A method for quantitatively evaluating influence of urban rail transit emergency on passenger flow

Abstract

The application provides a kind of urban rail burst event influence on passenger flow quantitative evaluation method.The method comprises: based on the multi-source data of urban rail transit, a multi-layer dependent network model of urban rail transit is constructed, a node and edge traffic propagation process is considered to construct an urban rail transit point-edge joint cascading failure model, the parameter calibration of the model is carried out using the historical failure data of urban rail, and a calibrated urban rail transit point-edge joint cascading failure model is obtained;the parameter information of urban rail burst event is input into the calibrated urban rail transit point-edge joint cascading failure model, and the calibrated urban rail transit point-edge joint cascading failure model outputs the influence of the burst event on urban rail transit passengers.The application is based on the physical law and real-time multi-source data of urban rail transit system, designs dynamic cascading failure rules, and accurately simulates the dynamic processes such as passenger flow transfer, station congestion propagation and multi-transportation mode connection obstruction under burst event.

Description

Technical Field

[0001] This invention relates to the field of urban rail transit passenger flow management technology, and in particular to a quantitative evaluation method for the impact of urban rail transit emergencies on passenger flow. Background Technology

[0002] Urban rail transit, as the backbone of public transportation in megacities, boasts strong convenience, timeliness, and accessibility. When accidents occur in urban rail transit systems, they can range from minor train delays and small-scale congestion to severe line shutdowns, widespread disruptions, substantial economic losses, and negative social impacts. Accidents often cause severe traffic congestion around the accident site and generate negative public opinion. The causes of various accidents in urban rail transit can be categorized into endogenous factors and external disturbances. The former refers to malfunctions in system components or poor coordination between components during the operation of the urban rail transit system itself, such as signal failures or scheduling errors. The latter refers to problems caused by various external factors acting on the urban rail transit system itself, such as extreme weather or large passenger surges. In reality, major emergencies are often caused by the combined effects of multiple factors. A deep understanding of the impact mechanisms of major emergencies in urban rail transit and the development of potential accident prevention plans are crucial for ensuring the resilient operation of urban rail transit systems.

[0003] Inferring the impact of an accident on a system often requires multifaceted information, including the accident itself, the physical laws governing the system, and the propagation patterns of disturbances within the system. The impact of an accident is the result of the combined effects of multiple factors. However, Bayesian networks can only handle static systems in equilibrium and cannot analyze dynamically evolving systems in reality. Bayesian networks rely on numerous assumptions and lack data support, and are difficult to handle small datasets. Some models oversimplify real-world traffic transfer processes or rely heavily on historical data, often leading to biased results. Therefore, it is essential to model urban rail transit networks based on refined modeling and incorporating actual physical laws.

[0004] The disadvantages of the methods for inferring the impact of accidents on the system in the prior art include:

[0005] Existing Bayesian networks rely on the assumption of static equilibrium, and can only analyze the steady-state impact after an accident, failing to characterize the dynamic evolution processes such as passenger flow transfer and station congestion during emergencies. Limited by small sample data, the models exhibit biases in predicting the probabilities of dynamic behaviors such as passenger route adjustments and evacuation disruptions, resulting in insufficient timeliness of emergency response.

[0006] Traditional cascading failure models abstract traffic distribution rules into uniform redistribution or rely on historical data fitting, ignoring real physical constraints such as inter-section capacity limitations and passenger transfer behavior in urban rail transit. This leads to distorted prediction of passenger congestion propagation paths and makes it impossible to accurately quantify passenger travel delays and path blockage risks. Summary of the Invention

[0007] The embodiments of the present invention provide a quantitative evaluation method for the impact of urban rail transit emergencies on passenger flow, so as to accurately quantify and assess the loss of passenger travel efficiency and secondary congestion risks caused by urban rail transit emergencies.

[0008] To achieve the above objectives, the present invention adopts the following technical solution.

[0009] A quantitative evaluation method for the impact of urban rail transit emergencies on passenger flow includes:

[0010] Collect multi-source data on urban rail transit, and construct a multi-layer interdependent network model of urban rail transit based on the multi-source data.

[0011] Based on the multi-layer interdependent network model of urban rail transit, considering the flow propagation process of nodes and edges, a point-edge joint cascade failure model of urban rail transit is constructed. The parameters of the point-edge joint cascade failure model of urban rail transit are calibrated using historical fault data of urban rail transit to obtain a calibrated point-edge joint cascade failure model of urban rail transit.

[0012] The parameter information of urban rail transit emergencies is input into a calibrated urban rail transit point-edge joint cascade failure model, and the calibrated urban rail transit point-edge joint cascade failure model outputs the impact of the emergencies on urban rail transit passengers.

[0013] Preferably, the collection of multi-source data on urban rail transit includes:

[0014] Collect multi-source data on urban rail transit, including historical fault data, urban rail transit network topology information, timetable information, route information and smart card swipe data for each line. The historical fault data includes all fault data for each line and station of the urban rail transit system, covering various fault types.

[0015] Preferably, the construction of a multi-layer interdependent network model for urban rail transit based on the multi-source data of urban rail transit includes:

[0016] The urban rail transit multi-layer interdependent network model includes: road infrastructure system, power infrastructure system, communication infrastructure system, and service operation system. The road infrastructure system includes the track, represented by the actual topology information of the urban rail transit system; the power infrastructure system includes the overhead contact line and overhead contact rail power supply network; and the service operation system includes the train operation system, represented by the actual timetable and route information.

[0017] Adopting a multi-layer interdependent network model for urban rail transit Describing urban rail transit systems Let v represent the set of all nodes in different layers of the network, ε represent the set of edges within and between layers of the network, and v = {V} α}, ε={E αβ}, and α,β∈{1,2,…,L}, where L is the total number of layers in the network. When α=β, E αα ∈V α ×V α E represents the set of edges inside layer α, when α ≠ β. αβ ∈V α ×V β This represents the set of edges connecting layer α and layer β. Furthermore, Where i,j∈{1,2,…,N} α}, N α The number of nodes representing layer α;

[0018] The urban rail transit multi-layer interdependent network model The adjacency matrix A is written as when This indicates that there is an edge connecting node i in layer α and node j in layer β; otherwise...

[0019] Preferably, the process of constructing a point-edge joint cascade failure model for urban rail transit based on the multi-layer dependent network model of urban rail transit, considering the flow propagation process of nodes and edges, and using historical fault data of urban rail transit to calibrate the parameters of the point-edge joint cascade failure model of urban rail transit, to obtain a calibrated point-edge joint cascade failure model of urban rail transit, includes:

[0020] Based on the aforementioned multi-layer interdependent network model of urban rail transit, considering the flow propagation process of nodes and edges, a joint cascade failure model of urban rail transit points and edges is constructed. The expressions of this joint cascade failure model of urban rail transit points and edges are shown in formulas (1)-(6):

[0021]

[0022] D(x)=δx#(3)

[0023]

[0024]

[0025] In this context, formulas (1)-(4) represent the node flow dissipation and propagation process, formulas (5)-(6) represent the edge flow propagation process, and n∈v represents the multi-layer dependent network represented by the adjacency matrix A. In the adjacency matrix A, the nodes e∈ε represent the multi-layer dependent network represented by the adjacency matrix A. In the edge, R n R e Let represent the set of edges connected to node n and the set of points connected to edge e, respectively. Let represent the overflow flow of node n and edge e at time step i, respectively. Let n and e represent the capacities of node n and edge e at time step i, respectively. Let represent the flow of node n and edge e at time step i, respectively. Let D(x) represent the flow rate of node n after dissipation at time step i, where D(x) is the flow dissipation function, δ is the flow decay coefficient, the initial node flow rate is derived from the inbound flow data, the node capacity is the average of its connected edge capacities, and the edge flow rate is derived from the stochastic user equilibrium model. The edge capacity is derived from the actual timetable data.

[0026] Preferably, the step of calibrating the parameters of the urban rail transit point-edge joint cascade failure model using historical fault data of urban rail transit to obtain a calibrated urban rail transit point-edge joint cascade failure model includes:

[0027] Historical fault data is input into the urban rail transit point-edge joint cascade failure model. The historical fault data records the actual value of the impact of disturbance on passenger flow. The urban rail transit point-edge joint cascade failure model outputs the simulated value of the impact of disturbance on passenger flow. By continuously adjusting the parameter δ in the urban rail transit point-edge joint cascade failure model, the simulated value is made closest to the actual value, and the urban rail transit point-edge joint cascade failure model is calibrated.

[0028] Preferably, the step of inputting parameter information of urban rail transit emergencies into a calibrated urban rail transit point-edge joint cascade failure model, and the calibrated urban rail transit point-edge joint cascade failure model outputting the impact of the emergencies on urban rail transit passengers, includes:

[0029] The network topology data, passenger smart card data, train timetable data, and historical fault data corresponding to sudden events in urban rail transit are used as input data. This input data is then fed into a calibrated urban rail transit point-edge joint cascaded failure model. The model processes the input data as follows: 1) Constructs a network based on the network topology data; 2) Allocates the traffic represented by passenger smart card data to various stations and lines according to a stochastic user equilibrium model; 3) Determines the capacity of each section of each line based on train timetable data; 4) Based on historical fault data, and using faults as input, performs cascaded failure simulation to obtain the impact of the sudden events on urban rail transit passengers.

[0030] As can be seen from the technical solutions provided by the embodiments of the present invention above, the present invention, based on the physical laws of urban rail transit systems and real-time multi-source data, designs dynamic cascading failure rules to accurately simulate dynamic processes such as passenger flow transfer, station congestion propagation, and multi-modal connection obstruction under sudden events. The present invention overcomes the limitations of static models by integrating historical fault data with simulation capabilities, effectively solving the problem of dependence on small sample data, and accurately quantifying and assessing passenger travel efficiency losses and secondary congestion risks.

[0031] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of the invention. Attached Figure Description

[0032] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Appendix Figure 1 A flowchart illustrating a method for quantitatively evaluating the impact of urban rail transit emergencies on passenger flow, provided in an embodiment of the present invention.

[0034] Appendix Figure 2 A schematic diagram of the Beijing urban rail transit network is provided for an embodiment of the present invention;

[0035] Appendix Figure 3 This invention provides a distribution of historical fault levels in Beijing's urban rail transit system.

[0036] Appendix Figure 4 This invention provides a time distribution of historical fault data for various levels of Beijing urban rail transit.

[0037] Appendix Figure 5 This invention provides a geographical distribution of the number of urban rail transit accidents in Beijing.

[0038] Appendix Figure 6 This invention provides simulation results for a 4C grouping system.

[0039] Appendix Figure 7 This invention provides simulation results for a 6B group configuration.

[0040] Appendix Figure 8 The simulation results provided in an 8A group configuration are provided for an embodiment of the present invention. Detailed Implementation

[0041] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0042] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or couplings. The term “and / or” as used herein includes any and all combinations of one or more of the associated listed items.

[0043] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as herein.

[0044] To facilitate understanding of the embodiments of the present invention, the following will provide further explanation and description with reference to the accompanying drawings and several specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.

[0045] This invention improves the cascading failure model by integrating real-time passenger flow data and multi-source disturbance information to simulate dynamic processes such as passenger route selection and station congestion propagation under sudden events, thus solving the problems of traditional models relying on static assumptions and small sample data. This invention designs a traffic redistribution algorithm based on interval saturation to optimize the quantification accuracy of the cascading failure model for passenger travel delays and route blockages, reducing the simplification bias of traditional models.

[0046] This invention provides a method for assessing the impact of sudden events on urban rail transit for passenger travel. Starting with multi-source data, it constructs a multi-layer interdependent network model of urban rail transit and calibrates the model parameters by injecting historical fault data, thereby assessing the impact of potential sudden events and providing theoretical guidance for the formulation of actual operation plans and emergency response plans.

[0047] The processing flow of a quantitative evaluation method for the impact of urban rail transit emergencies on passenger flow provided by this invention is as follows: Figure 1 As shown, the processing steps include the following:

[0048] Step S10: Collect multi-source data on urban rail transit.

[0049] This invention first collects multi-source data on urban rail transit, including historical fault data, urban rail transit network topology information, and timetable information, route information, and smart card swipe data for each line. The aforementioned historical fault data includes all fault data for each line and station in the urban rail transit system, ranging from minor issues like platform screen door malfunctions and people or objects getting caught in train doors, to major issues like substation fires and smoke, station flooding due to heavy rain, and foreign object intrusion into the clearance gauge. This provides a relatively comprehensive coverage of various fault types.

[0050] Step S20: Construct a multi-layer interdependent network model of urban rail transit based on the above multi-source data of urban rail transit.

[0051] Urban rail transit systems are the sum of multiple subsystems, which can be mainly divided into the following components based on their functional characteristics: road infrastructure systems (such as bridges, tunnels, and tracks), power infrastructure systems (such as substations and overhead contact lines), communication infrastructure systems (such as base stations, switches, and servers), service operation systems (such as trains, train control systems, and dispatching centers), and passenger components. Except for passengers, all other components within the entire system can be controlled and regulated by the operator, while passenger travel behavior is often not constrained by the operator. These systems are interconnected and influence each other, collectively forming a multi-layered interdependent network model for urban rail transit.

[0052] The systems considered in this invention include a road infrastructure system, a power infrastructure system, a service operation system, and passengers. These four systems are modeled as a multi-layered interdependent network model for urban rail transit. The road infrastructure system mainly refers to the tracks, which can be represented by the actual topology information of the urban rail transit system. The power infrastructure system mainly refers to the power supply network such as the overhead contact line and contact rail (excluding upstream facilities such as substations and power supply stations), therefore its topology is the same as the road infrastructure system. The service operation system mainly refers to the train operation system, which can be represented by actual timetables and route information. The passenger network is represented by actual smart card swipe information. These four systems have a strong coupling relationship. The transportation infrastructure network provides the basis for the existence of the overhead contact line; these two networks are static networks, jointly providing the basis for the existence of the train network. The existence of the train network, in turn, provides the basis for passenger travel. Both the train network and the passenger network are dynamically changing networks.

[0053] To address the shortcomings of single-layer networks in modeling urban rail transit systems, this invention employs a multi-layer dependent network model for urban rail transit. To characterize an urban rail transit system, v represents the set of all nodes in different layers of the network, and ε represents the set of edges within and between layers of the network. ε={E αβ}, and α,β∈{1,2,…,L}, where L is the total number of layers in the network. When α=β, E αα ∈V α ×V α E represents the set of edges inside layer α, when α ≠ β. αβ ∈V α ×V β This represents the set of edges connecting layers α and β. Furthermore, Where i,j∈{1,2,…,N} α}, N α The number of nodes representing layer α.

[0054] The above-mentioned multi-layer interdependent network model of urban rail transit The adjacency matrix A is written as when This indicates that there is an edge connecting node i in layer α and node j in layer β; otherwise...

[0055] Adjacency matrix A can be derived from a multi-layer dependency network. In the subsequent cascading failure model simulation process, each step requires obtaining the connection relationships between nodes from the adjacency matrix. Specifically, the flow and capacity on the edges / nodes calculated in the simulation rules represented by formulas (1)-(6) are all from the multi-layer interdependent network represented by adjacency matrix A. Capacity and flow of nodes and edges.

[0056] Step S30: Based on the above-mentioned multi-layer dependent network model of urban rail transit, considering the flow propagation process of nodes and edges, a point-edge joint cascade failure model of urban rail transit is constructed. The parameters of the point-edge joint cascade failure model of urban rail transit are calibrated using historical fault data of urban rail transit to obtain a calibrated point-edge joint cascade failure model of urban rail transit.

[0057] In the classic Motter-Lai model, nodes (point cascade failure model) or edges (edge ​​cascade failure model) in the network exist in only two states: normal or failed. When the flow in these components in the network does not exceed their own capacity, they are in a normal state; once the flow exceeds the capacity at some time step in the simulation, the component is considered to have completely collapsed and become a failed state, and the flow contained therein needs to be completely distributed to neighboring nodes.

[0058] Obviously, the classic model simplifies the model to consider a wider range of situations and cannot simulate the real state of the traffic network. This mainly includes the following three aspects: 1) When the flow of each component in the traffic network exceeds its capacity, it will not completely fail, but will gradually dissipate the congestion; 2) The classic model can only handle point failure or edge failure, and cannot handle the situation where both points and edges fail, but this situation exists in reality; 3) When performing initial failure, the traditional model often assumes that some components in the network have completely failed, but this situation is rarely seen in reality. Therefore, this invention proposes a point-edge joint cascade failure model for urban rail transit that considers flow dissipation. The expressions of this point-edge joint cascade failure model for urban rail transit are shown in formulas (1)-(6):

[0059]

[0060] D(x)=δx#(3)

[0061]

[0062] In this model, equations (1)-(4) represent the node flow dissipation and propagation process, and equations (5)-(6) represent the edge flow propagation process. Under this model, e∈ε, where e and e represent the multi-layer dependent network represented by the adjacency matrix A. The nodes and edges in R. n R e Let n and e represent the set of edges connected to node n and the set of points connected to edge e, respectively. These represent the overflow flow of node n and edge e at time step i, respectively. These represent the capacities of node n and edge e at time step i, respectively. These represent the flow rates of node n and edge e at time step i, respectively. This represents the flow rate of node n after dissipation at time step i. D(x) is the flow dissipation function, and δ is the flow rate decay coefficient. Furthermore, the initial node flow rate is derived from the inbound flow data, the node capacity is the average of its connected edge capacities, and the edge flow rate is derived from the stochastic user equilibrium model, i.e. The edge capacity is derived from the actual timetable data.

[0063] The urban rail transit point-edge joint cascade failure model represented by formulas (1)-(6) is a simulation model that mainly describes the flow rules of traffic in the network under sudden events. The final result is obtained through continuous iterative calculation. Therefore, this is not a solution to a certain equation in the general sense, but rather a simulation rule that is formulated to calculate the result.

[0064] After constructing the point-edge joint cascade failure model of urban rail transit, historical fault data is input to calibrate the parameters of the urban rail transit point-edge joint cascade failure model, resulting in a calibrated urban rail transit point-edge joint cascade failure model.

[0065] Historical fault data is used as the input to the disturbance of the urban rail transit point-edge joint cascade failure model. The historical fault data records the actual value of the disturbance's impact on passenger flow. The urban rail transit point-edge joint cascade failure model outputs the simulated value of the disturbance's impact on passenger flow. By continuously adjusting the parameter δ in the urban rail transit point-edge joint cascade failure model, the simulated value is made closest to the actual value, thereby calibrating the urban rail transit point-edge joint cascade failure model.

[0066] Step S40: Use the above-mentioned point-edge joint cascade failure model of rail transit to simulate the impact of sudden events on urban rail transit passengers.

[0067] The input data for the calibrated urban rail transit point-edge joint cascaded failure model includes: network topology data corresponding to urban rail transit emergencies, smart card data of passenger travel, train timetable data, and historical fault data. The process of processing the initial input data in the urban rail transit point-edge joint cascaded failure model includes: 1) constructing the network based on the network topology data; 2) allocating the traffic represented by the smart card data of passenger travel to various stations and lines according to the stochastic user equilibrium model; 3) determining the capacity of each section of each line based on the train timetable data; and 4) using historical fault data as input to perform cascaded failure simulation, thereby obtaining the results of the impact of emergencies on urban rail transit passengers.

[0068] By analyzing historical fault data and combining it with the actual physical characteristics of the urban rail transit system, different potential emergencies are set as model inputs to the network state of the urban rail transit during the corresponding time period. The simulation is carried out using a parameter-calibrated point-edge joint cascade failure model of urban rail transit to infer the possible impact of emergencies on passengers.

[0069] To verify the effectiveness of the invented method for assessing the impact of sudden events on urban rail transit oriented towards passenger travel, the Beijing urban rail transit system was selected as the research object. The scope of the research is as follows: Figure 2 As shown, the system includes 19 lines and 416 stations. The line operation data used in this example includes 75 different route data points for each line, 1,621 train timetable data points, and 6,574,583 AFC card swipe data points. The fault data used in this example includes all fault data for all lines and stations of the Beijing Urban Rail Transit System from January 1, 2021 to December 31, 2021, totaling 1,208 data points.

[0070] First, a feature analysis was performed on the historical accident or failure data, and the results are attached. Figure 3-5 As shown. From the perspective of hierarchical classification, attached Figure 3 The chart shows the frequency (bar chart) and percentage (pie chart) of accidents of each level occurring over a one-year period. It can be seen that Level 1 to 3 accidents account for over 90% of all accidents within a year, while Level 4 to 6 accidents, which have a slightly wider impact, account for less than 10%, and Level 6 accidents, which severely affect passenger travel, account for approximately 1.3%. This indicates that major emergencies in urban rail transit systems are characterized by a wide impact and low frequency; from the perspective of the temporal distribution of events, [the following text is missing]. Figure 4 The temporal distribution of accidents or malfunctions at different levels is presented. The results show that the temporal distribution of Level 1 and Level 2 accidents or malfunctions does not exhibit significant heterogeneity throughout the day, while the temporal distribution of Level 2 and above accidents shows significant heterogeneity, specifically, a higher frequency of occurrence during peak hours and a lower frequency or even no occurrence during off-peak hours, with a significantly higher frequency during the morning peak hours than the evening peak hours. From the perspective of location distribution, [the following text appears to be incomplete and requires further context: "attached..."] Figure 5 This shows the distribution of the number of accidents or faults occurring on different lines at different times, with darker colors indicating a higher frequency of occurrence.

[0071] Since each incident has a specific timeframe, the network at the time of the incident was selected as the model input during the simulation. The results are shown in the attached figure. Figure 6-8 As shown. (Attached) Figure 6-8The figures represent simulation results and real results for point-edge joint cascade failure considering traffic dissipation under different types of operating vehicles. In this invention, we assume the maximum capacity of a Type C car is 210 people, a Type B car is 240 people, and a Type A car is 310 people. Therefore, the train capacities corresponding to the 4C, 6B, and 8A train sets in the three figures are 840, 1440, and 2480 people, respectively. Different levels of accidents are represented by dots of different colors; the solid black line represents y = x, and the number of people affected by different levels of accidents is represented by dashed lines. The comparison between real and simulation results shows that when the train capacity is set less than the actual capacity (in the Beijing urban rail transit system, most lines use 6B train sets), the simulation model's inferences overestimate the impact of accidents (as shown in the attached figures). Figure 6 As shown in the attached diagram, when the train capacity is greater than the actual capacity, the simulation model's inferences will underestimate the impact of the accident (as shown in the attached diagram). Figure 8 (As shown). This is because the capacity of the train is related to the capacity of the section. The larger the capacity of the train, the larger the capacity of the section, and therefore the more flow it can handle when performing cascading failure simulation.

[0072] In summary, the embodiments of the present invention, through multi-layer dependent network modeling, integrate the coupling effect of system-endogenous faults and external disturbances, and combine historical fault data with simulation scenario modeling of cascading failures, overcome the limitations of small sample data and improve the quantitative accuracy of the number of passengers affected by sudden events.

[0073] This invention constructs a multi-layer interdependent network topology, defines cross-layer failure thresholds and traffic transfer rules, accurately simulates dynamic processes such as obstructed passenger flow at stations and broken transport capacity between sections, reveals the cascading failure path caused by external disturbances through the coupling of multiple network functions, and makes up for the simplified misjudgment of system cascading dynamics by traditional models.

[0074] This invention improves the cascading failure model, tracks the fault propagation path in real time, and predicts key congestion nodes, thereby shifting from passive emergency response to proactive prevention and control, and reducing the impact of system cascading failures on passenger travel.

[0075] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of one embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing the present invention.

[0076] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0077] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for apparatus or system embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The apparatus and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0078] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for quantitatively evaluating the influence of an urban rail transit emergency on passenger flow, characterized in that, The application relates to a method for predicting the influence of an urban rail transit emergency on passengers, and belongs to the field of urban rail transit emergency management. The method comprises the following steps: collecting urban rail transit multi-source data, constructing an urban rail transit multi-layer dependent network model based on the urban rail transit multi-source data, inputting parameter information of an urban rail transit emergency into a calibrated urban rail transit node-edge joint cascading failure model, and outputting the influence of the emergency on passengers of the urban rail transit by the calibrated urban rail transit node-edge joint cascading failure model. Based on the traffic propagation process of nodes and edges of the urban rail transit multi-layer dependent network model, an urban rail transit point-edge joint cascading failure model is constructed, and the historical failure data of urban rail transit are used to determine the parameters of the urban rail transit point-edge joint cascading failure model Calibration is performed to obtain a calibrated urban rail transit point-edge joint cascading failure model The method comprises the following steps: collecting urban rail transit multi-source data, constructing an urban rail transit multi-layer dependent network model based on the urban rail transit multi-source data, inputting parameter information of an urban rail transit emergency into a calibrated urban rail transit node-edge joint cascading failure model, and outputting the influence of the emergency on passengers of the urban rail transit by the calibrated urban rail transit node-edge joint cascading failure model. The method comprises the following steps: collecting urban rail transit multi-source data, constructing an urban rail transit multi-layer dependent network model based on the urban rail transit multi-source data, inputting parameter information of an urban rail transit emergency into a calibrated urban rail transit node-edge joint cascading failure model, and outputting the influence of the emergency on passengers of the urban rail transit by the calibrated urban rail transit node-edge joint cascading failure model. The method comprises the following steps: collecting urban rail transit multi-source data, constructing an urban rail transit multi-layer dependent network model based on the urban rail transit multi-source data, inputting parameter information of an urban rail transit emergency into a calibrated urban rail transit node-edge joint cascading failure model, and outputting the influence of the emergency on passengers of the urban rail transit by the calibrated urban rail transit node-edge joint cascading failure model. wherein formula (1)-(4) represent the node flow dissipation and propagation process, formula (5)-(6) represent the edge flow propagation process, , represents the adjacency matrix representing the multi-layer dependent network in which the node, , represents the adjacency matrix representing the multi-layer dependent network in which the edge, , respectively represent the set of edges connected to the node , the set of points connected to the edge , , respectively represent the overflow flow of the node and the edge at time step , , respectively represent the capacity of the node and the edge at time step , , respectively represent the flow of the node and the edge at time step , represents the flow of the node after dissipation at time step , is the flow dissipation function, is the flow decay coefficient, the initial node flow is obtained according to the inbound volume data, the point capacity is the average of the capacities of edges connected thereto, and the edge flow is obtained according to a stochastic user equilibrium model, i.e. , the edge capacity is obtained according to actual timetable data.

2. The method of claim 1, wherein, The method comprises the following steps: collecting urban rail transit multi-source data, constructing an urban rail transit multi-layer dependent network model based on the urban rail transit multi-source data, inputting parameter information of an urban rail transit emergency into a calibrated urban rail transit node-edge joint cascading failure model, and outputting the influence of the emergency on passengers of the urban rail transit by the calibrated urban rail transit node-edge joint cascading failure model. The method comprises the following steps: collecting urban rail transit multi-source data, constructing an urban rail transit multi-layer dependent network model based on the urban rail transit multi-source data, inputting parameter information of an urban rail transit emergency into a calibrated urban rail transit node-edge joint cascading failure model, and outputting the influence of the emergency on passengers of the urban rail transit by the calibrated urban rail transit node-edge joint cascading failure model.

3. The method of claim 2, wherein, The method comprises the following steps: collecting urban rail transit multi-source data, constructing an urban rail transit multi-layer dependent network model based on the urban rail transit multi-source data, inputting parameter information of an urban rail transit emergency into a calibrated urban rail transit node-edge joint cascading failure model, and outputting the influence of the emergency on passengers of the urban rail transit by the calibrated urban rail transit node-edge joint cascading failure model. The method comprises the following steps: collecting urban rail transit multi-source data, constructing an urban rail transit multi-layer dependent network model based on the urban rail transit multi-source data, inputting parameter information of an urban rail transit emergency into a calibrated urban rail transit node-edge joint cascading failure model, and outputting the influence of the emergency on passengers of the urban rail transit by the calibrated urban rail transit node-edge joint cascading failure model. Adopting a multi-layer dependent network model of urban rail transit characterizing urban rail transit system, a set of all nodes in different layers of the network, a set of edges within and between layers in the network, , , and , the total number of layers of the network, when , represent the edge set within layer , when , represent the edge set between layer and layer , and further, , wherein , represent the number of nodes in layer ; The urban rail transit multilayer dependent network model is written as , when there is an edge between a node in the layer and a node in the layer , otherwise .

4. The method of claim 3, wherein, ​ The historical failure data is input into the urban rail transit node-edge joint cascading failure model, the historical failure data records the real value of the influence of the disturbance on the passenger flow, the urban rail transit node-edge joint cascading failure model outputs the simulation value of the influence of the disturbance on the passenger flow, and the parameters in the urban rail transit node-edge joint cascading failure model are continuously adjusted so that the simulation value is closest to the real value, and the urban rail transit node-edge joint cascading failure model is calibrated.

5. The method of claim 4, wherein, ​ The line network topology data corresponding to the emergency of the urban rail transit, the smart card data of passenger travel, the train timetable data and the historical failure data are taken as input data, and the input data is input into the calibrated urban rail transit point-edge joint cascade failure model, and the input data is processed as follows by the urban rail transit point-edge joint cascade failure model: 1) constructing a network according to the line network topology data; 2) according to the stochastic user equilibrium model, the traffic represented by the smart card data of passenger travel is distributed to each station and line; 3) according to the train timetable data, the capacity of each interval of each line is determined; 4) according to the historical failure data, the failure is taken as input, and the cascade failure simulation is carried out to obtain the result of the influence of the emergency on the passengers of the urban rail transit.

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