Railway real-time traffic flow calculation method and device

Abstract

The invention relates to the technical field of railway traffic flow calculation, and particularly discloses a railway real-time traffic flow calculation method and equipment. The method comprises the following steps of S1, constructing a railway transportation database, S2, fusing and preprocessing acquired data, S3, determining a running path of a train, S4, constructing a train running aging coefficient model, S5, constructing a boundary train handover efficiency coefficient model, S6, calculating a railway traffic flow according to the coefficient model, and S7, calculating the railway traffic flow according to the coefficient model. Step S8, carrying out application feedback on the traffic flow calculation result; according to the method, the real-time calculation of the railway traffic flow is realized by constructing the multi-dimensional time analysis model, the traffic flow calculation result is corrected and adjusted, and the prediction accuracy and reliability are improved, so that the train dispatching and resource allocation are optimized, and the operation efficiency and emergency response capability of a railway system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of railway vehicle flow estimation, and in particular to a method and device for estimating railway vehicle flow in real time. Background Art

[0002] Railway transportation is a key link connecting economic regions, and its efficiency directly affects the circulation of materials and economic development. Traffic flow estimation involves the processing and prediction of train information. Through data collection, storage, transmission and calculation, traffic flow information is processed, and based on the train's en route and at the station, different business processing methods are used to obtain short-, medium- and long-term traffic flow forecasts. Accurate traffic flow estimation is crucial to the preparation of railway transportation plans, especially under the unified dispatch of the entire railway, which is of great significance to improving freight efficiency and reducing costs.

[0003] Traditional methods for estimating railway traffic usually rely on historical data and empirical rules for prediction. These methods are mainly based on past operating data, such as train schedules, actual operating time, train marshaling plans, and station arrival and departure times, and derive traffic distribution patterns through statistical analysis. On this basis, some static or quasi-static models are used to estimate future traffic conditions. This method usually requires manual participation and relies on statistical data within a fixed time window. Although traditional traffic estimation methods are simple and easy to use, they also have inevitable defects.

[0004] On the one hand, the traditional method cannot reflect the current actual operation status in time, especially in the face of emergencies (such as natural disasters, equipment failures, etc.), and its prediction accuracy will drop significantly. On the other hand, the traditional method does not analyze the time of trains at the station, fails to analyze the operation time when the train passes through the station in detail, and ignores the situation that the train does not perform relevant operations or stop at the station, which causes a large error in the traffic flow estimation work. In addition, the traditional method often relies on fixed models and algorithms, which is difficult to adjust flexibly according to the actual situation. It fails to analyze the efficiency of railway train operation and the efficiency of train handover at the boundary, and cannot accurately estimate the railway traffic, nor can it make real-time corrections to the estimated results of the railway traffic, and cannot ensure the accuracy of the traffic flow estimation. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a railway real-time vehicle flow estimation method and device to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a method and device for calculating real-time railway vehicle flow, the method comprising the following steps: step S1: constructing a railway transportation database, step S2: fusing and preprocessing the acquired data, step S3: determining the running path of the train, step S4: constructing a train running efficiency coefficient model, step S5: constructing a boundary train handover efficiency coefficient model, step S6: calculating the railway vehicle flow based on the coefficient model, step S7: correcting and adjusting the vehicle flow calculation results, and step S8: applying feedback to the vehicle flow calculation results.

[0007] Step S1: constructing a railway transportation database: constructing a railway transportation database including basic information of railway lines, train operation data, timetables, station information, signal system data, transportation plans and management data, and the database is used to store and update railway transportation data; Step S2: fusing and preprocessing the acquired data: collecting railway operation data in real time through IoT sensors installed at railway sections and stations, integrating them into a railway operation data set using data fusion technology, and preprocessing the railway operation data set; Step S3: Determine the running path of the train: adopt the shortest traffic path algorithm to determine the running path of the train based on the departure station, arrival station and type of the train; Step S4: constructing a train operation efficiency coefficient model: constructing a train operation efficiency coefficient model by analyzing the travel time of the train section in the en-route state and the operation time of the train in the station state, and calculating the railway train operation efficiency coefficient; Step S5: constructing a boundary train handover efficiency coefficient model: constructing a boundary train handover efficiency coefficient model by calculating and analyzing the operation time of the newly added trains, and calculating the boundary train handover efficiency coefficient; Step S6: Calculate the railway traffic flow according to the coefficient model: Calculate the real-time railway traffic flow according to the railway train running efficiency coefficient in step S4, the intersection train handover efficiency coefficient in step S5 and the railway operation comprehensive parameters; Step S7: Correcting and adjusting the traffic flow estimation result: Correcting and adjusting the estimation result in step S7 in real time based on the actual location of the train, the travel speed, the train length, the train operation time and the weather condition parameters; Step S8: Applying feedback on the traffic flow estimation results: Applying the traffic flow estimation results corrected and adjusted in step S7, collecting feedback and data based on actual application conditions, and continuously optimizing and improving the estimation method.

[0008] Preferably, the IoT sensor includes a GPS positioning device, an RFID tag reader and an infrared detector.

[0009] Preferably, the railway operation data set includes train number, number of train numbers, vehicle type, departure time, arrival time, number of passengers getting on and off at a station, cargo loading and unloading time, train operation status, travel section distance between two stations, average travel speed of the travel section between two stations, corresponding operation time of the train at different types of stations, total operation distance of the train, train operation speed, train traction power, number of trains and standard time for train handover operations.

[0010] Preferably, the specific content of preprocessing the railway operation data set in step S2 is: cleaning the railway operation data set, removing duplicate, invalid or erroneous data, and formatting the data including unifying the time format and data unit; the specific content of cleaning the railway operation data set is: using a data cleaning algorithm, identifying outliers based on filtering and statistical methods, and filling missing values ​​with interpolation.

[0011] Preferably, the specific steps of constructing the train operation efficiency coefficient model are as follows: Step S41: Classify the train status of railway traffic in the transportation process into the on-the-way status and the at-station status, where the at-station status includes the boundary station, marshaling station and ordinary passing station; the train passes through N stations in the complete traffic route, and each station is defined as n i , based on which we get the station set formula: , where i represents the station number; Step S42: constructing a train section running time analysis model in the en-route state, as follows: ,in, Tr It is expressed as the total time that the train is in transit. Sd i Represented as the i-th station n i With the i+1th station n i+1 The travel distance between As i Represented as the i-th station n i With the i+1th station n i+1 Average travel speed in the travel sections between; Step S43: constructing an analysis model for the operation time of trains in station status, as follows: ,in, Ts It is expressed as the total time that the train is in the station state. t iIndicates that the train is at the station n i The operation time matrix is , t 1 represents the operation time of the train at the boundary station, t 2 represents the technical operation time of the train at the marshaling yard, t 3 represents the operating time of the train at the normal station, where Expressed as the station type decision matrix, a 1, a 2, a 3 represents a variable between 0 and 1; Step S44: construct a train operation efficiency coefficient model, as follows: ,in, OEC Expressed as the train running efficiency coefficient, P Expressed as the traction power of the train, Rd It is the total running distance of the train. Rv It is the running speed of the train. e Expressed as a natural constant, Ts It is expressed as the total time that the train is in the station state. Tr It is expressed as the total time that the train is in transit.

[0012] Preferably, the steps of constructing the boundary train handover efficiency coefficient model are as follows: Step S51: Calculate the additional train operation time AT , the calculation model is as follows: ,in, Td It is the time when the train leaves the station. Ta It is the time when the train arrives at the station; Step S52: Calculate new traffic flow operation time NT , the calculation model is as follows: , where t represents the time value of the boundary for train operation under different operation types, AT It represents the time of newly added train operation, and n represents the number of historical train handover operations; Step S53: Calculate the efficiency coefficient of train handover at the junction, and the calculation model is as follows: ,in, HEC represents the efficiency coefficient of train handover at the junction, exp Expressed as a natural exponential function, NT Represents the new traffic operation time, m is the number of trains, OtIndicates the standard time for train handover operations.

[0013] Preferably, the comprehensive railway operation parameters include train timetable, actual operation status, station operation time, line conditions and external factors; the train timetable includes the planned departure and arrival times of trains; the actual operation status includes the actual speed, position and delay information of the train; the station operation time includes the stop time and loading and unloading time of the train at each station; the line conditions include the signal system status and track maintenance; the external factors include weather conditions and emergencies.

[0014] Preferably, the traffic flow estimation correction model for correcting and adjusting the traffic flow estimation result in step S6 is specifically: ,in, CM It is represented as the output value of the railway traffic estimation correction model, β is represented as the weather impact factor, v base is the expected train speed, L is the length of the train, m is the number of trains, α is the time correction factor, Tr It is expressed as the total time that the train is in transit. Ts It is expressed as the total time that the train is in the station state. NT It is represented as the new traffic operation time, γ is the influencing factor of train position change, and (x, y) is the coordinates of the actual location of the train.

[0015] Technical effects and advantages of the present invention: 1. The present invention can monitor and predict the train flow in the railway network in real time, help railway operators to adjust the dispatching plan in time, reduce service interruptions caused by train delays or congestion, and thus significantly improve the overall operational efficiency of the railway system; 2. The present invention achieves accurate prediction of train running paths and times by constructing a railway transportation database and a multi-dimensional time analysis model; this not only improves the operational efficiency and safety of the railway system, reduces train delays and congestion, but also optimizes resource allocation and supports scientific decision-making; in addition, the method enhances the emergency response capability of the railway system, significantly improves passengers' travel experience and reduces operating costs by quickly assessing the impact of emergencies and adjusting scheduling plans. The accuracy and real-time nature of the model provide strong support for the future development of intelligent transportation systems; 3. The present invention realizes the effective management and rapid processing of massive data by constructing a railway transportation database and performing data fusion and preprocessing, thereby improving the efficiency and accuracy of data processing and laying a solid foundation for subsequent analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0017] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] See also Figure 1 As shown, the present invention provides a method for calculating real-time railway vehicle flow, and the method comprises the following steps: step S1: constructing a railway transportation database, step S2: fusing and preprocessing the acquired data, step S3: determining the running path of the train, step S4: constructing a train running efficiency coefficient model, step S5: constructing a boundary train handover efficiency coefficient model, step S6: calculating the railway vehicle flow according to the coefficient model, step S7: correcting and adjusting the vehicle flow calculation result, and step S8: applying feedback to the vehicle flow calculation result.

[0020] Step S1: constructing a railway transportation database: constructing a railway transportation database including basic information of railway lines, train operation data, timetables, station information, signal system data, transportation plans and management data, and the database is used to store and update railway transportation data; In this embodiment, it should be specifically explained that the basic information of the railway line includes the line name, starting point and end point, line length, line type, design speed, operating status, line geometry information and equipment information; the train operation data includes the train number, type, starting station and terminal station, departure and arrival time, running speed, interval running time and stop time; the timetable includes the departure time, arrival time and stop time at intermediate stations; the station information includes the name, type, location, equipment, average daily passenger flow and peak passenger flow of the station; the signal system data includes signal type, status, turnout status, section occupancy and signal equipment history; the transportation plan includes train operation plan, operation diagram, transportation capacity and traffic organization plan; the management data includes safety management system, accident records, maintenance plan, employee information and operating costs.

[0021] Step S2: fusing and preprocessing the acquired data: collecting railway operation data in real time through IoT sensors installed at railway sections and stations, integrating them into a railway operation data set using data fusion technology, and preprocessing the railway operation data set; In this embodiment, it should be specifically explained that the IoT sensor includes a GPS positioning device, an RFID tag reader and an infrared detector.

[0022] In this embodiment, it should be specifically explained that the railway operation data set includes train number, number of train numbers, vehicle type, departure time, arrival time, number of passengers getting on and off at the station, cargo loading and unloading time, train operation status, travel section distance between two stations, average travel speed of the travel section between two stations, corresponding operation time of the train at different types of stations, total operation distance of the train, train operation speed, train traction power, number of trains and standard time for train handover operations.

[0023] In this embodiment, it should be specifically explained that the specific content of preprocessing the railway operation data set in step S2 is: cleaning the railway operation data set, removing duplicate, invalid or erroneous data, and formatting the data including unifying the time format and data unit; the specific cleaning of the railway operation data set is: using a data cleaning algorithm, identifying outliers based on filtering and statistical methods, and filling missing values ​​with interpolation.

[0024] Step S3: Determine the running path of the train: adopt the shortest traffic path algorithm to determine the running path of the train based on the departure station, arrival station and type of the train; Step S4: constructing a train operation efficiency coefficient model: constructing a train operation efficiency coefficient model by analyzing the travel time of the train section in the en-route state and the operation time of the train in the station state, and calculating the railway train operation efficiency coefficient; In this embodiment, it should be specifically explained that the specific steps of constructing the train operation efficiency coefficient model are as follows: Step S41: Classify the train status of railway traffic in the transportation process into the on-the-way status and the at-station status, where the at-station status includes the boundary station, marshaling station and ordinary passing station; the train passes through N stations in the complete traffic route, and each station is defined as n i , based on which we get the station set formula: , where i represents the station number; In this embodiment, it should be specifically explained that railway transportation involves a relatively complex process, including multiple stations. Railway traffic includes two states during transportation, namely, the in-transit state and the at-station state, and these two states appear alternately, so there will be state transition points and state durations. In this process, the transportation time of railway vehicles is relatively complicated. By classifying the traffic state to a certain extent, it is helpful to improve the accuracy of the calculation.

[0025] Step S42: constructing a train section running time analysis model in the en-route state, as follows: ,in, Tr It is expressed as the total time that the train is in transit. Sd i Represented as the i-th station n i With the i+1th station n i+1 The travel distance between As i Represented as the i-th station n i With the i+1th station n i+1 Average travel speed in the travel sections between; In this embodiment, it should be specifically explained that the train travel speed (referred to as travel speed) refers to the average number of kilometers per hour traveled by the train in the section, including the running time of the train in each section within the section and the stop time at the intermediate stations; the average travel speed of the train section is calculated by each railway bureau based on production data accumulated over many years, and can be uniformly queried and called by China Railway Corporation from each railway bureau.

[0026] Step S43: constructing an analysis model for the operation time of trains in station status, as follows: ,in, Ts It is expressed as the total time that the train is in the station state. t i Indicates that the train is at the station n i The operation time matrix is , t 1 represents the operation time of the train at the boundary station, t 2 represents the technical operation time of the train at the marshaling yard, t 3 represents the operating time of the train at the normal station, where Expressed as the station type decision matrix, a 1, a 2, a 3 represents a variable between 0 and 1; Step S44: construct a train operation efficiency coefficient model, as follows: ,in, OEC Expressed as the train running efficiency coefficient, P Expressed as the traction power of the train, Rd It is the total running distance of the train. Rv It is the running speed of the train. e Expressed as a natural constant, Ts It is expressed as the total time that the train is in the station state. Tr It is expressed as the total time that the train is in transit.

[0027] Step S5: constructing a boundary train handover efficiency coefficient model: constructing a boundary train handover efficiency coefficient model by calculating and analyzing the operation time of the newly added trains, and calculating the boundary train handover efficiency coefficient; In this embodiment, it should be specifically explained that the steps of constructing the boundary train handover efficiency coefficient model are as follows: Step S51: Calculate the additional train operation time AT , the calculation model is as follows: ,in, Td It is the time when the train leaves the station. Ta It is the time when the train arrives at the station; Step S52: Calculate new traffic flow operation time NT , the calculation model is as follows: , where t represents the time value of the boundary for train operation under different operation types, AT It represents the time of newly added train operation, and n represents the number of historical train handover operations; Step S53: Calculate the efficiency coefficient of train handover at the junction, and the calculation model is as follows: ,in, HEC represents the efficiency coefficient of train handover at the junction, exp Expressed as a natural exponential function, NT Represents the new traffic operation time, m is the number of trains, Ot Indicates the standard time for train handover operations.

[0028] Step S6: Calculate the railway traffic flow according to the coefficient model: Calculate the real-time railway traffic flow according to the railway train running efficiency coefficient in step S4, the intersection train handover efficiency coefficient in step S5 and the railway operation comprehensive parameters; In this embodiment, it should be specifically explained that the comprehensive railway operation parameters include the train timetable, actual operation status, station operation time, line conditions and external factors; the train timetable includes the planned train departure and arrival times; the actual operation status includes the actual speed, position and delay information of the train; the station operation time includes the train's stop time and loading and unloading time at each station; the line conditions include the signal system status and track maintenance; the external factors include weather conditions and emergencies.

[0029] Step S7: Correcting and adjusting the traffic flow estimation result: Correcting and adjusting the estimation result in step S7 in real time based on the actual location of the train, the travel speed, the train length, the train operation time and the weather condition parameters; In this embodiment, it should be specifically explained that the traffic flow estimation correction model for correcting and adjusting the traffic flow estimation result in step S6 is specifically: ,in, CM It is represented as the output value of the railway traffic estimation correction model, β is represented as the weather impact factor, v base is the expected train speed, L is the length of the train, m is the number of trains, α is the time correction factor, Tr It is expressed as the total time that the train is in transit. Ts It is expressed as the total time that the train is in the station state. NT It is represented as the new traffic operation time, γ is the influencing factor of train position change, and (x, y) is the coordinates of the actual location of the train.

[0030] Step S8: Applying feedback on the traffic flow estimation results: Applying the traffic flow estimation results corrected and adjusted in step S7, collecting feedback and data based on actual application conditions, and continuously optimizing and improving the estimation method.

[0031] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0032] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for calculating real-time railway traffic flow, characterized in that: The following steps are involved: Step S1: constructing a railway transportation database: constructing a railway transportation database including basic information of railway lines, train operation data, timetables, station information, signal system data, transportation plans and management data, and the database is used to store and update railway transportation data; Step S2: fusing and preprocessing the acquired data: collecting railway operation data in real time through IoT sensors installed at railway sections and stations, integrating them into a railway operation data set using data fusion technology, and preprocessing the railway operation data set; Step S3: Determine the running path of the train: adopt the shortest traffic path algorithm to determine the running path of the train based on the departure station, arrival station and type of the train; Step S4: constructing a train operation efficiency coefficient model: constructing a train operation efficiency coefficient model by analyzing the travel time of the train section in the en-route state and the operation time of the train in the station state, and calculating the railway train operation efficiency coefficient; Step S5: constructing a boundary train handover efficiency coefficient model: constructing a boundary train handover efficiency coefficient model by calculating and analyzing the operation time of the newly added trains, and calculating the boundary train handover efficiency coefficient; Step S6: Calculate the railway traffic flow according to the coefficient model: Calculate the real-time railway traffic flow according to the railway train running efficiency coefficient in step S4, the intersection train handover efficiency coefficient in step S5 and the railway operation comprehensive parameters; Step S7: Correcting and adjusting the traffic flow estimation result: Correcting and adjusting the estimation result in step S7 in real time based on the actual location of the train, the travel speed, the train length, the train operation time and the weather condition parameters; Step S8: Applying feedback on the traffic flow estimation results: Applying the traffic flow estimation results corrected and adjusted in step S7, collecting feedback and data based on actual application conditions, and continuously optimizing and improving the estimation method.

2. A method for calculating real-time railway traffic flow according to claim 1, characterized in that: The IoT sensor includes a GPS positioning device, an RFID tag reader, and an infrared detector.

3. A method for calculating real-time railway traffic flow according to claim 1, characterized in that: The railway operation data set includes train number, number of train numbers, model, departure time, arrival time, cargo loading and unloading time, train operation status, travel section distance between two stations, average travel speed of the travel section between two stations, operation time of the train at different types of stations, total operation distance of the train, train operation speed, train traction power, number of trains and standard time for train handover operation.

4. A method for calculating real-time railway traffic flow according to claim 1, characterized in that: The specific content of preprocessing the railway operation data set in step S2 is: cleaning the railway operation data set, removing duplicate, invalid or erroneous data, and formatting the data including unifying the time format and data unit; the specific content of cleaning the railway operation data set is: using a data cleaning algorithm, identifying outliers based on filtering and statistical methods, and filling missing values ​​with interpolation.

5. The method for calculating real-time railway traffic flow according to claim 1, characterized in that: The specific steps of constructing the train operation efficiency coefficient model are as follows: Step S41: classifying the train status of railway vehicles during transportation into the on-the-way status and the at-station status, wherein the at-station status includes three situations: the boundary station, the marshalling station, and the ordinary passing station; The train passes through N stations in the complete traffic flow route, and each station is defined as n i , based on which we get the station set formula: , where i represents the station number; Step S42: constructing a train section running time analysis model in the en-route state, as follows: ,in, Tr It is expressed as the total time that the train is in transit. Sd i Represented as the i-th station n i With the i+1th station n i+1 The travel distance between As i Represented as the i-th station n i With the i+1th station n i+1 Average travel speed in the travel sections between; Step S43: constructing an analysis model for the operation time of trains in station status, as follows: ,in, Ts It is expressed as the total time that the train is in the station state. t i Indicates that the train is at the station n i The operation time matrix is , t 1 represents the operation time of the train at the boundary station, t 2 represents the technical operation time of the train at the marshaling yard, t 3 represents the operating time of the train at the normal station, where Expressed as the station type decision matrix, a 1, a 2, a 3 represents a variable between 0 and 1; Step S44: construct a train operation efficiency coefficient model, as follows: ,in, OEC Expressed as the train running efficiency coefficient, P Expressed as the traction power of the train, Rd It is the total running distance of the train. R It is the running speed of the train. e Expressed as a natural constant, Ts It is expressed as the total time that the train is in the station state. Tr It is expressed as the total time that the train is in transit.

6. A method for calculating real-time railway traffic flow according to claim 1, characterized in that: The steps of constructing the boundary train handover efficiency coefficient model are as follows: Step S51: Calculate the additional train operation time AT , the calculation model is as follows: ,in, Td It is the time when the train leaves the station. Ta It is the time when the train arrives at the station; Step S52: Calculate new traffic flow operation time NT , the calculation model is as follows: , where t represents the time value of the boundary for train operation under different operation types, AT It represents the time of newly added train operation, and n represents the number of historical train handover operations; Step S53: Calculate the efficiency coefficient of train handover at the junction, and the calculation model is as follows: ,in, HEC represents the efficiency coefficient of train handover at the junction, exp Expressed as a natural exponential function, NT Represents the new traffic operation time, m is the number of trains, Ot Indicates the standard time for train handover operations.

7. A method for calculating real-time railway traffic flow according to claim 1, characterized in that: The railway operation comprehensive parameters include train timetable, actual operation status, station operation time, line conditions and external factors; the train timetable includes the planned train departure and arrival times; The actual operating status includes the actual speed, position and delay information of the train; the station operation time includes the train's stop time and loading and unloading time at each station; the line condition includes the signal system status and track maintenance; the external factors include weather conditions and emergencies.

8. A method for calculating real-time railway traffic flow according to claim 1, characterized in that: The traffic flow estimation correction model used to correct and adjust the traffic flow estimation result in step S6 is specifically: ,in, CM It is represented as the output value of the railway traffic estimation correction model, β is represented as the weather impact factor, v base is the expected train speed, L is the length of the train, m is the number of trains, α is the time correction factor, Tr It is expressed as the total time that the train is in transit. Ts It is expressed as the total time that the train is in the station state. NT It is represented as the new traffic operation time, γ is the influencing factor of train position change, and (x, y) is the coordinates of the actual location of the train.

9. A railway real-time vehicle flow estimation device, characterized in that: The railway real-time vehicle flow estimation device comprises a computer program, and when the computer program is executed in the memory of the server, the computer program implements a railway real-time vehicle flow estimation method as described in any one of claims 1 to 8.

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