Railway cargo transportation network vulnerability assessment method based on cargo flow data

Abstract

The invention provides a railway cargo transportation network vulnerability assessment method based on cargo flow data. The method comprises the following steps: constructing an initialized RFTN; a pre-interruption model is adopted to distribute a cargo flow / traffic flow to a network, and the cargo flow / traffic flow is mapped to # imgabs0 #; initial network performance indexes # imgabs 1 # and # imgabs 2 # are calculated; distributing the cargo flow / traffic flow to the network again to obtain an invalid network # imgabs3 #; according to an output result, calculating network performance indexes # imgabs4 # and # imgabs5 # after interruption; respectively calculating three vulnerability indexes and a normalized and coupled comprehensive vulnerability index; repeating the steps until all interrupt scenes are simulated; and respectively obtaining RFTN vulnerabilities, key stations, intervals and areas under different interruption scenes and different vulnerability index measurements. According to the method, four different recovery strategies are introduced, partial elastic stages are reflected, network structures and performance fluctuation conditions of longer time before and after interruption events are captured, and the obtained results and conclusions are beneficial to formulating railway infrastructure strengthening strategies, determining maintenance resource allocation and formulating emergency response plans.

Description

Technical Field

[0001] The invention relates to the field of traffic safety assessment, and in particular to a railway freight transportation network vulnerability assessment method based on freight flow data. Background Art

[0002] As China's railway network continues to expand, China's railway freight transportation network (RFTN) has become increasingly complex and intertwined. Regional and seasonal differences in freight transport demand have exacerbated the uncertainty and fragility of the system. This phenomenon is more obvious in disruption scenarios. Reducing the fragility of the RFTN to improve resilience is crucial to maintaining the reliability and sustainability of China's economic and social development.

[0003] RFTN plays an important role in the modern logistics system due to its economic efficiency, safety, convenience, environmental friendliness and large capacity. The extensive railway freight network also brings great challenges to network management, one of the important challenges is how to manage the interruption of the railway freight network.

[0004] Given the complexity of the railway transportation environment, the railway freight network is more susceptible to various events than other modes of transportation such as roads, which can cause temporary interruptions in transportation. The duration of interruptions ranges from a few hours to a few days. Long-term and large-scale interruptions can even cause huge economic losses. The various events mentioned here include emergencies such as natural disasters and man-made attacks, and planned events such as railway maintenance and political control. Therefore, in order to better operate and plan the railway freight transportation network under potential interruption risks, it is urgent to fully understand the performance of the railway freight transportation network under interruption conditions, and then analyze its performance and behavior during the interruption period.

[0005] In the past decade, a variety of vulnerability assessment methods have been developed for the rich research on transport network vulnerability, and they are used to assess the degree of performance change of aviation, maritime, railway and subway networks, as well as coupled networks and intermodal networks under disruption. Although there is interoperability between these methods and indicators, the vulnerability characteristics of the railway network, such as strict capacity constraints, make it significantly different from other modes of transport in vulnerability case studies. For example, the transportation capacity of the railway network is strictly constrained, while the constraints of other networks are relatively looser.

[0006] So far, the research methods of railway network vulnerability have mainly focused on two aspects: topology and system. The former is rooted in graph theory, describing the railway network as an unweighted network, focusing on analyzing the topological characteristics of the network to study its vulnerability, but ignoring the dynamic impact of system performance. The latter describes the railway network as a weighted network with travel time, passenger flow, travel demand, etc. as weights, focusing on evaluating the impact of interruptions from the perspective of supply and demand of the system. Correspondingly, vulnerability evaluation indicators can also be divided into two categories, one is complex network theory indicators, such as topological efficiency, maximum connected subgraph size, network global efficiency and other indicators; the other is system dynamic characteristic indicators, such as generalized total travel cost, delay time, unmet demand, etc. In addition, some studies have attempted to combine topological indicators with system performance indicators.

[0007] Railway network vulnerability assessment includes topology, simulation, optimization, probabilistic model, data-driven and other methods. These assessment methods are not separated from the assessment indicators. They are usually combined to explore the vulnerability of railway networks from different perspectives. Among them, the most commonly used are topology, simulation and optimization methods. The topology method belongs to the complete enumeration method. It deletes a single network element according to certain rules, tracks the changes in the assessment indicators and analyzes its vulnerability. However, the exponential growth scenario of network element combination deletion is prone to excessive calculation time. The simulation method is similar to the topology method. It describes the vulnerability assessment based on theoretical and / or realistic interruption distribution. The interruption scenarios collected are more targeted and tend to analyze dynamic or random network reactions. However, there are still limitations of exponentially growing combinations of multiple network element interruptions and high computational requirements of simulation models. Although the optimization method overcomes the limitations of topology and simulation methods, it can solve extreme scenarios without completely enumerating all other scenarios. It is suitable for solving scenarios where multiple network elements are interrupted at the same time, but it also generates a lot of computing requirements for large-scale networks. Summary of the invention

[0008] In view of the above problems, the present invention provides a new framework to evaluate the vulnerability of RFTN. By integrating the real China railway freight transportation OD data, a method combining network topology characteristics with functional characteristics is introduced, and the comprehensive simulation and mathematical optimization measurement methods are used to expand the research on the vulnerability of railway freight transportation network from the perspective of network services. In order to achieve the above purpose, the technical solution adopted by the present invention is: a railway freight transportation network vulnerability assessment method based on freight flow data, the method includes, Step 1: Build and initialize RFTN; Step 2: Use the pre-disruption model to allocate freight / vehicle flows to the network and map them to middle; Step 3: Calculate the initial network performance indicators based on the network structure and traffic distribution model output before the interruption and ; Step 4: Disrupt the network according to a certain interruption simulation scenario, and use the post-interruption model to redistribute the cargo / vehicle flow to the network to obtain a failed network ; Step 5: Calculate the network performance index after the interruption based on the output results of the network structure and traffic distribution model after the interruption and ; Step 6: According to the indicators obtained in steps 3 and 5, calculate the three vulnerability indicators and the normalized coupled comprehensive vulnerability indicator respectively; Step 7: Repeat steps 4-6 until all interruption scenarios are simulated; Step 8: Based on the above calculation results, the RFTN vulnerability, key stations, sections and regions under different interruption scenarios and different vulnerability indicators are obtained.

[0009] Furthermore, the interruption simulation scenario is used to simulate common destructive events in transportation, including single network element failure, spatial local network element failure, random failure, and intentional failure.

[0010] Furthermore, the failure of a single network element specifically refers to the complete or incomplete interruption of each station or section in the network one by one, and the interruption level is respectively Discrete values ​​within a range, namely 25%, 50%, 75% and 100%; the comprehensive vulnerability index values ​​calculated according to the interruption of different stations or sections are ranked to identify the key stations or sections of the railway freight transportation network.

[0011] Furthermore, the failure of spatial local network elements specifically refers to assuming that the spatial failure area is circular, designing different spatial failure radii, and assuming that all network elements covered by areas corresponding to different radii fail, so as to achieve a complete interruption of multiple stations or sections in the network at the same time; and sorting the comprehensive vulnerability index values ​​obtained under the failure of different spatial local areas to identify key areas of different sizes in the railway freight transportation network.

[0012] Furthermore, the random failure specifically refers to the random interruption of stations or sections in the network one by one; the simulation test under the random failure scenario randomly selects stations each time, and in order to eliminate the random influence, multiple tests are required for each interruption probability, and the corresponding vulnerability index value takes the average value of the index values ​​obtained from all tests.

[0013] Furthermore, the intentional failure specifically refers to attacking and interrupting stations or sections in the network one by one according to a given strategy; performing intentional failure simulation based on network centrality indicators from high to low; the network centrality indicators include degree centrality, proximity centerline, betweenness centrality and eigenvector centrality.

[0014] Furthermore, the vulnerability indicators include structural vulnerability indicators, Functional vulnerability index and comprehensive vulnerability index.

[0015] Furthermore, the structural vulnerability index specifically includes, based on the constructed infrastructure layer network and interruption simulation scenario, from the perspective of the topological structure of the railway freight transportation network, using the structural vulnerability index of the maximum connected subgraph size change rate and the global efficiency change rate to evaluate the network performance when the station and section are interrupted; The maximum connected subgraph size change rate specifically includes that the maximum connected subgraph size is the number of nodes contained in the largest connected subgraph in the network, as shown in formula (1) and formula (2), and the maximum connected subgraph size change index is given by formula (3): ; ; ; in, Respectively represent the maximum connected subgraph size in the initial state and after interruption, The larger it is, the more vulnerable the network is and the easier it is to be interrupted and destroyed. The global efficiency change rate specifically includes that the global efficiency is the average efficiency between all node pairs in the network. The efficiency between two nodes in the transportation network is expressed as the inverse of the distance between the two points. Therefore, by calculating the average efficiency between all node pairs, the global efficiency of the network is expressed as: ; Then the change in the global efficiency of the network before and after the interruption can be described as ; in represents the global efficiency of the railway freight transportation network under normal conditions, The global efficiency of railway freight transport under interruption conditions is shown. The larger it is, the more vulnerable the network is to interruptions, that is, the less robust it is to interruptions.

[0016] Furthermore, the functional vulnerability index specifically includes, based on the functional layer and service layer characteristics of RFTN, the functional vulnerability index is determined as a value reflecting the change in network system performance, that is, the transportation cost change rate , and its calculation formula is shown in formula (6): ; in, denote the total transportation cost in the initial state and after the interruption, The larger it is, the more vulnerable the network is.

[0017] Furthermore, the comprehensive vulnerability index specifically includes: Four coupling coefficients are used to synthesize the relative changes in the topological structure and total network transportation cost before and after the interruption. ; ; ; ; ; Among them, each vulnerability indicator is normalized to the maximum and minimum using formula (36), so that the value range of the indicator is in the interval [0,1], which is convenient for coupling between indicators; restricted by formulas (33) and (34), The performance is not greater than 1; There are four weight coefficients, which represent the contribution of the three indicators to the vulnerability of the entire network. This means that the priority of each indicator or the importance in different situations is different in the decision-making process of different stakeholders. The values ​​of the weight coefficients vary according to the actual preferences and personalized management goals of different stakeholders.

[0018] Beneficial effects of this patent: 1. The framework proposed in this paper can evaluate the vulnerability of RFTN more comprehensively. By integrating the real OD data of China's railway freight transportation, introducing a method that combines network topology characteristics with functional characteristics, and integrating simulation and mathematical optimization measurement methods, the research on the vulnerability of railway freight transportation network is expanded from the perspective of network services.

[0019] 2. The vulnerability analysis framework proposed in this paper introduces four different recovery strategies (i.e., waiting for repair before passing through, rerouting goods, and transporting goods using other modes of transportation), which reflects the partial resilience stage and captures the network structure and performance fluctuations for a longer period of time before and after the disruption event. The results and conclusions obtained are helpful to formulate railway infrastructure reinforcement strategies, determine maintenance resource allocation, and formulate emergency response plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present invention has the following accompanying drawings: Figure 1 It is a schematic diagram of a framework of a railway freight transportation network vulnerability assessment method based on freight flow data of the present invention. DETAILED DESCRIPTION

[0021] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0022] like Figure 1As shown in the figure, a railway freight transportation network vulnerability assessment method based on freight flow data is described in detail as follows: Step 1: Build and initialize RFTN; Step 2: Use the pre-disruption model to allocate freight / vehicle flows to the network and map them to middle; Step 3: Calculate the initial network performance indicators based on the network structure and traffic distribution model output before the interruption and ; Step 4: Disrupt the network according to a certain interruption simulation scenario, and use the post-interruption model to redistribute the cargo / vehicle flow to the network to obtain a failed network ; Step 5: Calculate the network performance index after the interruption based on the output results of the network structure and traffic distribution model after the interruption and ; Step 6: According to the indicators obtained in steps 3 and 5, calculate the three vulnerability indicators and the normalized coupled comprehensive vulnerability indicator respectively; Step 7: Repeat steps 4-6 until all interruption scenarios are simulated; Step 8: Based on the above calculation results, the RFTN vulnerability, key stations, sections and regions under different interruption scenarios and different vulnerability indicators are obtained.

[0023] Among them, the interruption simulation scenario specifically refers to: Based on the vulnerability analysis requirements of RFTN from different angles, the present invention designs four types of interruption simulation scenarios to simulate common destructive events in transportation, such as natural disasters and man-made attacks. Among them, random failure and intentional failure are commonly used simulation scenarios in complex network theory, and their failures are only for stations in the network. Single network element failure and spatial local network element failure are often used to analyze the criticality of elements or regions in the network (described by vulnerability level in the present invention), and their failure objects include any station or section of the network.

[0024] (1) Failure of a single network element: each station or section in the network is completely or partially interrupted one by one, and the interruption level is The discrete values ​​within the range are 25%, 50%, 75% and 100%. The comprehensive vulnerability index values ​​calculated based on the disruption of different stations or sections are ranked to identify the key stations or sections of the railway freight transportation network.

[0025] (2) Spatial local network element failure: Assuming that the spatial failure area is circular, different spatial failure radii are designed, and it is assumed that all network elements covered by the areas corresponding to different radii fail, so as to achieve the simultaneous complete interruption of multiple stations or sections in the network. The comprehensive vulnerability index values ​​obtained under different spatial local area failures are ranked to identify key areas of different sizes in the railway freight transportation network.

[0026] (3) Random failure: stations or sections in the network are randomly interrupted one by one. In this scenario, the simulation test randomly selects stations each time. Therefore, in order to eliminate the random influence, 100 tests are required for each interruption probability, and the corresponding vulnerability index value is the average value of the index values ​​obtained from all tests.

[0027] (4) Deliberate failure: Attack and interrupt stations or sections in the network one by one according to a given strategy. In graph theory and network analysis, network centrality indicators are used to identify the most important nodes in the graph. The present invention simulates deliberate failure based on four network centrality indicators: degree centrality, proximity centrality, betweenness centrality, and eigenvector centrality, from high to low.

[0028] Vulnerability indicators, specifically 1. Structural vulnerability indicators Based on the constructed infrastructure layer network and interruption simulation scenarios, from the perspective of the topological structure of the railway freight transportation network, this study uses two structural vulnerability indicators, namely the maximum connected subgraph size and the global efficiency of the network, to evaluate the network performance when stations and sections are interrupted.

[0029] (1) Maximum connected subgraph size change rate The maximum connected subgraph size refers to the number of nodes contained in the largest connected subgraph in the network, as shown in equations (1) and (2). The maximum connected subgraph size change index is given by equation (3).

[0030] ; ; ; in, They represent the maximum connected subgraph size in the initial state and after the interruption respectively. The larger it is, the more vulnerable the network is and the easier it is to be interrupted and destroyed.

[0031] (2) Global efficiency change rate Global efficiency is often used to analyze the overall connectivity and transportation efficiency of a network, and is defined as the average efficiency between all pairs of nodes in the network. The efficiency between two nodes in a transportation network is generally expressed as the inverse of the distance between the two points. Therefore, by calculating the average efficiency between all pairs of nodes, the global efficiency of the network is expressed as: ; Then the change in the global efficiency of the network before and after the interruption can be described as ; in represents the global efficiency of the railway freight transportation network under normal conditions, Represents the global efficiency of railway freight transportation under disruption conditions. The larger it is, the more vulnerable the network is to interruptions, that is, the less robust it is to interruptions.

[0032] 2. Functional Vulnerability Indicators Based on the functional layer and service layer characteristics of RFTN, its functional vulnerability index is determined as the value that reflects the change of network system performance, that is, the transportation cost change rate , and its calculation formula is given by formula (6).

[0033] ; in, represent the total transportation cost in the initial state and after the interruption respectively. The larger it is, the more vulnerable the network is.

[0034] Considering the difficulty and scale of solving the point-arc based model and its difficulty in tracing the specific infeasible OD, the cargo flow allocation optimization problem is formulated as an arc-road based vehicle flow path optimization model to reduce the number of variables in advance by removing paths that do not meet the additional operation constraints. The symbols and meanings used in model construction are listed in Table 1.

[0035] Table 1 Overview of tags

[0036] (1) Pre-interruption model In a given railway freight transportation network, the optimization model of railway traffic flow before interruption can be calculated to obtain the traffic flow path that minimizes the total railway freight transportation cost under normal circumstances. We model this problem as a 0-1 integer programming model: ; Subject to: ; ; ; ; ; ; ; Objective function (7) seeks to minimize the total transportation cost of railway transportation of goods from the supply location to the demand location. Part 1 describes that the transportation cost of each feasible route is composed of the total interval mileage and unit mileage transportation cost of the operating route. Taking into account the capacity limitations of the railway freight transportation network and the timeliness constraints of freight transportation, it is allowed that some freight transportation needs cannot be met, which corresponds to the second part of objective function (7). Constraint (8) describes the principle of inseparable traffic in railway traffic allocation, ensuring that each traffic flow has and passes through a feasible route to meet the freight transportation demand. Constraints (9) and (10) describe the interval and station capacity constraints of the network respectively, taking into account the limited interval throughput capacity and station capacity in the network. Constraint (11) describes the traffic route mileage constraint, also called the detour rate constraint, and introduces the detour rate threshold. The concept is that for a certain traffic flow, the traffic flow paths available for selection include the shortest path, and also include the physical mileage in the shortest path. Constraint (12) indicates that at least one feasible path is required to meet each cargo transportation demand before the cargo delivery deadline. Finally, equations (13) and (14) restrict the range of decision variables.

[0037] (2) Post-interruption model Considering that the interruption of stations and sections in the network will lead to a certain degree of capacity loss, in order to observe how RFTN deploys the remaining network resources to meet all demands after the interruption, we embed four coping strategies in the post-interruption model. Next, we introduce the specific mathematical model.

[0038] ; Subject to: ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; When a network interruption occurs, the traffic flow needs to be adjusted in time to meet as many freight transport demands as possible and reduce the impact on the quality of freight transport services. Assuming that only the traffic flow organization currently remaining in the network is considered during the interruption, the traffic flow allocation optimization model after the interruption is established as shown in equations (15)-(31). On the basis of seeking to minimize the generalized railway freight transportation cost, the objective function (15) considers the allocation of traffic flows that are not affected by the interrupted network elements and traffic flows that are affected by the interrupted network elements. Four response strategies are included in the objective function of the post-interruption model, namely, the waiting repair strategy, the rerouting strategy, and the strategy of using other transportation methods to meet demand. Traffic flows that are not affected by the interrupted network elements can still use the residual network for transportation. Part of the traffic flows affected by the interrupted network elements can choose to change the transportation route and participate in the traffic flow allocation that is not affected by the interrupted network elements. These two parts of traffic flows correspond to the first part of the objective function (15). The traffic flow affected by the interrupted network element can choose to wait for repair and continue to use the network element for transportation, which corresponds to the second part of the objective function (15); it can choose to switch to other modes of transportation, which corresponds to the third part of the objective function (15); or it can choose to cancel the service, that is, include it in the infeasible flow of the fourth part of the objective function (15).

[0039] Similar to the constraints of the pre-disruption model, the post-disruption model also contains four types of constraints. The traffic demand is modeled in constraint (16), assuming that the demand can be distributed among the available paths, including transportation through the interrupted network elements under different repair schemes, transportation through other modes of transportation, and transportation through virtual arcs for infeasible flows. Constraints (17) and (18) respectively specify the capacity constraints of the normal operating interval and the interval affected by the disruption, and constraints (19) and (20) respectively specify the capacity constraints of the normal operating stations and the stations affected by the disruption. We consider two types of disruptions: partial disruption and complete disruption. Constraints (21) and (22) respectively describe the mileage constraints of the traffic paths that pass through or do not pass through the interrupted interval or station, which means that even after the disruption, the railway traffic organization should meet the rationality requirements of the path and should not make excessive detours to meet the transportation demand, resulting in a waste of transportation resources. Constraints (23) and (24) indicate that the freight transportation service before and after the disruption must meet the time constraint of the freight delivery deadline to be considered as a complete transportation service, that is, to meet the freight transportation demand. Constraint (25) ensures that the path through the interrupted station or section is only accessible after the repair process is completed. Constraint (26) limits the transportation capacity of other modes of transportation after the interruption to prevent all freight demands from being met by other modes of transportation, which in turn leads to extremely low railway freight service quality that is out of touch with reality. Finally, equations (27)-(31) specify the sign restrictions of integer decision variables, corresponding to whether the traffic passes through an uninterrupted network element, whether the traffic passes through an interrupted network element, whether the interrupted network element is repaired, whether the traffic meets the transportation demand through other modes of transportation, and whether it is an infeasible traffic flow.

[0040] 3. Comprehensive vulnerability index From the perspective of the three-layer comprehensive performance of the railway freight transportation network, this application proposes a comprehensive vulnerability index for railway freight network vulnerability assessment , as shown in formula (32), Four coupling coefficients are used to synthesize the relative changes in the topological structure and total network transportation cost before and after the disruption.

[0041] ; ; ; ; ; Among them, each vulnerability indicator is normalized to the maximum and minimum using formula (36), so that the value range of each indicator is in the interval [0,1], which is convenient for coupling between indicators. Limited by formulas (33) and (34), The performance is not greater than 1. There are four weight coefficients, which represent the contribution of the three indicators to the overall network vulnerability, which means that the priority of each indicator or the importance in different situations is different in the decision-making process of different stakeholders. The weight coefficient values ​​can be differentiated according to the actual preferences and personalized management goals of different stakeholders.

[0042] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present patent is described in detail with reference to the embodiments, a person skilled in the art should understand that the technical solutions implemented in the present invention can be modified or replaced by equivalents without departing from the design spirit and scope of the present invention, which should be included in the scope of the claims of the present invention.

[0043] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

Claims

1. A railway freight transportation network vulnerability assessment method based on freight flow data, characterized in that: The method comprises, Step 1: Build and initialize RFTN; Step 2: Use the pre-disruption model to allocate freight / vehicle flows to the network and map them to middle; Step 3: Calculate the initial network performance indicators based on the network structure and traffic distribution model output before the interruption and ; Step 4: Disrupt the network according to a certain interruption simulation scenario, and use the post-interruption model to redistribute the cargo / vehicle flow to the network to obtain a failed network ; Step 5: Calculate the network performance index after the interruption based on the output results of the network structure and traffic distribution model after the interruption and ; Step 6: According to the indicators obtained in steps 3 and 5, calculate the three vulnerability indicators and the normalized coupled comprehensive vulnerability indicator respectively; Step 7: Repeat steps 4-6 until all interruption scenarios are simulated; Step 8: Based on the above calculation results, the RFTN vulnerability, key stations, sections and regions under different interruption scenarios and different vulnerability indicators are obtained.

2. The method for assessing the vulnerability of a railway freight transportation network based on freight flow data according to claim 1, characterized in that: The disruption simulation scenario is used to simulate common disruptive events in transportation. Including failure of single network elements, failure of local network elements in space, random failure and intentional failure.

3. The method for assessing the vulnerability of a railway freight transportation network based on freight flow data according to claim 2, characterized in that: The failure of a single network element specifically refers to the complete or incomplete interruption of each station or section in the network one by one, and the interruption level is respectively Discrete values ​​within a range, namely 25%, 50%, 75% and 100%; the comprehensive vulnerability index values ​​calculated according to the interruption of different stations or sections are ranked to identify the key stations or sections of the railway freight transportation network.

4. The method for assessing the vulnerability of a railway freight transportation network based on freight flow data according to claim 2, characterized in that: The spatial local network element failure specifically refers to assuming that the spatial failure area is circular, designing different spatial failure radii, and assuming that all network elements covered by areas corresponding to different radii fail, so as to achieve simultaneous and complete interruption of multiple stations or sections in the network; The comprehensive vulnerability index values ​​obtained under different spatial local area failures are ranked to identify critical areas of different sizes in the railway freight transportation network.

5. The method for assessing the vulnerability of a railway freight transportation network based on freight flow data according to claim 2, characterized in that: The random failure specifically refers to the random interruption of stations or sections in the network one by one. The simulation test under the random failure scenario randomly selects stations each time. In order to eliminate the random influence, multiple tests are required for each interruption probability, and the corresponding vulnerability index value takes the average value of the index values ​​obtained from all tests.

6. A railway freight transportation network vulnerability assessment method based on freight flow data as claimed in claim 2, characterized in that: The intentional failure specifically refers to attacking and interrupting stations or sections in the network one by one according to a given strategy; performing intentional failure simulation based on network centrality indicators from high to low; the network centrality indicators include degree centrality, proximity centerline, betweenness centrality and eigenvector centrality.

7. The method for assessing the vulnerability of a railway freight transportation network based on freight flow data according to claim 1, characterized in that: The vulnerability indicators include structural vulnerability indicators, Functional vulnerability index and comprehensive vulnerability index.

8. The method for assessing the vulnerability of a railway freight transportation network based on freight flow data according to claim 7, characterized in that: The structural vulnerability index specifically includes, based on the constructed infrastructure layer network and interruption simulation scenario, from the perspective of the topological structure of the railway freight transportation network, using the structural vulnerability index of the maximum connected subgraph size change rate and the global efficiency change rate to evaluate the network performance when the station and section are interrupted; The maximum connected subgraph size change rate specifically includes that the maximum connected subgraph size is the number of nodes contained in the largest connected subgraph in the network, as shown in formula (1) and formula (2), and the maximum connected subgraph size change index is given by formula (3): ； ； ； in, Respectively represent the maximum connected subgraph size in the initial state and after the interruption, The larger it is, the more vulnerable the network is and the easier it is to be interrupted and destroyed. The global efficiency change rate specifically includes that the global efficiency is the average efficiency between all node pairs in the network. The efficiency between two nodes in the transportation network is expressed as the inverse of the distance between the two points. Therefore, by calculating the average efficiency between all node pairs, the global efficiency of the network is expressed as: ； Then the change in the global efficiency of the network before and after the interruption can be described as ； in represents the global efficiency of the railway freight transportation network under normal conditions, represents the global efficiency of railway freight transportation under disruption, The larger it is, the more vulnerable the network is to interruptions, that is, the less robust it is to interruptions.

9. The method for assessing the vulnerability of a railway freight transportation network based on freight flow data according to claim 7, characterized in that: The functional vulnerability index specifically includes, based on the functional layer and service layer characteristics of RFTN, the functional vulnerability index is determined as a value reflecting the change of network system performance, that is, the transportation cost change rate , and its calculation formula is shown in formula (6): ； in, denote the total transportation cost in the initial state and after the interruption, The larger it is, the more vulnerable the network is.

10. The railway freight transportation network vulnerability assessment method based on freight flow data according to claim 7, characterized in that: The comprehensive vulnerability index specifically includes: Four coupling coefficients are used to synthesize the relative changes in the topological structure and total network transportation cost before and after the interruption. ； ； ； ； ； Among them, each vulnerability indicator is normalized to the maximum and minimum using formula (36), so that the value range of the indicator is in the interval [0,1], which is convenient for coupling between indicators; restricted by formulas (33) and (34), The performance is not greater than 1; There are four weight coefficients, which represent the contribution of the three indicators to the vulnerability of the entire network. This means that the priority of each indicator or the importance in different situations is different in the decision-making process of different stakeholders. The values ​​of the weight coefficients vary according to the actual preferences and personalized management goals of different stakeholders.

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