Simulation method and device for single-line passenger flow of rail transit and storage medium

Abstract

The invention discloses a rail transit single line passenger flow simulation method and device and a storage medium. The method comprises the steps that a to-be-simulated single line is determined on rail transit; the line comprises a plurality of stations and intervals between two adjacent stations; real-time or historical passenger flow parameters generated on the line are set for each station and each interval; according to the passenger flow parameters, calculating the passenger flow contribution rate of the newly added passenger flow of each station to each interval; and in a simulation scene of transport capacity change and / or passenger flow change, performing iterative deduction on the passenger flow of each station and each interval according to the passenger flow parameters and / or the contribution rate. The embodiment does not depend on OD data, passenger flow simulation can be carried out on a single line in rail transit in time, and the real-time performance of passenger flow simulation is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of rail transit, and in particular, to a simulation method, device, and storage medium for single-line passenger flow of rail transit. Background Art

[0002] The networked operation of urban rail transit has formed a situation of "seamless connection" transfer with a single ticket for passengers, bringing convenience to passenger travel.

[0003] Currently, passenger flow simulation mainly relies on OD (Origin-Destination) passenger flow data of the urban rail transit network. By using mathematical models or software to simulate the passenger flow behavior and its distribution along the lines and in the network with the movement of trains, the congestion risk, evacuation efficiency, and equipment load during peak hours can be predicted, supporting the optimization of station design, the formulation of emergency plans, and the adjustment of operation strategies.

[0004] However, in actual operation and production, there are usually multiple operation entities in the rail transit network, each responsible for its own affiliated lines, resulting in the lag in obtaining the clearing data for the networked operation of rail transit. Therefore, there is a lag in obtaining refined passenger flow data for a single line, leading to a lag in passenger flow simulation. Summary of the Invention

[0005] In view of this, the present invention provides a simulation method, device, and storage medium for single-line passenger flow of rail transit, which can perform passenger flow simulation without relying on the OD passenger flow data of a single rail transit line, improving the real-time performance of passenger flow simulation.

[0006] The first aspect of the present invention provides a simulation method for single-line passenger flow of rail transit, including:

[0007] Determine a single line to be simulated on the rail transit; the line includes multiple stations and sections between adjacent stations;

[0008] Set passenger flow parameters generated in real-time or historically on the line for each of the stations and each of the sections;

[0009] Calculate the contribution rate of the passenger flow newly added to each station to the passenger flow of each section based on the passenger flow parameters;

[0010] In a simulation scenario of capacity change and / or passenger flow change, iterate and deduce the passenger flow of each station and each section based on the passenger flow parameters and / or the contribution rate.

[0011] The second aspect of the present invention provides a simulation device for single-line passenger flow of rail transit, including:

[0012] A route determination module is used to determine a single route to be simulated in rail transit; the route includes multiple stations and sections between two adjacent stations;

[0013] A passenger flow parameter setting module, configured to set real-time or historical passenger flow parameters generated on the line for each of the stations and each of the sections;

[0014] A contribution rate calculation module, configured to calculate the contribution rate of the passenger flow added at each station to the passenger flow of each section according to the passenger flow parameters;

[0015] The scenario passenger flow simulation module is used to iteratively deduce the passenger flow of each of the stations and each of the sections based on the passenger flow parameters and / or the contribution rate in a simulation scenario of capacity changes and / or passenger flow changes.

[0016] A third aspect of the present invention provides an electronic device, comprising:

[0017] at least one processor; and

[0018] a memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the simulation method of single-line passenger flow of rail transit as described in the first aspect above.

[0020] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for simulating single-line passenger flow of rail transit as described in the first aspect above.

[0021] A fifth aspect of the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the simulation method of rail transit single-line passenger flow as described in the first aspect above.

[0022] In this embodiment, a single line to be simulated is determined on the rail transit; the line includes multiple stations and sections between two adjacent stations; real-time or historical passenger flow parameters generated on the line are set for each station and each section; the contribution rate of the new passenger flow from each station to the passenger flow of each section is calculated based on the passenger flow parameters; in simulation scenarios with changes in transportation capacity and / or passenger flow, the passenger flow of each station and each section is iteratively deduced based on the passenger flow parameters and / or contribution rate. This embodiment does not rely on OD data and can perform passenger flow simulation on a single line in rail transit in a timely manner, effectively improving the real-time performance of passenger flow simulation.

[0023] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0025] Figure 1 is a flowchart of a simulation method for single-line passenger flow of rail transit provided in the first embodiment of the present invention.

[0026] Figure 2 is a schematic diagram of passenger flow transfer provided in the first embodiment of the present invention.

[0027] Figure 3 is a schematic structural diagram of a simulation device for single-line passenger flow of rail transit provided in the second embodiment of the present invention.

[0028] Figure 4 is a schematic structural diagram of an electronic device provided in the third embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can cover sequences other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0031] In practical applications, there are multiple operating entities in the rail transit networks of many cities, each in charge of its own affiliated lines. For example, the subway Line 6 in a certain city is operated by a certain rail transit company rather than the city's rail transit group. Regularly dividing the network passenger flow paths and clearing the ticket revenue have become the tasks of the rail transit operating entities in various cities. Through the clearing system, it is possible to restore as much as possible the real passenger flow carried on each line, and its necessary condition is the origin-destination (OD) passenger flow data of passengers.

[0032] Among them, the OD passenger flow data refers to the spatio-temporal distribution data of the origin and destination of passengers' trips. By collecting information on passengers getting on and off the train through sensors, cameras, etc., analyzing the travel paths, peak hours, and transfer behaviors, it provides a basis for optimizing the line network and allocating transport capacity. For example, OD data can identify blind spots in the subway and supplement feeder lines.

[0033] Clearing refers to the financial settlement process of splitting the fare paid by passengers to each operating line or operator according to pre-agreed distribution rules (such as travel paths, transfer times, etc.) based on ticket transaction data. The clearing system takes into account factors such as the road network structure and transfer modes to ensure fair distribution of revenues.

[0034] Due to the inconsistency of operating entities and the lag of data in the rail transit clearing center system, there are barriers in aspects such as data sharing and obtaining the actual passenger flow data carried by lines, which brings difficulties in data support for passenger flow simulation under single-line operation.

[0035] The simulation models used in traditional line network operation simulation are mostly fine-grained train-passenger interaction simulations. The mechanism is to design passengers as individuals or aggregated groups and trains as multi-agents, and simulate their interaction relationships with train operations. The goal is to obtain the train numbers of passengers' trips and the running times of the corresponding trains. The interaction simulation between trains and passengers is a technology based on system dynamics theory and agent theory, using a computer to simulate the interaction process inside the train and between the train and passengers. The purpose is to simulate the behaviors of passengers in the network, stations, and trains, such as the process of passengers getting on and off the train, moving in the station, and using facilities such as trains and turnstiles, and can better obtain the states of passengers and trains in a complex network in multiple time and space dimensions.

[0036] However, in actual operation and production, due to the lag in obtaining clearing data for rail transit network operation and the often inability to obtain refined passenger flow data for a single line under real-time or short-term conditions, it is impossible to complete such passenger flow simulations.

[0037] In addition, focusing on a single line, there are four types of passenger flows on each line: ① the passenger flow of this line (entering this line and leaving this line), ② the incoming transfer passenger flow (entering from another line and leaving this line), ③ the outgoing transfer passenger flow (entering this line and leaving from another line), and ④ the transfer passenger flow (entering from another line and leaving from another line). The passenger flow involving transfers in a single line accounts for almost more than half of the passenger volume. It is difficult to obtain the OD data of this line (focusing on the origin and destination of the travel process on this line) in real-time or short-term situations.

[0038] Embodiment 1

[0039] Refer to Figure 1 , which shows a flowchart of a simulation method for the passenger flow of a single rail transit line provided in Embodiment 1 of the present invention. This embodiment is oriented to the simulation and deduction scenario of single-line operation, and proposes a simulation and deduction method. The purpose is to, without obtaining OD passenger flow data, use historical passenger flow parameters such as the platform population data obtained from real-time video monitoring and the cross-section passenger flow data obtained from historical and short-term predictions as inputs, and deduce the operation trend of the line under special operation scenarios (capacity change and / or passenger flow change). This method can be executed by a simulation device for the passenger flow of a single rail transit line. The simulation device for the passenger flow of a single rail transit line can be implemented in the form of hardware and / or software, and the simulation device for the passenger flow of a single rail transit line can be configured in an electronic device. As Figure 1 shown, the method includes:

[0040] Step 101: Determine a single line to be simulated on the rail transit.

[0041] In this embodiment, a single line to be simulated for passenger flow can be determined in the rail transit network. A single line includes multiple stations and the sections between adjacent two stations.

[0042] Step 102: Set the passenger flow parameters generated on the line in real-time or historically for each station and each section.

[0043] A single line is usually operated by an independent operation entity. Therefore, the information generated on the line for each station and each section can be independently collected and converted into various passenger flow parameters.

[0044] Exemplarily, some of the passenger flow parameters include at least one of the following:

[0045] 1. The original number of people staying on the platform It represents the number of people staying on the platform in the d direction of station N within a real-time unit time (such as per hour) (usually represented by statistical values such as the average value), and can be evaluated through a crowd density evaluation model under video monitoring.

[0046] 2. The original number of people queuing on the platform [[ID=3,4]] It represents the number of people queuing (waiting for the train) at the d-direction platform of station N in real-time unit time (such as per hour) (usually expressed as statistical values such as average values), which can be evaluated by the crowd density assessment model under video surveillance.

[0047] 3. Historical passenger flow It represents the cross-sectional passenger flow in the section from station N to station N-1 in historical unit time (such as per hour) under normal conditions, which can be queried from the line network center.

[0048] 4. Original cross-sectional capacity It represents the section capacity of the section from station N to station N-1 in historical unit time (such as per hour) under normal conditions, which can be calculated from the operation diagram.

[0049] Accordingly, when performing iterative deduction in the simulation scenario, the following passenger flow parameters are defined:

[0050] 5. Predict the number of people stranded on the platform It represents the number of people stranded at the d-direction platform of station N within the simulated unit time (such as per hour) (usually expressed as statistical values such as average values).

[0051] 6. Predict the number of people queuing at the platform It represents the number of people queuing (waiting for the train) at the platform in the d direction of station N per unit time (such as per hour) (usually expressed as a statistical value such as an average value).

[0052] 7. Deducing cross-section passenger flow It represents the cross-sectional passenger flow per unit time (e.g., per hour) in the interval from station N to station N-1 in the simulation scenario.

[0053] 8. Deduction cross-section capability It represents the cross-section capacity per unit time (e.g., per hour) of the section from station N to station N-1 in the simulation scenario.

[0054] Cross-sectional passenger flow refers to the number of passengers passing through a specific section (or section) of a rail transit line per unit time, divided into upstream and downstream cross-sectional flow. It is a key factor in train operation organization, station scale design, and equipment capacity allocation. For example, the maximum cross-sectional passenger flow during peak hours directly influences train formation plans.

[0055] Sectional capacity refers to the transportation capacity through a specific section (section) of a rail transit line per unit time, which is divided into upstream and downstream section capacities.

[0056] Among the above parameters, the original average number of people stranded on the platform and the original number of people queuing on the platform are used as input data to represent the supply and demand relationship level of the platform. Accordingly, when the original number of people stranded on the platform is > 0, it means that the passenger demand is large and the capacity is insufficient.

[0057] Step 103: Calculate the contribution rate of the newly added passenger flow at each station to the passenger flow in each interval based on the passenger flow parameters.

[0058] In this embodiment, each station and each interval on a single line can be traversed, and the influence degree of the newly added passenger flow at a certain station on the passenger flow in subsequent intervals can be calculated based on the passenger flow parameters as the contribution rate.

[0059] At the platform of a certain station in a certain direction, when there is an excess of passenger flow demand and passengers are detained at the platform, if new transport capacity is added, the passenger flow in the subsequent sections in this direction will also increase. The increase amplitude of the passenger flow in subsequent sections is related to the passenger flow contribution rate with this station as O and other stations as D.

[0060] In specific implementation, the passenger flow parameters include the newly added passenger flow at the current station and the moving probability; the moving probability represents the proportion of the historical passenger flow from the current station to the subsequent intervals.

[0061] On the one hand, set the contribution rate of the newly added passenger flow at the current station to the passenger flow in the prior interval to 0.

[0062] On the other hand, sum the products of the newly added passenger flow at the current station and each moving probability to obtain the transferred passenger flow in the subsequent intervals; divide the difference between the newly added passenger flow at the current station and the transferred passenger flow in the subsequent intervals by the newly added passenger flow at the current station as the contribution rate of the newly added passenger flow at the current station to the passenger flow in the subsequent intervals.

[0063] Furthermore, from the perspective of OD data, if part of the passenger flow Num is newly added at station N <U+ o Station, then this part of the passenger flow may go to the subsequent stations N1, N2, N3,..., N <U+ New , as <U+ n shown, assuming that according to historical data, the proportions (i.e., moving probabilities) of the passenger flow going to subsequent stations can be obtained as P1, P2, P3,..., P <U+ Figure 2 , then, for any station N, the passenger flow <U+ n passing through its subsequent interval N <U+ i →N <U+ i+1 is: <U+ is: <U+ <U+

[0064] <U+ <U+ <U+

[0065] Therefore, the contribution rate <U+ i →N <U+ i+1 of station N to interval N <U+ is: <U+ <U+

[0066] <U+ <U+ <U+

[0067] where i represents the i-th station, i ∈ n, and n is the total number of stations.

[0068] Under the condition that OD data is available, the set of each station can be calculated from historical data, which can reflect the movement trend and section contribution degree of the inbound passenger flow in a certain direction of a line. However, due to the situation where OD data cannot be obtained, the outbound volume statistics of each station can be used to replace the OD data for approximate calculation, that is, assuming that the flow distribution of the inbound passenger flow at a certain station is consistent with the total outbound volume distribution of other stations, the contribution rate of each station can be calculated in turn, and these contribution rates are represented in the form of matrices and the like.

[0069] Exemplarily, taking the passenger flow data of a certain period in the upward direction of the urban subway Line 6 as an example, a sample of the contribution degree is shown in the following matrix table.

[0070]

[0071]

[0072] Among them, the rows are stations and the columns are sections.

[0073] Step 104: In the simulation scenario of capacity change and / or passenger flow change, iterate and deduce the passenger flow of each station and each section according to the passenger flow parameters and / or contribution rate.

[0074] When simulating a single line, the contribution degree of the newly added passenger flow at the station to the subsequent section (denoted as matrix A) can be used as a fixed value, or it can change dynamically according to the historical situation of the total outbound volume or the short-term prediction situation of the total outbound volume.

[0075] Since the changes in passenger flow input and capacity change will cause chain changes in the cross-section passenger flow of multiple sections of the line, for the capacity change of the line, the target of the line passenger flow situation deduced in this embodiment is the change situation of the cross-section passenger volume of the line and the change situation of the load factor. The input and output of the iterative deduction algorithm for a certain direction of the line are shown in the following deduction table:

[0076]

[0077]

[0078] Among them, the above data belongs to a row of data in the deduction table. For the convenience of expression, it is converted into columns.

[0079] The adjustment of capacity may bring about changes in the cross-section passenger flow, which is related to the matching degree of the original supply and demand relationship. At the platform in the d direction of Station N, there are two passenger flow monitoring indicators, one is the number of people queuing on the platform and the other is the number of people staying on the platform It is assumed that Station N will travel towards Station N-1 in the upstream direction. When the number of passengers staying on the platform of Station N > 0, it means that the section N→N-1 in the forward direction is close to full load, the train capacity is occupied, and passengers are stranded.

[0080] In this embodiment, it is assumed that on the basis of obtaining passenger flow parameters such as the number of passengers staying on the platform, the number of passengers queuing on the platform, the section flow, and the section full load rate, the passenger flow parameters are used to study the iterative deduction of the line passenger flow situation under the conditions of changes in transport capacity and passenger flow, providing data support for the passenger flow situation deduction of a single line.

[0081] In the iterative deduction, the following several factors are usually considered:

[0082] (1) Changes in the section passenger flow and section full load rate caused by changes in transport capacity;

[0083] (2) Changes in the number of passengers queuing on the platform and the number of passengers staying that may be caused by changes in the passenger flow carrying capacity;

[0084] (3) Simulation of the impact of changes in the passenger flow carrying capacity in the upstream section on the satisfaction rate of the downstream section.

[0085] I. In the simulation scenario of changes in transport capacity:

[0086] The change in transport capacity is divided into an increase in transport capacity and a decrease in transport capacity. Suppose there are i departure research periods in total, then there is a corresponding train set train_list for each research period i i , and there is a corresponding deduction table B i for deduction.

[0087] Traverse the deduction table B i and input the data of each row in the deduction table B i sequentially from top to bottom.

[0088] At the beginning, if the deduced section passenger flow of the previous section is not 0 then assign the historical section passenger flow of the previous section to the deduced section passenger flow of the previous section

[0089] In the simulation scenario of an increase in transport capacity, if the historical section passenger flow of the previous section is less than the product of the original section capacity of the previous section and the restricted full load rate (the maximum full load rate restriction, usually 100%-120%, which is a hyperparameter) and (and) the deduced number of passengers staying on the platform of the current station is 0 then perform the following operations:

[0090] Assign the historical section passenger flow of the previous section to the deduced section passenger flow of the previous section

[0091] Divide the original number of passengers detained on the platform of the current station by the ratio between the simulated cross-section capacity of the previous section and the original cross-section capacity of the previous section, and assign the quotient to the deduced number of passengers queuing on the platform of the current station

[0092] Set the deduced number of passengers detained on the platform of the current station to 0

[0093] In the simulation scenario of capacity improvement, if the product of the simulated cross-section capacity of the previous section and the restricted full-load rate is greater than or equal to the sum of the historical cross-section passenger flow of the previous section and the original number of passengers detained at the current station Then perform the following operations:

[0094] Assign the sum of the historical cross-section passenger flow of the previous section and the original number of passengers detained at the current station to the deduced cross-section passenger flow of the previous section

[0095] Set the deduced number of passengers detained on the platform of the current station to 0

[0096] Assign the difference between the original number of passengers queuing on the platform of the current station and the original number of passengers detained on the platform of the current station to the deduced number of passengers queuing on the platform of the current station

[0097] If the product of the simulated cross-section capacity of the previous section and the restricted full-load rate is less than the sum of the historical cross-section passenger flow of the previous section and the original number of passengers detained at the current station Then perform the following operations:

[0098] Assign the product of the simulated cross-section capacity of the previous section and the restricted full-load rate to the deduced cross-section passenger flow of the previous section

[0099] Assign the product of the original number of passengers queuing on the platform of the current station and the cross-section passenger flow change coefficient of the previous section to the deduced number of passengers queuing on the platform of the current station

[0100] Assign the product of the original number of passengers detained on the platform of the current station and the cross-section passenger flow change coefficient of the previous section to the deduced number of passengers detained on the platform of the current station;

[0101] Among them, the cross-sectional passenger flow change coefficient of the previous interval is the absolute value of the difference between 1 and the cross-sectional passenger flow deviation rate of the previous interval; the cross-sectional passenger flow deviation rate of the previous interval is the difference between the deduced cross-sectional passenger flow volume of the previous interval and the historical cross-sectional passenger flow volume of the previous interval, divided by the quotient obtained by dividing the historical cross-sectional passenger flow volume of the previous interval

[0102] In the simulation scenario with reduced transport capacity, if the deduced cross-sectional passenger flow of the previous interval is greater than the historical cross-sectional passenger flow of the previous interval Then calculate the difference between the deduced cross-sectional passenger flow of the previous interval and the historical cross-sectional passenger flow of the previous interval, and use it as the cross-sectional passenger flow change value of the previous interval

[0103] Traverse the subsequent n intervals (i.e., cross-sections) sectionname and the corresponding contribution rates in matrix A Assign the sum of the historical cross-sectional passenger flow of the subsequent interval and the product obtained by multiplying the cross-sectional deviation value of the subsequent interval by the contribution rate of the subsequent interval to the deduced cross-sectional passenger flow of the subsequent interval Thereby update the deduced table B i The deduced cross-sectional passenger flow of the subsequent interval in

[0104] In the simulation scenario with reduced transport capacity, if the historical cross-sectional passenger flow of the previous interval is less than the product of the original cross-sectional capacity of the previous interval and the restricted full-load rate And the inferred platform queuing number of the current station is equal to 0 Then perform the following operations:

[0105] Assign the historical cross-sectional passenger flow of the previous interval to the deduced cross-sectional passenger flow of the previous interval

[0106] Assign the quotient obtained by dividing the original platform queuing number of the current station by the ratio between the simulated cross-sectional capacity of the previous interval and the original cross-sectional capacity of the previous interval to the deduced platform queuing number of the current station

[0107] Set the inferred platform queuing number of the current station to 0

[0108] In the simulation scenario with reduced transport capacity, assign the product of the simulated cross-sectional capacity of the previous interval and the restricted full-load rate to the deduced cross-sectional passenger flow volume of the previous interval

[0109] If the original platform queuing number of the current station is greater than 0 Then perform the following operations:

[0110] Assign the sum value between the deduced platform queuing number of the current station and the change amount of the cross-section capacity of the previous section to the average queuing number of the deduced platform of the current station

[0111] Assign the sum value between the deduced platform staying number of the current station and the change amount of the cross-section capacity of the previous section to the staying queuing number of the deduced platform of the current station

[0112] Among them, the change amount of the cross-section capacity of the previous section is the difference obtained by multiplying the simulated cross-section capacity of the previous section by the restricted full-load rate and subtracting the historical cross-section passenger flow of the previous section, and then taking the absolute value of the difference and the specified ratio ( 0.5 is an example of the ratio).

[0113] If the original platform staying number of the current station is less than or equal to 0 Then perform the following operations:

[0114] Assign the quotient obtained by dividing the original platform staying number of the current station by the ratio between the simulated cross-section capacity of the previous section and the original cross-section capacity of the previous section to the deduced platform queuing number of the current station

[0115] Assign the product between the absolute value of the cross-section passenger flow deviation rate of the previous section and the deduced platform queuing number of the current station to the deduced platform staying number of the current station

[0116] Among them, the cross-section passenger flow deviation rate of the previous section is the quotient obtained by dividing the difference between the deduced cross-section passenger flow of the previous section and the historical cross-section passenger flow of the previous section by the historical cross-section passenger flow of the previous section

[0117] If the deduced cross-section passenger flow of the previous section is less than the historical cross-section passenger flow of the previous section Then calculate the difference between the deduced cross-section passenger flow of the previous section and the historical cross-section passenger flow of the previous section as the cross-section passenger flow change value of the previous section;

[0118] Traverse the subsequent n sections (i.e., cross-sections) sectionname and the corresponding contribution rates in matrix A Assign the sum value between the historical cross-section passenger flow of the subsequent section and the product obtained by multiplying the cross-section deviation value of the subsequent section by the contribution rate of the subsequent section to the deduced cross-section passenger flow of the subsequent section Thus update the deduction table B i Deduced cross-sectional passenger flow in the middle and subsequent sections

[0119] After that, the difference between the deduced number of people stranded on the platform at the current station and the original number of people stranded on the platform at the current station is used to obtain the intermediate number of people stranded on the platform.

[0120] The deduction table B in this round of iterative deduction i The median number of people stranded at the platform for each row of data and the deduction table B in the next round of iterative deduction i+1 The original number of people stranded on the platform at the current station The sum of the values between them is updated to the deduction table B in the next round of iterative deduction i+1 The original number of people stranded on the platform at the current station

[0121] When the iterative deduction is completed, the deduction table B is output i Deduced cross-sectional passenger flow in each section

[0122] 2. In the simulation scenario of passenger flow changes:

[0123] Assume that the passenger flow changes (especially large passenger flow, that is, the passenger flow exceeds a certain threshold) and the station is S HPF , the passenger flow expansion factor is ω, there are a total of i study periods for large passenger flow stations, then each study period i has a corresponding train set train_list i , a total of train_sum i There are corresponding deduction tables for trains. i .

[0124] Traverse deduction table B i Enter the deduction table B in order from top to bottom i Each row of data.

[0125] If the current station is not a station with large passenger flow, S HPF , then perform the following operations:

[0126] Assign the historical section passenger flow of the previous interval to the deduced section passenger flow of the previous interval

[0127] Assign the original number of people queuing on the platform of the current station to the deduced number of people queuing on the platform of the current station

[0128] Assign the original number of people stranded on the platform of the current station to the deduced number of people stranded on the platform of the current station

[0129] If the current station is a station with large passenger flow, S HPF , then for large passenger flow, station S HPF Perform the following operations:

[0130] Calculate the original section capacity of the previous section and the historical section passenger flow of the previous section to obtain the residual section capacity of the previous section

[0131] Calculate the difference between the original number of people queuing at the current station and the original number of people stranded at the current station, multiply it by the passenger flow expansion factor and the number of trains, and get the demand section passenger flow of the previous interval.

[0132] Set the actual increment of the previous section to 0

[0133] If the demand section passenger flow of the previous interval is less than or equal to the remaining section capacity of the previous interval Then perform the following operations:

[0134] Assign the sum of the historical section passenger flow of the previous interval and the demand section passenger flow of the previous interval to the deduced section passenger flow of the previous interval

[0135] Assign the demand section passenger flow of the previous interval to the actual section increment of the previous interval

[0136] If the demand section passenger flow of the previous interval is greater than the remaining section capacity of the previous interval Then perform the following operations:

[0137] Assign the original cross-section capacity of the previous interval to the deduced cross-section passenger flow of the previous interval

[0138] Assign the remaining section capacity of the previous interval to the actual section increment of the previous interval

[0139] If the actual increment of the section in the previous interval is greater than 0 Then, the increased section passenger flow should be added to the subsequent intervals, and the loop (foreach) is for the interval collection {sectionname} i In the subsequent interval after_sec, find the station S with large passenger flow in matrix A. HPF Contribution rate to the subsequent interval after_sec The product of the actual section increment of the subsequent interval and the contribution rate of the subsequent interval is taken as the theoretical new passenger flow of the subsequent interval.

[0140] If the remaining cross-sectional capacity of the subsequent section is greater than or equal to the theoretical additional passenger flow of the subsequent section Then perform the following operations:

[0141] The sum of the original cross-sectional capacity of the subsequent interval and the theoretical additional passenger flow of the subsequent interval is assigned to the deduced cross-sectional capacity of the subsequent interval.

[0142] Assign the original platform queue number of the subsequent station to the deduced platform queue number of the subsequent station

[0143] Assign the original number of people stranded on the platform of the subsequent station to the deduced number of people stranded on the platform of the subsequent station

[0144] If the remaining cross-sectional capacity of the subsequent section is less than the theoretical additional passenger flow of the subsequent section The subsequent sections are fully loaded, resulting in the subsequent stations S HPF +nIf the person who was originally scheduled to board the bus cannot board, perform the following operations:

[0145] Assign the remaining cross-sectional capacity of the subsequent interval to the deduced cross-sectional passenger flow of the subsequent interval

[0146] Based on the historical cross-sectional passenger flow of the subsequent intervals, the theoretical new passenger flow of the current interval is added and the remaining cross-sectional capacity of the subsequent intervals is subtracted to obtain the cross-sectional flow difference of the subsequent intervals.

[0147] Calculate the ratio between the cross-sectional flow difference of the subsequent interval and the number of trains to obtain the average train flow difference of the subsequent interval.

[0148] The sum of the original platform queue number in the subsequent interval and the average difference between the vehicles in the subsequent interval is assigned to the deduced platform queue number in the subsequent interval.

[0149] The sum of the original number of people stranded on the platform in the subsequent interval and the average difference between the vehicles in the subsequent interval is assigned to the deduced number of people stranded on the platform in the subsequent interval.

[0150] Traverse the set of subsequent intervals after_sec {sectionname} more, traverse the contribution rate of each interval in the set The difference between the historical section passenger flow of the subsequent interval and the product of the average difference between the subsequent interval and the contribution rate of the subsequent interval is assigned to the historical section passenger flow of the subsequent interval.

[0151] After that, the difference between the deduced number of people stranded on the platform at the current station and the original number of people stranded on the platform at the current station is used to obtain the intermediate number of people stranded on the platform. And, the difference between the deduced platform queue number of the current station and the original platform queue number of the current station is used to obtain the intermediate number of platform queue numbers.

[0152] The deduction table B in this round of iterative deduction i The median number of people stranded at the platform for each row of data and the deduction table B in the next round of iterative deduction i+1 The sum of the original number of people stranded on the platform of the current station is updated to the deduction table B in the next round of iterative deduction i+1 The original number of people stranded on the platform at the current station

[0153] The deduction table B in this round of iterative deduction i The median number of people queuing at each platform in each row of data and the deduction table B in the next round of iterative deduction i+1 The original number of people queuing on the platform at the current station The sum of the values between them is updated to the deduction table B in the next round of iterative deduction i+1 The original number of people queuing on the platform at the current station

[0154] When the iterative deduction is completed, the deduction table B is output i Deduced cross-sectional passenger flow in each section

[0155] Under normal operation, when transport resources and passenger demand remain stable, time-series forecasts based on historical data are typically used to meet daily passenger flow forecasting needs and basic transportation requirements. However, in special scenarios, such as changes in transport resource allocation or sudden surges in passenger flow at a single point, conventional passenger flow forecasting models lack historical data input under the same conditions, making it difficult to meet the forecasting requirements of machine learning and regression forecasting methods. In these scenarios, the problem is typically addressed by leveraging transport organization and the mechanisms of passenger flow fluctuations.

[0156] This embodiment intends to study the macro-simulation and deduction method of line operation status under special scenarios based on cross-sectional passenger flow, inbound and outbound passenger flow, and platform passenger flow monitoring data without relying on OD data.

[0157] Urban rail transit is vigorously developing digital twin technology, placing higher demands on line operation simulation. Simulating passenger flow trends for specific operational scenarios, either offline or in real time, is a new goal and demand for operational management. Passenger flow trend simulation differs from conventional passenger flow forecasting in its application scenarios and focus. Passenger flow trend simulation primarily involves predicting changes in passenger flow distribution over a period of time by analyzing and simulating passenger movement trends under specific circumstances (such as emergencies or foreseeable abnormal events). This type of simulation often considers complex external factors, such as the nature, time, and location of the event, and their impact on passenger behavior and flow distribution. Therefore, passenger flow trend simulation emphasizes the dynamic changes and specific responses to specific events. Conventional passenger flow forecasting, on the other hand, focuses more on predicting passenger flow at various temporal and spatial granularities within a future time period under normal conditions, based on historical data and statistical analysis. This prediction method typically utilizes various statistical and machine learning techniques to predict spatiotemporal passenger flow based on historical passenger flow data, date, and time.

[0158] This embodiment is based on the actual needs of on-site dispatching and command. In the context of unavailable passenger flow OD data for this route, a passenger flow situation deduction model for a single route is studied. The deduction results are cross-sectional passenger flow and cross-sectional load factor, which can be applied to the following two scenarios:

[0159] (1) Capacity resource change scenarios: Train operation adjustment measures such as route changes, section speed limits, interval adjustments, adding and subtracting trains, and empty train direct runs will involve changes in the transport capacity of different sections, affecting the waiting and detention of passengers at the platform and the spatiotemporal matching of transport capacity and volume of line sections. Such changes can be predicted through deduction models.

[0160] (2) Real-time single-point high passenger flow scenario: When a sudden high passenger flow occurs at a single station on the line, and the transport resources are unable to respond in time, the high passenger flow will occupy the capacity of the train and affect the stations and sections in the subsequent running direction. It is necessary to deduce the changes in cross-section passenger flow.

[0161] In this embodiment, a single line to be simulated is determined on the rail transit; the line includes multiple stations and sections between two adjacent stations; real-time or historical passenger flow parameters generated on the line are set for each station and each section; the contribution rate of the new passenger flow from each station to the passenger flow of each section is calculated based on the passenger flow parameters; in simulation scenarios with changes in transportation capacity and / or passenger flow, the passenger flow of each station and each section is iteratively deduced based on the passenger flow parameters and / or contribution rate. This embodiment does not rely on OD data and can perform passenger flow simulation on a single line in rail transit in a timely manner, effectively improving the real-time performance of passenger flow simulation.

[0162] Example 2

[0163] See also Figure 3 , shows a schematic structural diagram of a rail transit single-line passenger flow simulation device provided by the second embodiment of the present invention. Figure 3 As shown, the device includes:

[0164] The route determination module 301 is used to determine a single route to be simulated in rail transit; the route includes multiple stations and sections between two adjacent stations;

[0165] A passenger flow parameter setting module 302 is used to set real-time or historical passenger flow parameters generated on the line for each of the stations and each of the sections;

[0166] A contribution rate calculation module 303 is used to calculate the contribution rate of the passenger flow added to each station to each section according to the passenger flow parameters;

[0167] The scenario passenger flow simulation module 304 is used to iteratively deduce the passenger flow of each of the stations and each of the sections based on the passenger flow parameters and / or the contribution rate in a simulation scenario of capacity changes and / or passenger flow changes.

[0168] In one embodiment of the present invention, the passenger flow parameters include the new passenger flow at the current station and the movement probability; the movement probability represents the proportion of passenger flow history from the current station to the subsequent section; the contribution rate calculation module 303 includes:

[0169] A transfer passenger flow calculation module is used to sum the products of the newly added passenger flow at the current station and each of the movement probabilities to obtain the transfer passenger flow of the subsequent interval;

[0170] The interval contribution calculation module is used to divide the difference between the new passenger flow at the current station and the transferred passenger flow in the subsequent interval by the new passenger flow at the current station as the contribution rate of the new passenger flow at the current station to the passenger flow in the subsequent interval.

[0171] In an embodiment of the present invention, the scenario passenger flow simulation module 304 includes:

[0172] An initialization module, which, at the initial stage, if the deduced cross-section passenger flow of the previous interval is not 0, assigns the historical cross-section passenger flow of the previous interval to the deduced cross-section passenger flow of the previous interval;

[0173] A first transport capacity improvement simulation module, which, in the simulation scenario of transport capacity improvement, if the historical cross-section passenger flow of the previous interval is less than the product of the original cross-section capacity of the previous interval and the restricted full-load rate, and the deduced platform retention number of the current station is 0, assigns the historical cross-section passenger flow of the previous interval to the deduced cross-section passenger flow of the previous interval, assigns the quotient obtained by dividing the original platform retention number of the current station by the ratio between the simulated cross-section capacity of the previous interval and the original cross-section capacity of the previous interval to the deduced platform queuing number of the current station, and sets the deduced platform retention number of the current station to 0;

[0174] A second transport capacity improvement simulation module, which, in the simulation scenario of transport capacity improvement, if the product of the simulated cross-section capacity of the previous interval and the restricted full-load rate is greater than or equal to the sum of the historical cross-section passenger flow of the previous interval and the original station retention number of the current station, assigns the sum of the historical cross-section passenger flow of the previous interval and the original station retention number of the current station to the deduced cross-section passenger flow of the previous interval, sets the deduced platform retention number of the current station to 0, and assigns the difference between the original platform queuing number of the current station and the original platform retention number of the current station to the deduced platform queuing number of the current station;

[0175] A third transport capacity improvement simulation module, which, if the product of the simulated cross-section capacity of the previous interval and the restricted full-load rate is less than the sum of the historical cross-section passenger flow of the previous interval and the original station retention number of the current station, assigns the product of the simulated cross-section capacity of the previous interval and the restricted full-load rate to the deduced cross-section passenger flow of the previous interval, assigns the product of the original platform queuing number of the current station and the cross-section passenger flow change coefficient of the previous interval to the deduced platform queuing number of the current station, and assigns the product of the original platform retention number of the current station and the cross-section passenger flow change coefficient of the previous interval to the deduced platform retention number of the current station;

[0176] Among them, the cross-sectional passenger flow variation coefficient of the previously mentioned interval is the absolute value of the difference between 1 and the cross-sectional passenger flow deviation rate of the previously mentioned interval; the cross-sectional passenger flow deviation rate of the previously mentioned interval is the difference between the deduced cross-sectional passenger flow of the previously mentioned interval and the historical cross-sectional passenger flow of the previously mentioned interval, divided by the quotient obtained by the historical cross-sectional passenger flow of the previously mentioned interval.

[0177] In one embodiment of the present invention, the scene passenger flow simulation module 304 includes:

[0178] A first capacity reduction simulation module is configured to, in a capacity reduction simulation scenario, calculate, if the deduced cross-sectional passenger flow of the previous interval is greater than the historical cross-sectional passenger flow of the previous interval, a difference between the deduced cross-sectional passenger flow of the previous interval and the historical cross-sectional passenger flow of the previous interval as a cross-sectional passenger flow change value of the previous interval;

[0179] The second capacity reduction simulation module is used to assign the sum of the historical cross-sectional passenger flow of the subsequent interval and the product obtained by multiplying the cross-sectional deviation value of the subsequent interval by the contribution rate of the subsequent interval to the deduced cross-sectional passenger flow of the subsequent interval.

[0180] In one embodiment of the present invention, the scene passenger flow simulation module 304 includes:

[0181] The third capacity reduction simulation module is configured to, in a capacity reduction simulation scenario, assign the historical cross-sectional passenger flow of the previous interval to the deduced cross-sectional passenger flow of the previous interval, divide the original platform stranded population of the current station by the ratio of the simulated cross-sectional capacity of the previous interval to the original cross-sectional capacity of the previous interval, and assign the quotient obtained by dividing the original platform stranded population of the current station by the ratio of the simulated cross-sectional capacity of the previous interval to the original cross-sectional capacity of the previous interval to the deduced platform queue population of the current station, and set the deduced platform stranded population of the current station to 0;

[0182] a fourth capacity reduction simulation module, configured to assign, in a capacity reduction simulation scenario, a product of the simulated cross-sectional capacity of the aforementioned section and the limited full load factor to the deduced cross-sectional passenger flow of the aforementioned section;

[0183] a fifth capacity reduction simulation module, configured to assign the sum of the deduced platform queue number at the current station and the change in sectional capacity in the previous interval to the deduced platform average queue number at the current station, and to assign the sum of the deduced platform queue number at the current station and the change in sectional capacity in the previous interval to the deduced platform queue number at the current station, if the original number of people stranded on the platform at the current station is greater than 0;

[0184] Among them, the change amount of the cross-section capacity of the previous interval is the difference obtained by multiplying the simulated cross-section capacity of the previous interval by the restricted full-load rate and then subtracting the historical cross-section passenger flow of the previous interval, and then taking the absolute value and the specified ratio in sequence;

[0185] The sixth transport capacity reduction simulation module is used to, if the original platform retention number of the current station is less than or equal to 0, divide the original platform retention number of the current station by the ratio between the simulated cross-section capacity of the previous interval and the original cross-section capacity of the previous interval, and assign the quotient obtained to the deduced platform queuing number of the current station, and multiply the absolute value of the cross-section passenger flow deviation rate of the previous interval by the deduced platform queuing number of the current station, and assign the product to the deduced platform retention number of the current station;

[0186] Among them, the cross-section passenger flow deviation rate of the previous interval is the quotient obtained by dividing the difference between the deduced cross-section passenger flow of the previous interval and the historical cross-section passenger flow of the previous interval by the historical cross-section passenger flow of the previous interval;

[0187] The seventh transport capacity reduction simulation module is used to, if the deduced cross-section passenger flow of the previous interval is less than the historical cross-section passenger flow of the previous interval, calculate the difference between the deduced cross-section passenger flow of the previous interval and the historical cross-section passenger flow of the previous interval as the cross-section passenger flow change value of the previous interval;

[0188] The eighth transport capacity reduction simulation module is used to assign the sum value between the historical cross-section passenger flow of the subsequent interval and the product obtained by multiplying the cross-section deviation value of the subsequent interval by the contribution rate of the subsequent interval to the deduced cross-section passenger flow of the subsequent interval.

[0189] In an embodiment of the present invention, the scenario passenger flow simulation module 304 further includes:

[0190] The first intermediate quantity calculation module is used to obtain the intermediate quantity of the platform retention number by taking the difference between the deduced platform retention number of the current station and the original platform retention number of the current station;

[0191] The first platform retention number update module is used to update the sum value between the intermediate quantity of the platform retention number in the current round of iterative deduction and the original platform retention number of the current station in the next round of iterative deduction to the original platform retention number of the current station in the next round of iterative deduction;

[0192] The first deduced cross-section passenger flow output module is used to output the deduced cross-section passenger flow of each interval when the iterative deduction is completed.

[0193] In an embodiment of the present invention, the scenario passenger flow simulation module 304 includes:

[0194] The non-large passenger flow simulation module is used to assign the historical cross-sectional passenger flow of the previous interval to the deduced cross-sectional passenger flow of the previous interval, assign the original platform queuing number of the current station to the deduced platform queuing number of the current station, and assign the original platform staying number of the current station to the deduced platform staying number of the current station if the current station is not a large passenger flow application station;

[0195] The first large passenger flow simulation module is used to calculate the original cross-sectional capacity of the previous interval and the historical cross-sectional passenger flow of the previous interval to obtain the remaining cross-sectional capacity of the previous interval, calculate the product of the difference between the original platform queuing number of the current station minus the original platform staying number of the current station, the passenger flow amplification factor, and the number of trains to obtain the required cross-sectional passenger flow of the previous interval, and set the actual cross-sectional increment of the previous interval to 0 if the current station is a large passenger flow application station;

[0196] The second large passenger flow simulation module is used to assign the sum value between the historical cross-sectional passenger flow of the previous interval and the required cross-sectional passenger flow of the previous interval to the deduced cross-sectional passenger flow of the previous interval, and assign the required cross-sectional passenger flow of the previous interval to the actual cross-sectional increment of the previous interval if the required cross-sectional passenger flow of the previous interval is less than or equal to the remaining cross-sectional capacity of the previous interval;

[0197] The third large passenger flow simulation module is used to assign the original cross-sectional capacity of the previous interval to the deduced cross-sectional passenger flow of the previous interval, and assign the remaining cross-sectional capacity of the previous interval to the actual cross-sectional increment of the previous interval if the required cross-sectional passenger flow of the previous interval is greater than the remaining cross-sectional capacity of the previous interval;

[0198] The fourth large passenger flow simulation module is used to take the product of the actual cross-sectional increment of the subsequent interval and the contribution rate of the subsequent interval as the theoretical new passenger flow of the subsequent interval if the actual cross-sectional increment of the previous interval is greater than 0;

[0199] The fifth large passenger flow simulation module is used to assign the sum value between the original cross-sectional capacity of the subsequent interval and the theoretical new passenger flow of the subsequent interval to the deduced cross-sectional capacity of the subsequent interval, assign the original platform queuing number of the subsequent station to the deduced platform queuing number of the subsequent station, and assign the original platform staying number of the subsequent station to the deduced platform staying number of the subsequent station if the remaining cross-sectional capacity of the subsequent interval is greater than or equal to the theoretical new passenger flow of the subsequent interval;

[0200] The sixth passenger flow simulation module is used to assign the remaining sectional capacity of the subsequent interval to the deduced sectional passenger flow of the subsequent interval if the remaining sectional capacity of the subsequent interval is less than the theoretical new passenger flow of the subsequent interval, add the theoretical new passenger flow of the current interval to the historical sectional passenger flow of the subsequent interval, and subtract the remaining sectional capacity of the subsequent interval to obtain the sectional flow difference of the subsequent interval, calculate the ratio of the sectional flow difference of the subsequent interval to the number of trains, and obtain the average vehicle difference of the subsequent interval, assign the sum of the original number of people queuing on the platform of the subsequent interval and the average vehicle difference of the subsequent interval to the deduced number of people queuing on the platform of the subsequent interval, and assign the sum of the original number of people stranded on the platform of the subsequent interval and the average vehicle difference of the subsequent interval to the deduced number of people stranded on the platform of the subsequent interval;

[0201] The seventh passenger flow simulation module is used to assign the difference between the historical cross-sectional passenger flow of the subsequent interval and the product obtained by multiplying the average vehicle difference in the subsequent interval by the contribution rate of the subsequent interval to the historical cross-sectional passenger flow of the subsequent interval.

[0202] In one embodiment of the present invention, the scene passenger flow simulation module 304 further includes:

[0203] A second intermediate quantity calculation module is configured to obtain an intermediate quantity of the number of people stranded on the platform by calculating the difference between the deduced number of people stranded on the platform at the current station and the original number of people stranded on the platform at the current station;

[0204] A third intermediate quantity calculation module is configured to obtain an intermediate quantity of platform queue numbers by calculating the difference between the deduced platform queue number of the current station and the original platform queue number of the current station;

[0205] A second platform stranded population updating module is configured to update the sum of the intermediate number of platform stranded population in the current iterative deduction and the original platform stranded population of the current station in the next iterative deduction to the original platform stranded population of the current station in the next iterative deduction;

[0206] A platform queue number updating module is used to update the sum of the intermediate number of platform queues in the current round of iterative deduction and the original number of platform queues at the current station in the next round of iterative deduction to the original number of platform queues at the current station in the next round of iterative deduction;

[0207] The second deduced section passenger flow output module is used to output the deduced section passenger flow of each of the intervals when the iterative deduction is completed.

[0208] The simulation device for single-line passenger flow of rail transit provided by the embodiments of the present invention can execute the simulation method for single-line passenger flow of rail transit provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the simulation method for single-line passenger flow of rail transit.

[0209] Embodiment III

[0210] See Figure 4 , which shows a schematic structural diagram of an electronic device provided by an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as, for example, laptop computers, desktop computers, workstations, personal digital assistants, blade servers, mainframe computers, and other suitable computers. The components shown herein, their connections and relationships, and their functions are merely exemplary and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0211] As Figure 4 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. Among them, the memory stores a computer program executable by at least one processor. The processor 11 can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0212] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0213] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the simulation method for single-line passenger flow of rail transit.

[0214] In some embodiments, the simulation method of the single-track passenger flow of rail transit can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the above-described simulation method of the single-track passenger flow of rail transit can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the simulation method of the single-track passenger flow of rail transit in any other suitable manner (e.g., by means of firmware).

[0215] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs, which can be executed and / or interpreted on a programmable system including at least one programmable processor, the programmable processor can be a dedicated or general-purpose programmable processor, can receive data and instructions from a storage system, at least one input device, and at least one output device, and can transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0216] The computer program for implementing the method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a dedicated computer, or other programmable data processing devices, such that when the computer programs are executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0217] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0218] In order to provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0219] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0220] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The relationship between the client and the server is generated by computer programs that run on respective computers and have a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0221] Embodiment 4

[0222] The embodiment of the present invention also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the simulation method of single-track passenger flow of rail transit provided in any embodiment of the present invention.

[0223] In the process of implementing the computer program product, the computer program code for performing the operations of the present invention can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network - including a local area network (LAN) or a wide area network (WAN) - or can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0224] It should be understood that the various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0225] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A simulation method for single-line passenger flow of rail transit, characterized in that, Including: Determine a single line to be simulated on rail transit; the line includes multiple stations and sections between adjacent stations; Set passenger flow parameters generated on the line in real time or historically for each of the stations and each of the sections; Calculate the contribution rate of the newly added passenger flow at each station to the passenger flow in each section based on the passenger flow parameters; In a simulation scenario of capacity change and / or passenger flow change, iteratively deduce the passenger flow at each station and each section based on the passenger flow parameters and / or the contribution rate; 2. The method according to claim 1, wherein The passenger flow parameters include the newly added passenger flow at the current station and the moving probability; the moving probability represents the proportion of the passenger flow history from the current station to the subsequent section; The calculating the contribution rate of the newly added passenger flow at each station to the passenger flow in each section based on the passenger flow parameters includes: Sum the products of the newly added passenger flow at the current station and each of the moving probabilities to obtain the transferred passenger flow in the subsequent section; Divide the difference between the newly added passenger flow at the current station and the transferred passenger flow in the subsequent section by the newly added passenger flow at the current station as the contribution rate of the newly added passenger flow at the current station to the passenger flow in the subsequent section; 3. The method according to claim 1, wherein The iteratively deducing the passenger flow at each station and each section based on the passenger flow parameters and / or the contribution rate in a simulation scenario of capacity change and / or passenger flow change includes: Initially, if the deduced cross-section passenger flow of the previous section is not 0, then assign the historical cross-section passenger flow of the previous section to the deduced cross-section passenger flow of the previous section; In a simulation scenario of capacity improvement, if the historical cross-section passenger flow of the previous section is less than the product of the original cross-section capacity of the previous section and the restricted full-load rate, and the deduced platform retention number of the current station is 0, then assign the historical cross-section passenger flow of the previous section to the deduced cross-section passenger flow of the previous section, divide the original platform retention number of the current station by the ratio of the simulated cross-section capacity of the previous section to the original cross-section capacity of the previous section to obtain the quotient and assign it to the deduced platform queuing number of the current station, and set the deduced platform retention number of the current station to 0; In a simulation scenario of capacity improvement, if the product of the simulated cross-section capacity of the previous section and the restricted full-load rate is greater than or equal to the sum of the historical cross-section passenger flow of the previous section and the original station retention number of the current station, then assign the sum of the historical cross-section passenger flow of the previous section and the original station retention number of the current station to the deduced cross-section passenger flow of the previous section, set the deduced platform retention number of the current station to 0, and assign the difference between the original platform queuing number and the original platform retention number of the current station to the deduced platform queuing number of the current station; If the product of the simulated section capacity and the restricted full load rate of the previous interval is less than the sum of the historical section passenger flow of the previous interval and the original number of people stranded at the current station, then the product of the simulated section capacity and the restricted full load rate of the previous interval is assigned to the deduced section passenger flow of the previous interval, the product of the original number of people queuing on the platform of the current station and the section passenger flow variation coefficient of the previous interval is assigned to the deduced number of people queuing on the platform of the current station, and the product of the original number of people stranded on the platform of the current station and the section passenger flow variation coefficient of the previous interval is assigned to the deduced number of people stranded on the platform of the current station; Among them, the cross-sectional passenger flow variation coefficient of the previously mentioned interval is the absolute value of the difference between 1 and the cross-sectional passenger flow deviation rate of the previously mentioned interval; the cross-sectional passenger flow deviation rate of the previously mentioned interval is the difference between the deduced cross-sectional passenger flow of the previously mentioned interval and the historical cross-sectional passenger flow of the previously mentioned interval, divided by the quotient obtained by the historical cross-sectional passenger flow of the previously mentioned interval.

4. The method according to claim 1, wherein In the simulation scenario of capacity change and / or passenger flow change, iteratively deducing the passenger flow of each station and each section according to the passenger flow parameter and / or the contribution rate includes: In the simulation scenario of reduced transport capacity, if the deduced cross-sectional passenger flow of the previous interval is greater than the historical cross-sectional passenger flow of the previous interval, the difference between the deduced cross-sectional passenger flow of the previous interval and the historical cross-sectional passenger flow of the previous interval is calculated as the cross-sectional passenger flow change value of the previous interval; The sum of the historical cross-sectional passenger flow of the subsequent interval and the product of the cross-sectional deviation value of the subsequent interval and the contribution rate of the subsequent interval is assigned to the deduced cross-sectional passenger flow of the subsequent interval.

5. The method according to claim 1, wherein In the simulation scenario of capacity change and / or passenger flow change, iteratively deducing the passenger flow of each station and each section according to the passenger flow parameter and / or the contribution rate includes: In the simulation scenario of reduced transport capacity, if the historical cross-sectional passenger flow of the previous interval is less than the product of the original cross-sectional capacity of the previous interval and the restricted full load factor, and the inferred number of people stranded on the platform at the current station is equal to 0, then the historical cross-sectional passenger flow of the previous interval is assigned to the inferred cross-sectional passenger flow of the previous interval, the quotient obtained by dividing the original number of people stranded on the platform at the current station by the ratio of the simulated cross-sectional capacity of the previous interval to the original cross-sectional capacity of the previous interval is assigned to the inferred number of people queuing on the platform at the current station, and the inferred number of people stranded on the platform at the current station is set to 0; In the simulation scenario of reduced transport capacity, the product of the simulated cross-sectional capacity of the aforementioned section and the restricted full load factor is assigned to the deduced cross-sectional passenger flow of the aforementioned section; If the original platform queuing number of the current station is greater than 0, then assign the sum value between the deduced platform queuing number of the current station and the cross-section capacity change of the previous interval to the deduced average platform queuing number of the current station, and assign the sum value between the deduced platform queuing number of the current station and the cross-section capacity change of the previous interval to the deduced platform queuing number with queuing people of the current station; Among them, the cross-section capacity change of the previous interval is the ratio obtained by taking the absolute value of the difference between the product of the simulated cross-section capacity and the restricted full load rate of the previous interval minus the historical cross-section passenger flow of the previous interval in turn and a specified ratio; If the original platform queuing number of the current station is less than or equal to 0, then assign the quotient obtained by dividing the original platform queuing number of the current station by the ratio between the simulated cross-section capacity of the previous interval and the original cross-section capacity of the previous interval to the deduced platform queuing number of the current station, and assign the product between the absolute value of the cross-section passenger flow deviation rate of the previous interval and the deduced platform queuing number of the current station to the deduced platform queuing number with queuing people of the current station; Among them, the cross-section passenger flow deviation rate of the previous interval is the quotient obtained by dividing the difference between the deduced cross-section passenger flow and the historical cross-section passenger flow of the previous interval by the historical cross-section passenger flow of the previous interval; If the deduced cross-section passenger flow of the previous interval is less than the historical cross-section passenger flow of the previous interval, then calculate the difference between the deduced cross-section passenger flow and the historical cross-section passenger flow of the previous interval as the cross-section passenger flow change value of the previous interval; Assign the sum value between the historical cross-section passenger flow of the subsequent interval and the product obtained by multiplying the cross-section deviation value of the subsequent interval by the contribution rate of the subsequent interval to the deduced cross-section passenger flow of the subsequent interval.

6. The method according to any one of claims 3 to 5, characterized in that In the simulation scenario of transport capacity change and / or passenger flow change, the iterative deduction of the passenger flow of each station and each interval according to the passenger flow parameters and / or the contribution rate further includes: Obtain the intermediate quantity of the platform queuing number with queuing people by taking the difference between the deduced platform queuing number with queuing people of the current station and the original platform queuing number with queuing people of the current station; Update the sum value between the intermediate quantity of the platform queuing number with queuing people in this round of iterative deduction and the original platform queuing number with queuing people of the current station in the next round of iterative deduction to the original platform queuing number with queuing people of the current station in the next round of iterative deduction; When the iterative deduction is completed, output the deduced cross-section passenger flow of each interval.

7. The method according to claim 1, characterized in that, In the simulation scenario of transport capacity change and / or passenger flow change, the iterative deduction of the passenger flow of each station and each interval according to the passenger flow parameters and / or the contribution rate includes: If the current station is not a station with large passenger flow imposed, then assign the historical cross-section passenger flow of the previous interval to the deduced cross-section passenger flow of the previous interval, assign the original platform queuing number of the current station to the deduced platform queuing number of the current station, and assign the original platform queuing number with queuing people of the current station to the deduced platform queuing number with queuing people of the current station; If the current station is a high passenger flow station, calculate the original cross-sectional capacity of the previous section and the historical cross-sectional passenger flow of the previous section to obtain the residual cross-sectional capacity of the previous section. Calculate the difference between the original number of people queuing on the current station and the original number of people stranded on the current platform, multiply this by the passenger flow expansion factor and the number of trains to obtain the required cross-sectional passenger flow of the previous section. Set the actual cross-sectional passenger flow increment of the previous section to 0. If the demand section passenger flow of the previous interval is less than or equal to the remaining section capacity of the previous interval, the sum of the historical section passenger flow of the previous interval and the demand section passenger flow of the previous interval is assigned to the deduced section passenger flow of the previous interval, and the demand section passenger flow of the previous interval is assigned to the actual section increment of the previous interval; If the demand section passenger flow of the previous interval is greater than the residual section capacity of the previous interval, the original section capacity of the previous interval is assigned to the deduced section passenger flow of the previous interval, and the residual section capacity of the previous interval is assigned to the actual section increment of the previous interval; If the actual increase in the section of the previous section is greater than 0, the product of the actual increase in the section of the subsequent section and the contribution rate of the subsequent section is used as the theoretical additional passenger flow of the subsequent section; If the remaining section capacity of the subsequent section is greater than or equal to the theoretical additional passenger flow of the subsequent section, the sum of the original section capacity of the subsequent section and the theoretical additional passenger flow of the subsequent section is assigned to the deduced section capacity of the subsequent section, the original number of people queuing on the platform of the subsequent station is assigned to the deduced number of people queuing on the platform of the subsequent station, and the original number of people stranded on the platform of the subsequent station is assigned to the deduced number of people stranded on the platform of the subsequent station; If the remaining sectional capacity of the subsequent interval is less than the theoretical new passenger flow of the subsequent interval, the remaining sectional capacity of the subsequent interval is assigned to the deduced sectional passenger flow of the subsequent interval, and the theoretical new passenger flow of the current interval is added to the historical sectional passenger flow of the subsequent interval, and the remaining sectional capacity of the subsequent interval is subtracted to obtain the sectional flow difference of the subsequent interval, and the ratio of the sectional flow difference of the subsequent interval to the number of trains is calculated to obtain the average vehicle difference of the subsequent interval, and the sum of the original number of people queuing on the platform of the subsequent interval and the average vehicle difference of the subsequent interval is assigned to the deduced number of people queuing on the platform of the subsequent interval, and the sum of the original number of people stranded on the platform of the subsequent interval and the average vehicle difference of the subsequent interval is assigned to the deduced number of people stranded on the platform of the subsequent interval; The difference between the historical section passenger flow of the subsequent interval and the product obtained by multiplying the average vehicle difference of the subsequent interval by the contribution rate of the subsequent interval is assigned to the historical section passenger flow of the subsequent interval.

8. The method according to claim 7, wherein In the simulation scenario of capacity change and / or passenger flow change, iteratively deducing the passenger flow of each station and each section according to the passenger flow parameters and / or the contribution rate further includes: The difference between the deduced number of people stranded on the platform of the current station and the original number of people stranded on the platform of the current station is used to obtain the intermediate number of people stranded on the platform; The difference between the deduced number of people queuing on the platform at the current station and the original number of people queuing on the platform at the current station is used to obtain the median number of people queuing on the platform; The sum of the intermediate number of people stranded on the platform in the current iterative deduction and the original number of people stranded on the platform at the current station in the next iterative deduction is updated to the original number of people stranded on the platform at the current station in the next iterative deduction; The sum of the median number of people queuing on the platform in the current round of iterative deduction and the original number of people queuing on the platform at the current station in the next round of iterative deduction is updated to the original number of people queuing on the platform at the current station in the next round of iterative deduction; When the iterative deduction is completed, the deduced cross-sectional passenger flow of each of the intervals is output.

9. An electronic device, characterized in that, The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for simulating rail transit single-line passenger flow according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for simulating rail transit single-line passenger flow according to any one of claims 1 to 8 is implemented.

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