数許容範囲テーブル
226 基本本数テーブル
227 代替路線テーブル
228 列車計画テーブル
229 車両運用計画テーブル
230 乗務員運用計画テーブル
300 人流予測データベース
400 入出力装置
500 関連装置
600 外部情報受信装置
601 外部情報受信部
602 外部情報生データテーブル
700 人流計測装置 1 Diamond
Claims (9)
- 需要変動予測に対応した列車ダイヤの計画を支援するための列車ダイヤ作成支援システムであって、
管理対象である線区に属する駅ごとに収集された人流計測結果から各駅での単位時間あたりの乗降人数を算出し、需要変動の要因となる外部要因に関する情報である外部情報と関連付けて人流計測・外部要因結果情報として格納し、
将来発生が予定されている外部要因に関する前記外部情報を外部要因予測情報として格納し、
将来予定されている前記外部要因の前記外部情報について変更を検知した場合、変更された当該外部情報により前記外部要因予測情報を更新するとともに、変更された前記外部情報と一致する前記外部情報を含む前記人流計測・外部要因結果情報を取得し、当該人流計測・外部要因結果情報から前記外部情報変更が発生する線区の各駅間の乗車人数を算出し、人流予測情報として格納する人流予測部と、
前記人流予測部から受信した前記外部情報の変更に基づいて、当該変更発生日時に対応する前記人流予測情報を取得し、当該人流予測情報に含まれる前記外部情報変更が発生する線区の各駅間の乗車人数に基づいて各駅間で運転すべき列車本数を算出する列車本数計算部と、
を備えている列車ダイヤ作成支援システム。 A train diagram creation support system for supporting a plan of a train diagram corresponding to a demand fluctuation prediction,
Calculate the number of people getting on and off per unit time at each station from the results of people flow collected for each station belonging to the line to be managed, and correlate with external information that is information about external factors that cause demand fluctuations -Stored as external cause result information
Storing the external information related to external factors that are expected to occur in the future as external factor prediction information;
When the external information of the external factor scheduled in the future is detected, the external factor prediction information is updated with the changed external information, and the external information that matches the changed external information is included. A flow prediction unit that obtains the flow measurement / external factor result information, calculates the number of passengers between each station in the line area where the external information change occurs from the flow measurement / external cause result information, and stores it as flow prediction information; ,
Based on the change of the external information received from the human flow prediction unit, obtain the human flow prediction information corresponding to the change occurrence date and time, between each station of the line section where the external information change included in the human flow prediction information occurs A train number calculation unit that calculates the number of trains to be operated between stations based on the number of passengers
Train schedule creation support system equipped with. - 請求項1に記載の列車ダイヤ作成支援システムであって、
前記人流予測部は、変更された前記外部情報と一致する前記外部情報を含む前記人流計測・外部要因結果情報をすべて取得し、当該人流計測・外部要因結果情報から前記外部情報変更が発生する線区の各駅間の乗車人数の平均値を算出する、列車ダイヤ作成支援システム。 The train diagram creation support system according to claim 1,
The human flow prediction unit acquires all of the human flow measurement / external factor result information including the external information that matches the changed external information, and a line on which the external information change is generated from the human flow measurement / external factor result information. Train diagram creation support system that calculates the average number of passengers between each station in the ward. - 請求項1に記載の列車ダイヤ作成支援システムであって、
前記列車本数計算部は、管理対象である線区の各駅間について通常の需要に対して設定されている乗車人数対列車本数情報を参照し、前記人流予測情報に含まれる前記外部情報変更が発生する線区の各駅間の乗車人数に対応する列車本数を前記乗車人数対列車本数情報から取得する、列車ダイヤ作成支援システム。 The train diagram creation support system according to claim 1,
The train number calculation unit refers to information on the number of passengers vs. the number of trains set for normal demand between stations in the line area to be managed, and the external information change included in the human flow prediction information occurs A train diagram creation support system that obtains the number of trains corresponding to the number of passengers between stations in a line district from the information on the number of passengers versus number of trains. - 請求項1に記載の列車ダイヤ作成支援システムであって、
前記列車本数計算部は、管理対象である線区全体について許容される列車本数の範囲である全体本数許容範囲情報を参照し、前記人流予測情報に含まれる前記外部情報変更が発生する線区の各駅間の乗車人数に基づいて算出した各駅間で運転すべき列車本数の最小値が前記全体本数許容範囲内であると判定した場合、当該最小値を当該線区の列車本数と設定し、前記最小値が前記全体本数許容範囲外であると判定した場合、当該許容範囲の上限値又は下限値を当該線区の列車本数として設定する、列車ダイヤ作成支援システム。 The train diagram creation support system according to claim 1,
The train number calculation unit refers to the entire number allowable range information that is the range of the allowable number of trains for the entire line area to be managed, and the line number of the line area where the external information change included in the human flow prediction information occurs When it is determined that the minimum value of the number of trains to be operated between the stations calculated based on the number of passengers between the stations is within the overall number allowable range, the minimum value is set as the number of trains in the line, A train diagram creation support system that sets an upper limit value or a lower limit value of the permissible range as the number of trains in the line section when it is determined that the minimum value is outside the permissible total number. - 請求項1に記載の列車ダイヤ作成支援システムであって、
前記列車本数計算部は、管理対象である線区の各駅間について許容される列車本数の範囲である駅間列車本数許容範囲情報を参照し、前記人流予測情報に含まれる前記外部情報変更が発生する線区の各駅間の乗車人数に基づいて算出した各駅間で運転すべき列車本数が前記駅間列車本数許容範囲内であると判定した場合、当該列車本数を該当駅間での列車本数に設定し、前記人流予測情報に含まれる前記外部情報変更が発生する線区の各駅間の乗車人数に基づいて算出した各駅間で運転すべき列車本数が前記駅間列車本数許容範囲の下限値未満であると判定した場合、当該下限値を該当駅間での列車本数に設定し、前記人流予測情報に含まれる前記外部情報変更が発生する線区の各駅間の乗車人数に基づいて算出した各駅間で運転すべき列車本数が前記駅間列車本数許容範囲の上限値を超えると判定した場合、当該上限値を該当駅間での列車本数に設定するとともに、前記列車本数と前記上限値との差分を理想値として格納する、列車ダイヤ作成支援システム。 The train diagram creation support system according to claim 1,
The train number calculation unit refers to the inter-station train number allowable range information that is the range of the allowable number of trains for each station in the line area to be managed, and the external information change included in the human flow prediction information occurs If it is determined that the number of trains to be operated between each station calculated based on the number of passengers between each station in the line district to be within the allowable number of trains between the stations, the number of trains to the number of trains between the corresponding stations Set and the number of trains to be operated between stations calculated based on the number of passengers between stations in the line section where the external information change occurs included in the human flow prediction information is less than the lower limit value of the allowable number of trains between stations Each station calculated based on the number of passengers between each station in the line section where the external information change included in the human flow prediction information occurs, the lower limit is set to the number of trains between the stations Trains to drive between When it is determined that the number exceeds the upper limit of the allowable number of trains between stations, the upper limit is set to the number of trains between the corresponding stations, and the difference between the number of trains and the upper limit is stored as an ideal value. Train schedule creation support system. - 請求項1に記載の列車ダイヤ作成支援システムであって、
管理対象である線区の各駅間についての代替路線情報を格納しており、
前記列車本数計算部は、前記人流予測情報に含まれる前記外部情報変更が発生する線区の各駅間の乗車人数に基づいて算出した各駅間で運転すべき列車本数が前記駅間列車本数許容範囲の上限値を超えると判定した場合、当該駅間についての前記代替路線情報の有無を検索する、列車ダイヤ作成支援システム。 The train diagram creation support system according to claim 1,
Stores alternative route information for each station in the line area being managed,
The number of trains calculating section is based on the number of trains to be operated between the stations calculated based on the number of passengers between the stations of the line section where the external information change occurs included in the human flow prediction information. If it is determined that the upper limit value is exceeded, a train diagram creation support system that searches for the presence or absence of the alternative route information between the stations. - 請求項5に記載の列車ダイヤ作成支援システムであって、
前記列車本数計算部の処理結果を出力する出力部を備え、
前記列車本数計算部は、算出された列車本数に対応する日付、曜日、時間帯、線区、運転方向の全項目又は一部の項目と、該当線区全体の列車本数、及び当該線区の各駅間の列車本数を前記出力部から出力する、列車ダイヤ作成支援システム。 The train diagram creation support system according to claim 5,
An output unit that outputs the processing result of the train number calculation unit;
The train number calculation unit includes all items or some items of the date, day of the week, time zone, line section, operation direction corresponding to the calculated number of trains, the number of trains for the entire line section, and A train diagram creation support system for outputting the number of trains between stations from the output unit. - 請求項7に記載の列車ダイヤ作成支援システムであって、
前記列車本数計算部は、前記各駅間について、算出された前記列車本数、駅間列車本数許容範囲を超えた場合の理想値、駅間列車本数許容範囲上限値超過の有無、及び前記代替路線情報のすべての項目又は一部の項目を前記出力部から出力する、列車ダイヤ作成支援システム。 The train diagram creation support system according to claim 7,
The train number calculation unit, for each station, the calculated number of trains, the ideal value when the number of trains between stations exceeds the allowable range, whether the number of trains between stations exceeds the upper limit allowable range, and the alternative route information A train diagram creation support system that outputs all or some of the items from the output unit. - 需要変動予測に対応した列車ダイヤの計画を支援するための列車ダイヤ作成支援方法であって、
プロセッサとメモリとを有するコンピュータが、
管理対象である線区に属する駅ごとに収集された人流計測結果から各駅での単位時間あたりの乗降人数を算出し、需要変動の要因となる外部要因に関する情報である外部情報と関連付けて人流計測・外部要因結果情報として格納し、
将来発生が予定されている外部要因に関する前記外部情報を外部要因予測情報として格納し、
将来予定されている前記外部要因の前記外部情報について変更を検知した場合、変更された当該外部情報により前記外部要因予測情報を更新するとともに、変更された前記外部情報と一致する前記外部情報を含む前記人流計測・外部要因結果情報を取得し、当該人流計測・外部要因結果情報から前記外部情報変更が発生する線区の各駅間の乗車人数を算出し、人流予測情報として格納し、
前記外部情報の変更に基づいて、当該変更発生日時に対応する前記人流予測情報を取得し、当該人流予測情報に含まれる前記外部情報変更が発生する線区の各駅間の乗車人数に基づいて各駅間で運転すべき列車本数を算出する、
列車ダイヤ作成支援方法。 A train diagram creation support method for supporting a train schedule plan corresponding to demand fluctuation prediction,
A computer having a processor and memory
Calculate the number of people getting on and off per unit time at each station from the results of people flow collected for each station belonging to the line to be managed, and correlate with external information that is information about external factors that cause demand fluctuations -Stored as external cause result information
Storing the external information related to external factors that are expected to occur in the future as external factor prediction information;
When the external information of the external factor scheduled in the future is detected, the external factor prediction information is updated with the changed external information, and the external information that matches the changed external information is included. Obtain the human flow measurement / external factor result information, calculate the number of passengers between each station of the line section where the external information change occurs from the human flow measurement / external factor result information, and store it as human flow prediction information,
Based on the change of the external information, the person flow prediction information corresponding to the change occurrence date and time is acquired, and each station based on the number of passengers between the stations of the line section where the external information change included in the person flow prediction information occurs Calculate the number of trains to be
Train schedule creation support method.
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