CN112685864B - Double-layer high-speed rail dependent network construction method and system based on reality destruction factors - Google Patents
Double-layer high-speed rail dependent network construction method and system based on reality destruction factors Download PDFInfo
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Abstract
The invention discloses a method and a system for constructing a double-layer high-speed rail dependent network based on reality destruction factors, wherein a Space L traffic network modeling method is adopted to construct a Space L network with undirected high-speed rail on the basis of high-speed rail road lines; all nodes on the same high-speed rail in the Space L network are connected by adopting a superside, so that the Space L superside network is obtained; constructing a Space P network with undirected high-speed rail and undirected high-speed rail by adopting a Space P traffic network modeling method based on the high-speed rail road line; all nodes on the same high-speed railway in the Space P network are connected by adopting a superside, so that the Space P superside network is obtained; and connecting the nodes on the superside in the Space L superside network with the nodes corresponding to the superside in the Space P superside network to obtain a double-layer high-speed rail dependent network model, wherein the model is more fit with the actual situation based on the actual damage factors, and can better embody a real traffic system relative to the Space L network model and the Space P network model.
Description
Technical Field
The invention belongs to the technical field of rail transit, and relates to a method and a system for constructing a double-layer high-speed rail dependent network based on a real damage factor.
Background
The latest coming-out 'new era traffic national railway advanced planning outline' clearly indicates that railway transportation needs to ensure safety and stability, and the research on railway safety refers to the research on the robustness of a railway system, so that the recognition of the robustness of the railway system is one of the most important preconditions for realizing traffic national. The research on the network robustness problem is to study the change of network indexes after the node in the network fails. In the traffic field, the research of node failure refers to the occurrence of traffic jam or the damage of hub sites, so that a road or a transportation network fails and has no transportation capability in a short time.
In the research of the transportation network, different network models need to be constructed due to different emphasis on the research. When the robustness of the actual traffic network is analyzed, a single space L or space P construction mode is often adopted, but in the actual application process, the network indexes of the space L cannot well represent various indexes of the reachability in the actual traffic network, and when the space P network faces to the attack, the deletion mode of the network nodes cannot well represent the damage condition of the actual traffic network.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a method and a system for constructing a double-layer high-speed rail dependent network based on a real damage factor, and solves the problem that a traffic network constructed by the existing single space L or space P construction mode cannot well represent the damage situation of the real traffic network.
In order to solve the technical problems, the invention adopts the following technical scheme:
the construction method of the double-layer high-speed rail dependency network based on the actual damage factors comprises the following steps:
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.
Preferably, in the step 1 and the step 3, when there is more than one node in a city, the nodes are combined into one node.
The invention also discloses a system for constructing the double-layer high-speed rail dependency network based on the real destruction factors, which comprises 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.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, based on the fact that the Space P network is adopted to characterize the robustness of the high-speed rail network system, a Space L network model is introduced to analyze the network damage condition in the actual situation, and a double-layer high-speed rail dependent network model based on the actual damage factors, which is more fit with the actual situation, is constructed. As can be seen from the analysis result of the robust performance of the network model, the network constructed by the invention has better robustness than the existing Space L network and worse robustness than the Space P network model, and the model can better embody a real traffic system relative to the Space L network model and the Space P network model.
Drawings
Fig. 1 is a flow chart of the method of the present invention.
Fig. 2 is a schematic diagram of a network model of a high-speed rail in China according to the Space L method topology.
Fig. 3 is a schematic diagram of a network model of a high-speed rail in China according to the Space P method topology.
FIG. 4 is a schematic topology of a two-layer high-speed rail dependent network model of the present invention.
Fig. 5 is a descending situation of the maximum connected subgraph when the Space L constructed high-speed rail network model faces different attacks.
FIG. 6 is a diagram showing a decrease in network efficiency when the Space L-constructed high-speed rail network model is subjected to different attacks
Fig. 7 is a descending situation of the maximum connected subgraph when the high-speed railway network model constructed by Space P faces different attacks.
Fig. 8 is a descending situation of the maximum connected subgraph when the high-speed railway network model constructed by Space P faces different attacks.
FIG. 9 is a graph showing the maximum connected subgraph drop when the dual-layer dependent network model of the present invention faces different attacks.
FIG. 10 is a graph showing the degradation of network efficiency when the dual-layer dependent network model of the present invention faces different attacks.
Detailed Description
The terms involved in the present invention are explained as follows:
the term "unowned unoriented network" refers to an unoriented network that is constructed without considering the differences between different edges and that is an unoriented network that is constructed without considering the differences in different directions.
The "superside" in the network is used to connect nodes on the same line, representing the same line.
The following specific embodiments of the present invention are provided, and it should be noted that the present invention is not limited to the following specific embodiments, and all equivalent changes made on the basis of the technical solutions of the present application fall within the protection scope of the present invention.
Example 1
The embodiment discloses a method for constructing a double-layer high-speed rail dependent network based on realistic damage factors, which specifically comprises the following steps as shown in fig. 1:
step 1, constructing a Space L network with undirected high-speed rail by adopting a Space L traffic network modeling method based on data corresponding to a high-speed rail line and a high-speed rail station and data corresponding to a city, and obtaining an adjacency matrix of a Space L model. Wherein, the nodes in the network represent cities with high-speed rail stations, one node represents one city, and preferably, if more than one high-speed rail station exists in the city, the stations are represented by one node; the border between nodes represents the existence of adjacent sites between two cities on a high-speed rail line.
The high-speed rail road line of the present embodiment is obtained from the chinese railway office and the hundred degree map. The Space L network constructed in this embodiment includes 172 nodes and 372 edges, as shown in fig. 2.
Step 2, connecting all nodes on the same high-speed railway in the Space L network in the step 1 by adopting a superside, namely connecting all nodes on the same high-speed railway in series by the superside to obtain the Space L superside network;
and 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 to obtain an adjacency matrix of the Space P model. Wherein a node in the network represents a city with a high-speed rail station, a node represents a city, preferably, if more than one high-speed rail station exists in the city, the stations are represented by a node; the connecting edge between the nodes represents a high-speed railway line passing between two cities, namely the connecting edge between the nodes is a connecting line between any two cities on the same high-speed railway line.
The Space P network constructed in this embodiment includes 172 nodes and 3424 connected edges, as shown in fig. 3.
Step 4, all nodes on the same high-speed railway in the Space P network in the step 3 are connected by a superside, so that the Space P superside network is obtained;
step 5, superb in the Space L superb network in step 2And (3) connecting the node with the node corresponding to the superside in the Space P superside network in the step (4) to obtain the double-layer high-speed rail dependent network model. The model structure of this embodiment is shown in fig. 4. X in FIG. 4 1 Representing network layer 1, X 2 Represented is network layer 2, namely, space P superside network and Space L superside network are represented as two network layers, e 1 1 、e 2 1 Representing superside 1 and superside 2, e in network layer 1 1 2 、e 2 2 Representing superside 1 and superside 2 in network layer 2.
Example 2
The embodiment discloses a double-layer high-speed rail dependent network construction system based on reality destruction factors, which comprises a Space L network construction module, a Space P network construction module, an over-edge network construction module and a double-layer high-speed rail dependent network construction module. 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; simultaneously, all nodes on the same high-speed railway in the Space P network are connected by using a superside so as to obtain the Space P superside network; 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.
The Space L traffic network modeling method used in the above embodiment of the invention can refer to pico, zhang Fugui, zhao Xiaobo. Rail transit network topology model and node importance analysis [ J ]. Chongqing university journal (Nature science edition), 2019,38 (07): 107-113'; space P traffic network modeling methods can be referred to as "Wang Yuhuan, cheng, dujia. Nanjing rail transit transfer reachability research based on Space-P complex networks [ J ]. Geographic and geographic information science, 2020,36 (01): 87-92 ].
The performance of the two-layer high-speed rail dependent network model constructed in the above embodiment of the present invention is described below.
Example 3
The embodiment analyzes the reduction situation of the network robustness index when the Space L network model, the Space P network model and the double-layer dependent network model constructed by the embodiment of the invention face different attack situations. The specific evaluation method comprises the following steps:
1) Aiming at a P layer network (a processed Space P traffic network, and replaced by the P layer network) in the double-layer high-speed rail dependent network model, all nodes are classified according to the following five ordering modes, wherein the ordering modes of the nodes comprise 5 attack modes of random attack, initial degree attack, initial betting attack, update degree attack and update betting attack. For each type of node, deleting the node in turn, calculating the maximum connected subgraph and network efficiency of the P-layer network once every node is deleted, and selecting the relative maximum connected subgraph and network efficiency to measure the network robustness after deletion, specifically, the slower the relative maximum connected subgraph and network efficiency is, the slower the network is destroyed, namely the better the network robustness is.
2) Classifying according to the node classification mode in 1) aiming at the P layer network in the double-layer high-speed rail dependent network model, deleting the node in each classification in sequence, correspondingly deleting the node in one P layer network for each node in one L layer network deleted by an attack network node aiming at the L layer network, updating the structure of the P layer network once according to the structure of the L layer network, and calculating the maximum connected subgraph and network efficiency of the current P layer network once; taking the relative maximum connected subgraph and the network efficiency to measure the network robustness after deletion, specifically, the slower the relative maximum connected subgraph and the network efficiency are, the slower the network is destroyed, namely the better the network robustness is.
3) Taking the robustness index obtained by the Space L network model of the 1) and the foundation as a control group, taking the robustness index obtained by the 2) as an experimental group, and evaluating the constructed high-speed rail double-layer dependent network model according to a control analysis result.
Fig. 5 to 10 show the results of the decrease of the network robustness index when facing different attack situations, respectively, the Space L network model, the Space P network model, and the dual-layer dependent network model of the present invention. The result shows that the network model constructed by the method provided by the invention has better robustness than a single Space L network model and worse robustness than a Space P network model. It can be explained that: the double-layer dependent network considers that a turn-back mechanism exists in the train on the basis of the Space L network model, so that the double-layer dependent network has better robustness than the Space L network model; compared with the Space P network model, the double-layer dependent network model considers that nodes on two sides of the same line cannot be connected due to the breakage of the nodes, so that the double-layer dependent network model has poorer robustness than the Space P network model.
Obviously, the double-layer dependent network constructed by the method can reflect a more real high-speed rail network system, the introduction of a turn-back mechanism and the damage of two sides of a node can not be communicated with vehicles so as to more accord with the operation of high-speed rails in reality, and measures of the high-speed rail system for coping with attacks are displayed. The method solves the problems that when a single space L or space P builds a model, network indexes of the space L cannot well represent various indexes of reachability in a real traffic network, and when the space P network faces attack, the deleting mode of network nodes cannot well represent the damage condition of the real traffic network.
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.
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