CN112685864B - Double-deck high-speed rail interdependent network construction method and system based on realistic destructive factors - Google Patents

Abstract

The invention discloses a method and system for constructing a double-layer high-speed railway interdependent network based on realistic damage factors. Based on the high-speed railway track route, the Space L transportation network modeling method is used to build a high-speed railway unrighted and undirected Space L network; the Space L network is located on the same All nodes on the high-speed rail line are connected by a hyperedge to obtain the Space L hyperedge network; based on the high-speed rail track route, the Space P transportation network modeling method is used to construct a high-speed rail unrighted and undirected Space P network; the Space P network located on the same All nodes on the high-speed rail line are connected by a hyperedge to obtain the Space P hyperedge network; connect the nodes on the hyperedge in the Space L hyperedge network to the corresponding nodes on the hyperedge in the Space P hyperedge network to obtain the double-layer high-speed rail dependence The network model is based on realistic damage factors and is more in line with the actual situation. Compared with the Space L network model and the Space P network model, it can better reflect the real transportation system.

Description

Method and system for constructing a double-layer high-speed railway interdependent network based on realistic damage factors

Technical Field

The invention belongs to the technical field of rail transportation and relates to a method and system for constructing a double-layer high-speed railway interdependent network based on realistic damage factors.

Background technique

The latest "Outline of the Railway Priority Planning for a Strong Transportation Country in the New Era" clearly points out that railway transportation needs to ensure safety, continuity and stability, and the research on railway safety refers to the research on the robustness of the railway system. Therefore, recognizing the robustness of the railway system is one of the most important prerequisites for achieving a strong transportation country. The research on network robustness issues is to study the changes in network indicators when nodes in the network fail. In the field of transportation, the research on node failure refers to the occurrence of traffic congestion or the destruction of hub sites, which makes the road or transportation network fail and has no transportation capacity in a short period of time.

In the study of transportation networks, due to different research focuses, different network models need to be constructed. When analyzing the robustness of actual transportation networks, two construction methods, single space L or space P, are often used. However, in the process of actual application, the network indicators of space L cannot well represent the accessibility in real transportation networks. Indicators, when the space P network faces an attack, the deletion method of its network nodes cannot well represent the damage situation of the real transportation network.

Contents of the invention

In order to solve the deficiencies in the existing technology, the present invention provides a method and system for constructing a double-layer high-speed rail interdependent network based on realistic damage factors, which solves the problem that the existing transportation network constructed by a single space L or space P construction method cannot work well. The problem of characterizing the damage situation of real transportation network.

In order to solve the above technical problems, the present invention adopts the following technical solutions to achieve:

The construction method of double-layer high-speed rail interdependent network based on realistic damage factors includes:

Step 1. Based on the high-speed rail track route, use the Space L transportation network modeling method to build a high-speed rail unrighted and undirected Space L network. Among them, the nodes in the network represent cities with high-speed rail sites, and the connected edges between nodes represent a high-speed rail line. Connect two adjacent sites on the Internet;

Step 2: Use a hyperedge to connect all nodes in the Space L network in Step 1 that are located on the same high-speed rail line to obtain the Space L hyperedge network;

Step 3: Based on the high-speed rail route, use the Space P transportation network modeling method to build a high-speed rail unrighted and undirected Space P network. The nodes in the network represent cities with high-speed rail stations, and the edges between nodes represent the same high-speed rail. The connection between any two stations on the line;

Step 4: Use a hyperedge to connect all nodes in the Space P network in Step 3 that are located on the same high-speed rail line to obtain the Space P hyperedge network;

Step 5: Connect the nodes on the hyperedges in the Space L hyperedge network of step 2 and the corresponding nodes on the hyperedges in the Space P hyperedge network of step 4 to obtain a double-layer high-speed rail dependent network model.

Preferably, in steps 1 and 3, when there is more than one node in a city, these nodes are merged into one node.

The invention also discloses a double-layer high-speed railway dependent network construction system based on realistic damage factors, including: Space L network building module, used to build a high-speed railway unrightted and undirected Space L network;

Space P network building module, used to build a high-speed rail unrighted and undirected Space P network;

The hyperedge network building module is used to connect all nodes located on the same high-speed rail line in the Space L network using hyperedges to obtain the Space L hyperedge network; to connect all nodes located on the same high-speed rail line in the Space P network using hyperedges. Edge connection leads to Space P hyperedge network;

The double-layer high-speed rail dependent network building module is used to connect the nodes on the hyperedge in the Space L hyperedge network obtained by the hyperedge network building module with the nodes corresponding to the hyperedge in the Space P hyperedge network to obtain the double-layer high-speed rail dependent network model. .

Compared with the prior art, the beneficial effects of the present invention are:

Based on the characterization of the robustness of the high-speed rail network system by using the Space P network, the present invention introduces the Space L network model to analyze the damage of the network in actual situations, and constructs a double-layer high-speed rail interdependent network model that is more in line with the actual situation and based on real damage factors. From the results of the robust performance analysis of the network model of the present invention, it can be seen that the network constructed by the present invention exhibits better robustness than the existing Space L network and worse robustness than the Space P network model. Compared with the Space L network model and the Space P network model, the model can better reflect the real transportation system.

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1 is a flow chart of the method of the present invention.

Figure 2 is a schematic diagram of my country's high-speed rail network model based on the Space L method topology.

Figure 3 is a schematic diagram of my country's high-speed rail line network model based on the Space P method topology.

Figure 4 is a topological schematic diagram of the double-layer high-speed rail dependent network model of the present invention.

Figure 5 shows the decline of the maximum connected subgraph when the high-speed rail network model built by Space L faces different attacks.

Figure 6 shows the decline in network efficiency when the high-speed rail network model built by Space L faces different attacks.

Figure 7 shows the decrease of the maximum connected subgraph when the high-speed rail network model constructed by Space P faces different attacks.

Figure 8 shows the decline of the maximum connected subgraph when the high-speed rail network model constructed by Space P faces different attacks.

Figure 9 shows the decline of the maximum connected subgraph when the two-layer dependent network model of the present invention faces different attacks.

FIG. 10 shows the decrease in network efficiency when the double-layer interdependent network model of the present invention faces different attacks.

Detailed ways

The terms involved in the present invention are explained below:

"Unweighted and undirected network" means that the constructed network model does not consider the differences between different links, that is, it is an unweighted network, and does not consider the differences in different directions, that is, it is an undirected network.

"Hyperedge" in the network is used to connect nodes on the same line and represents the same line.

Specific embodiments of the present invention are given below. It should be noted that the present invention is not limited to the following specific embodiments. All equivalent transformations based on the technical solutions of this application fall within the protection scope of the present invention.

Example 1

This embodiment discloses a method for constructing a double-layer high-speed railway interdependent network based on realistic damage factors. As shown in Figure 1, the method specifically includes the following steps:

Step 1: Based on the corresponding data of high-speed rail lines and high-speed rail stations and the corresponding data of high-speed rail stations and cities, use the Space L transportation network modeling method to construct the unweighted and undirected Space L network of high-speed rail, and obtain the adjacency matrix of the Space L model. Among them, the nodes in the network represent cities with high-speed rail sites, and one node represents a city. Preferably, if there is more than one high-speed rail site in the city, these sites are still represented by one node; the edges between the nodes represent a high-speed rail line. There are adjacent sites between the above two cities.

The high-speed rail track route in this embodiment is obtained from the China Railway Administration and Baidu Maps. The Space L network constructed in this embodiment contains 172 nodes and 372 edges, as shown in Figure 2.

Step 2: Connect all nodes on the same high-speed rail line in the Space L network in Step 1 using a hyperedge, that is, the hyperedge connects all nodes on the same route in series to obtain the Space L hyperedge network;

Step 3: Based on the high-speed rail track route, use the Space P transportation network modeling method to construct the unweighted and undirected Space P network of the high-speed rail, and obtain the adjacency matrix of the Space P model. Among them, the nodes in the network represent cities with high-speed rail stations, and one node represents a city. Preferably, if there is more than one high-speed rail station in the city, these sites are still represented by one node; the edges between the nodes represent two cities. There is a high-speed rail line between them, that is, the edge between the nodes is the connection between any two cities on the same high-speed rail line.

The Space P network constructed in this embodiment contains 172 nodes and 3424 edges, as shown in Figure 3.

Step 4: Use a hyperedge to connect all nodes in the Space P network in Step 3 that are located on the same high-speed rail line to obtain the Space P hyperedge network;

Step 5, connect the nodes on the hyperedge in the Space L hyperedge network of step 2 and the corresponding nodes on the hyperedge in the Space P hyperedge network of step 4 to obtain a double-layer high-speed rail interdependent network model. The model structure of this embodiment is shown in Figure 4. In Figure 4, _X1 represents network layer ¹ , and _X2 represents network layer 2, that is, the Space P hyperedge network and ^the Space L hyperedge network are represented as two network layers, _e11 , _e21 represent hyperedge 1 and hyperedge 2 in network layer ^{1, and e12} _, ^e22 _represent hyperedge 1 and hyperedge 2 in network layer 2.

Example 2

This embodiment discloses a double-layer high-speed rail dependent network construction system based on realistic damage factors. The system includes a Space L network building module, a Space P network building module, a hyperedge network building module and a double-layer high-speed rail dependent network building module. Among them, the Space L network building module is used to build a high-speed rail unrighted and undirected Space L network; the Space P network building module is used to build a high-speed rail unrighted and undirected Space P network; the hyperedge network building module is used to connect the Space L network located in All nodes on the same high-speed rail line are connected using hyperedges to obtain the Space L hyperedge network; at the same time, all nodes on the same high-speed rail line in the Space P network are connected using hyperedges to obtain the Space P hyperedge network; double-decker high-speed rail The dependent network building module is used to connect the nodes on the hyperedge in the Space L hyperedge network obtained by the hyperedge network building module with the corresponding nodes on the hyperedge in the Space P hyperedge network to obtain the double-layer high-speed rail dependent network model.

For the Space L transportation network modeling method used in the above embodiments of the present invention, please refer to "Chen Weiwei, Zhang Fugui, Zhao Xiaobo. Rail transit network topological structure model and node importance analysis [J]. Journal of Chongqing Jiaotong University (Natural Science Edition), 2019, 38(07):107-113."; For the Space P transportation network modeling method, please refer to "Wang Yuhuan, Jin Cheng, Du Jiazhen. Nanjing Rail Transit Transfer Accessibility Based on Space-P Complex Network Sex Research[J]. Geography and Geographic Information Science, 2020, 36(01):87-92."

The performance of the double-layer high-speed rail dependent network model constructed in the above embodiment of the present invention will be described below.

Example 3

This embodiment analyzes the decline in network robustness indicators when facing different attack scenarios for the Space L network model, the Space P network model, and the two-layer dependent network model constructed in the above embodiments of the present invention. The specific evaluation methods are:

1) For the P-layer network in the double-layer high-speed rail interdependent network model (the processed Space P transportation network, which is later replaced by the P-layer network), all nodes are classified according to the following five sorting methods. The node sorting methods include five attack methods: random attack, initial degree attack, initial betweenness attack, update degree attack, and update betweenness attack. For each type of node, delete the nodes in turn. Each time a node is deleted, the maximum connected subgraph and network efficiency of the P-layer network are calculated. The relative maximum connected subgraph and network efficiency are selected to measure the robustness of the network after deletion. Specifically, the slower the relative maximum connected subgraph and network efficiency, the slower the network is destroyed, that is, the better the network robustness.

2) For the P-layer network in the double-layer high-speed rail dependent network model, classify the nodes according to the node classification method in 1), and then delete the nodes in each classification in turn. The attack network nodes target the L-layer network, and each time L is deleted For nodes in the layer network, a node in the P layer network will be deleted correspondingly, the structure of the P layer network will be updated according to the structure of the L layer network, and the maximum connected subgraph and network efficiency of the current P layer network will be calculated once; the relative maximum connectivity will be calculated Subgraph and network efficiency measure the robustness of the network after deletion. Specifically, the slower the relative maximum connected subgraph and network efficiency decrease, the slower the network is destroyed, that is, the better the network robustness.

3) Use the robustness index obtained in 1) and the basic Space L network model as the control group, and use the robustness index obtained in 2) as the experimental group. Based on the comparative analysis results, the constructed high-speed rail double-layer dependence Network models are evaluated.

Figures 5 to 10 show respectively the decline results of the network robustness index when the Space L network model, the Space P network model and the double-layer dependent network model of the present invention face different attack scenarios. The results show that the network model constructed by the present invention exhibits better robustness than the single Space L network model and worse robustness than the Space P network model. It can be explained as follows: the double-layer dependent network considers the reentry mechanism in the train on the basis of the Space L network model, so the double-layer dependent network has better robustness than the Space L network model; the double-layer dependent network model is better than the Space L network model The P network model takes into account that the break of a node will cause the nodes on both sides of the same line to be unable to connect, so the double-layer dependent network has worse robustness than the Space P network model.

Obviously, the double-layer interdependent network constructed by the method of the present invention can reflect a more realistic high-speed rail network system. The introduction of the reentry mechanism and the destruction of both sides of the node to prevent traffic are more in line with the actual operation of high-speed rail. The measures for the high-speed rail system to deal with attacks are also demonstrated. It solves the problem that when building a model using a single space L or space P, the network indicators of space L cannot well represent the various accessibility indicators in the real transportation network, and when the space P network faces attacks, the deletion method of network nodes cannot be very accurate. The problem of good representation of damage situations in real-world transportation networks.

Claims (3)

1. The method for constructing the double-layer high-speed rail dependent network based on the actual damage factor is characterized by comprising the following steps of:

step 1, constructing a Space L network with undirected high-speed rail by adopting a Space L traffic network modeling method based on a high-speed rail route, wherein nodes in the network represent cities with high-speed rail stations, and connecting edges between the nodes represent connecting lines between two adjacent stations on one high-speed rail line;

step 2, all nodes on the same high-speed railway in the Space L network in the step 1 are connected by adopting a superside, so that the Space L superside network is obtained;

step 3, constructing a Space P network with undirected high-speed rail by adopting a Space P traffic network modeling method based on the high-speed rail road line, wherein nodes in the network represent cities with high-speed rail stations, and connecting edges between the nodes represent connecting lines between any two stations on the same high-speed rail road line;

step 4, all nodes on the same high-speed railway in the Space P network in the step 3 are connected by adopting a superside, so that a Space P superside network is obtained;

and 5, connecting the nodes on the superside in the Space L superside network in the step 2 with the corresponding nodes on the superside in the Space P superside network in the step 4 to obtain a double-layer high-speed rail dependent network model.

2. The method for constructing a two-layer high-speed rail dependent network based on real destruction factors according to claim 1, wherein in the steps 1 and 3, when there is more than one node in one city, the nodes are combined into one node.

3. The double-layer high-speed rail dependent network construction system based on the actual damage factors is characterized in that the system is constructed by the double-layer high-speed rail dependent network construction method based on the actual damage factors according to claim 1 or 2; comprising the following steps:

the Space L network construction module is used for constructing a high-speed rail unowned and undirected Space L network;

the Space P network construction module is used for constructing a high-speed rail unowned and undirected Space P network;

the superside network construction module is used for connecting all nodes on the same high-speed railway in the Space L network by superside to obtain the Space L superside network; all nodes on the same high-speed railway in the Space P network are connected by using a superside, so that the Space P superside network is obtained;

and the double-layer high-speed rail dependent network construction module is used for connecting the nodes on the superside in the Space L superside network obtained by the superside network construction module with the corresponding nodes on the superside in the Space P superside network to obtain a double-layer high-speed rail dependent network model.