CN113742870A - Multi-mode public transport network key station identification method based on local heterogeneous influence - Google Patents
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Abstract
A multi-mode public transport network key station identification method based on a network topology structure and station passenger flow belongs to the field of urban public transport operation. Firstly, abstracting a multi-mode public transport network topological graph according to the data of the multi-mode public transport lines and stations; on the basis, based on a complex network basic theory, calculating the network node degree and the H-index, considering the weak relation influence of the network nodes, and fully mining the potential influence of the network nodes in the aspect of a network structure; then combining the ground bus and subway card swiping data to obtain the passenger flow of the multi-mode public transport network station; identification of network key sites. The invention considers the topological structure importance and passenger flow characteristics of the multi-mode public transport station, constructs a method capable of representing the importance of the multi-mode public transport network station, and provides protection measures for urban public transport managers aiming at key nodes in the network in advance to guarantee the travel safety.
Description
Technical Field
The invention belongs to the technical field of public transport operation, and is specially used for identifying key stations of urban public transport networks.
Background
With the diversification of travel modes, urban public transport networks become more complicated from the aspects of network structures and passenger flows, once key nodes (stations) in the public transport networks are damaged, the whole public transport system can be seriously affected, even the whole public transport system can be irreparably affected, especially at the peak time of morning and evening. Therefore, in the face of a highly complex public transport network, it is necessary to provide a multi-mode public transport network key station identification method for the public transport network by combining the passenger flow characteristics of the public transport network, so as to provide theoretical support for enhancing the toughness of the multi-mode public transport network. However, the following problems still exist in relation to urban multi-mode public transport network modeling and station importance discrimination at present:
(1) the modeling method of the urban public transport network in a single mode is more, and how to construct a multi-link and multi-mode coupled public transport network method with rights according to different travel modes and connection rules among the travel modes is still lacking at present.
(2) At present, the identification process of key stations of a public transport network is mainly based on a single network topology index, the single index cannot accurately identify the topological importance degree of network nodes, a comprehensive network topology evaluation index is lacked, and the importance degree of each station in a multi-mode public transport network is measured.
(3) The network topology structure is the basis of public transport operation, passenger flow is the blood vessel of a public transport network, and public transport network key station evaluation is carried out by only depending on one aspect, which generates larger deviation with actual operation, so that a set of comprehensive multi-mode public transport network key station identification and evaluation method fusing the network topology structure and the passenger flow characteristics is urgently needed to be constructed.
Disclosure of Invention
The method abstractly extracts the multi-mode public transport network topological graph according to the multi-mode public transport line and station data; on the basis, a key station identification method representing the integration of the multi-mode public transport network is constructed according to the network topology index and the station passenger flow index, theoretical support is provided for urban public transport managers to monitor weak links of the public transport network daily, protective measures are provided in advance aiming at key nodes in the network in a targeted manner, and the toughness of the multi-mode public transport network is improved.
The design principle of the invention is that each link of a trip chain in the multi-mode public transport network is taken as an edge, and a multi-mode public transport network adjacency matrix containing different public transport modes such as subway and ground public transport is constructed and stored as a multi-mode public transport network topology database; abstracting the multi-mode public transport network topological graph according to the topological relation matrix, and calculating a topological importance index for measuring network nodes; and further provides a key station identification method based on comprehensive heterogeneous influence by combining the topological characteristics of the multi-mode public transport network and the passenger flow characteristics of the stations. The specific identification method of the invention is as follows:
step 1: constructing a multi-mode public transport network adjacency matrix;
the multi-mode public transport that this patent relates to mainly includes: ground public transport, subway, bicycle and walking mode. The basic public transport network in this patent is ground public transport network and subway network, and bicycle and walking mode mainly used are transferred in this patent.
The multi-mode public transport network consists of nodes and connecting edges, wherein the nodes are stations in the multi-mode public transport network; the connecting edge comprises two types in the patent, one type is a line edge, namely the connecting edge between adjacent stations in a ground public transport or subway line system, the other type is a virtual edge, namely a transfer edge between the ground public transport and the subway system, and the specific travel mode is a bicycle or a walking mode. And constructing a multi-mode public transport network node adjacency matrix according to the incidence relation between the stations based on the description of the nodes and the connection edges, and drawing a multi-mode public transport network diagram.
Step 2: calculating a topological importance index of the multi-mode public transport network;
based on the multi-mode public transport network adjacency matrix, the degree and the H-index of each node in the network are calculated by adopting a complex network basic theory. The network node degree refers to the degree of the network, which is one of the simplest and most important concepts for describing the attribute of a single node and represents the number of edges directly connected with the node. The larger the value of a node, the more edges it represents connected to, and the more important the position in the network. The node H-index in the network is defined as that the node has at least m neighbors connected with the node, and the value of the neighbor node is not less than m. And finally, storing the result as a bus network topology index database.
And step 3: acquiring the passenger flow of the multi-mode public transport network station according to the multi-mode public transport card swiping data;
the multi-mode public transportation network station passenger flow is one of the key factors for constructing the multi-mode public transportation network. Through the multi-mode public transport network card swiping data, the station incoming and outgoing passenger flow of each station of the multi-mode public transport network is extracted, the statistical time interval unit is day, and the processed passenger flow of the station of the multi-mode public transport network is stored in a database.
And 4, step 4: and (3) according to the calculation indexes of the steps 2 and 3, considering the influence of the weak relationship of the nodes, and constructing a multi-mode public transport network key station identification method based on local heterogeneous influence.
The method for identifying the key station of the multi-mode public transport network based on the local heterogeneous influence of the nodes is creatively constructed by exploring the defects of a single network topology index in the research of the importance of the nodes, combining the meaning of the multi-mode public transport network topology structure index and the station passenger flow and considering the weak relation influence in the node information transmission process.
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FIG. 1 general framework diagram of the system
FIG. 2 is a schematic diagram of a multi-mode public transportation network construction
FIG. 3 is a general flow chart of an embodiment of the present invention
Detailed Description
The following detailed description of the preferred embodiments of the present invention, taken in conjunction with the accompanying drawings, is intended to be illustrative, and not restrictive, and any other similar items may be considered within the scope of the present invention. The method of the invention is set up and explained according to the overall frame diagram of the system shown in FIG. 1; the modeling process of the multi-mode public transportation network is explained with reference to fig. 2; a general flow chart of the method and implementation of the present invention is given in conjunction with fig. 3. Specific embodiments of the present invention are described in detail below:
a multi-mode public transport network key station identification method based on local heterogeneous influence is characterized in that: the method comprises the following operation steps:
a multi-mode public transport network key station identification method based on local heterogeneous influence is characterized in that: the method comprises the following operation steps:
step S1: constructing a multi-mode public transport network adjacency matrix;
(1) and establishing a multi-mode public transport network basic database. The basic data mainly used in the patent comprises ground public transportation network information and rail transit network information. The ground public transportation information mainly comprises public transportation line information data and stop information data; the rail transit network information includes rail transit line information data and station information data.
(2) And (4) removing the duplication of the multi-mode public transport network station. Stations in different modes related to the patent are numbered in sequence, and the same stations of different lines are combined into a station number.
(3) And constructing a multi-mode public transport adjacency matrix. According to the modeling method of the multi-mode public transport network, a network adjacency matrix is established. The urban public transport network is composed of public transport stations and lines, and is a typical complex network, the stations can be abstracted into nodes of the complex network, and the lines between adjacent stations can be abstracted into connecting edges of the complex network. According to the complex network modeling method, the urban public transport network can be abstracted into a topological graph. According to the connection relationship among the modes of the multi-mode public transport network, the multi-mode public transport network can be divided into an intra-layer connection mode and an inter-layer connection mode. Wherein, the intra-layer connection is the connection of each adjacent station in the same mode, if there is a bus line between two adjacent stations, the adjacency relation can be expressed as aroute1 is ═ 1; the interlayer connection is the transfer edge between different modes,constructing a buffer area by taking a subway network station as a circle center and taking 500 meters as a radius, connecting all bus stations in the buffer area with the subway station by transfer sides, wherein the adjacency relation can be expressed as avirtual1. In addition to the above two relationships, the adjacency between adjacent stations is 0.
Thus, the urban public transport network can be represented by G (V, E), V being the set of public transport stations, E being the set of public transport network edges, G being an N × N matrix with 0 or 1, which can be represented by A.
The specific construction formula of the multi-mode public transport network adjacency matrix is as follows:
in the formula, ai,jRepresenting the connection relationship between the multi-mode public transportation network stations; i, j denote multi-mode public transport network stations; a isroute1 indicates that a connecting edge exists between the connected stations in the same layer; a isvirtual1 indicates that a connection relationship exists between layers.
The adjacency matrix a between the stations of a particular multimode public transportation network may be represented as follows:
wherein N represents the number of multi-mode public transportation network nodes.
Step S2: calculating a topological importance index of the multi-mode public transport network;
(1) the node degree of the multi-mode public transport network calculates extremely heterogeneous weight. Calculating the number of adjacent nodes aiming at any node in the multi-mode public transport network and using kiIs shown, i.e.
Ai.jAre elements in the adjacency matrix a. Due to the difference of network structures, the small nodes in the network have strong transmission capacity and large nodesSince the point intermediary capability is strong, even if two points have paths with the same length, the transfer capabilities are very different, and to compensate for this defect, the node degree heterogeneous is further performed, that is, the square of the mean value of the node degrees and the square of the mean value of the node degrees, as shown in the following formula:
in the formula, HkiRepresenting the heterogeneous degree of the multi-mode public transport network website degree;<ki 2>a mean value representing the square of the station degrees of the multi-mode public transportation network;<ki>2representing the square of the mean of the multi-mode mass transit network sites.
(2) And calculating the H-index of the multi-mode public transport network and the heterogeneous weight of the H-index. The node H-index in the network can be calculated according to the node neighbor degree, and the specific calculation is as follows: the degree of the neighbors of the multi-mode public transport network node i is sequentially
And calculating the H-index of the specific node i:
in the formula, hiH-index representing a multimode public transport network node i;
the values of n neighbors of the node i are represented, and H represents the sum of the n neighbors which is not less than a certain value.
And consistent with the step 2, further performing H-index isomerism on the nodes, specifically as follows:
in the formula, HhiRepresenting the heterogeneous degree of the multi-mode public transport network site H-index;<hi 2>representing the mean of the squares of the H-index of the multi-mode public transport network station;<hi>2representing the square of the mean of the multi-mode mass transit network stations H-index.
(3) And considering node structure importance indexes influenced by node information propagation weak relation. In order to suppress nodes with larger values in the network, a penalty factor alpha is adopted for constraint, and a calculation formula of the importance of the structural level of the network node specifically considering the weak relationship is as follows:
in the formula, S represents the structural importance of the multi-mode public transport network node considering the weak relationship influence; | E | represents the number of multi-mode public transportation network edges; alpha represents a penalty factor, because different networks have different topological structures, the optimal alpha value can be obtained according to specific network training, and the specific calibration process is as follows:
1) dividing the network edge set E into 90% training sets ET10% of test set EPIf E is equal to ET+EP,
Simultaneous definition of
The data set is randomly divided 10 times in calculation, which is the complement of E.
2) According to the node influence calculation model considering the weak relation, S which is the possibility of the occurrence of the connecting edge between two points is calculatedi,j＝Si+SjIn the formula Si,SjIs the influence of node i, j, Si,jIs the probability value of an edge. First, at ETTraining the model according to the probability value of the continuous edge, and then randomly selecting EPAnd
each one of them is extracted and their likelihood values are compared, if from EPIs greater than the edge connecting probability value from
And (4) adding 1 to the count if the number of the edges is equal to the number of the edges, and adding 0.5 to the count if the number of the edges is equal to the number of the edges, otherwise, adding 0 to the count. Therefore, the model accuracy can be expressed in AUC as follows:
wherein n is the number of times of extraction, and n' is derived from EPIs greater than the edge connecting probability value from
The run-to-run probability value of (1); n' represents a group derived from EPIs equal to the edge connecting probability value from
The edge connection probability value of (1).
3) And (3) sequentially bringing the value of alpha into the model from [ -1,1] according to the interval of 0.01, calculating the precision of the model, and obtaining the optimal alpha value when the precision of the model is the highest.
Step S3: acquiring the passenger flow of the multi-mode public transport network station according to the multi-mode public transport card swiping data;
the multi-mode public transportation network station passenger flow is one of the key factors for constructing the multi-mode public transportation network. In order to depict the passenger flow attribute of the station, the passenger flow volume of each station of the multi-mode public transport network is extracted and normalized through the card swiping data of the multi-mode public transport network, and the method specifically comprises the following steps:
in the formula, Ψ represents the normalized site passenger volume; x is the number of1,x2,...,xnRepresenting the amount of ingress and egress of the multimode public transportation network station 1 to station n.
Step S4: and (3) according to the calculation indexes of the steps 2 and 3, considering the influence of the weak relationship of the nodes, and constructing a multi-mode public transport network key station identification method based on local heterogeneous influence.
The method for identifying the key station of the multi-mode public transport network based on the local heterogeneous influence of the nodes is creatively constructed by exploring the defects of a single network topology index in the research of the importance of the nodes, combining the meaning of the multi-mode public transport network topology structure index and the station passenger flow and considering the weak relation influence in the node information transmission process.
According to the influence factors which influence the identification of the key stations of the multi-mode public transport network and are obtained in the steps 1, 2 and 3, a multi-mode public transport network key station identification model theta based on local influence is constructedSHIPThe following were used:
in the formula, thetaSHIPRepresenting the local influence of the multi-mode public transportation network node.
Step S5: according to the model, the local influence of each node of the multi-mode public transport network is calculated, the nodes are ranked from large to small according to the influence, and the higher the ranking is, the more important the position of the node in the network is, namely the key node in the network is.
Claims (1)
1. A multi-mode public transport network key station identification method based on local heterogeneous influence is characterized by comprising the following operation steps:
step S1: constructing a multi-mode public transport network adjacency matrix;
(1) establishing a basic database of the multi-mode public transport network; the basic data comprises ground public transport network information and rail transit network information; the ground public transportation information comprises public transportation line information data and stop information data; the rail transit network information comprises rail transit line information data and station information data;
(2) removing the duplication of the multi-mode public transport network station; stations in different modes are numbered in sequence, and the same stations of different lines are combined into a station number;
(3) constructing a multi-mode public transport adjacency matrix; establishing a network adjacency matrix according to a multi-mode public transport network modeling method; the urban public transport network consists of public transport stations and lines and is a typical complex network, the stations are abstracted into nodes of the complex network, and the lines between adjacent stations are abstracted into connecting edges of the complex network; abstracting an urban public transport network into a topological graph according to a complex network modeling method; according to the connection relation among all modes of the multi-mode public transport network, the multi-mode public transport network can be divided into an intra-layer connection mode and an inter-layer connection mode; wherein, the in-layer connection is the connection of each adjacent station between the same mode, if a bus line exists between the two adjacent stations, the adjacency relation is expressed as aroute1 is ═ 1; the interlayer connection is a transfer edge among different modes, a buffer area is constructed by taking a subway network station as a circle center and taking 500 meters as a radius, all bus stations in the buffer area are connected with the subway station by the transfer edge, and the adjacency relation is represented as avirtual1 is ═ 1; in addition to the above two relationships, the adjacency relationship between adjacent sites is 0;
thus, the urban public transport network is denoted by G (V, E), V is the set of public transport stations, E is the set of public transport network edges, G is an NxN matrix with 0 or 1, denoted by A;
the specific construction formula of the multi-mode public transport network adjacency matrix is as follows:
in the formula, ai,jRepresenting the connection relationship between the multi-mode public transportation network stations; i, j denote multi-mode public transport network stations; a isroute1 denotes the storage between the inter-peer connected stationsConnecting edges; a isvirtual1 represents that connection relation exists between layers;
the adjacency matrix a between the stations of a particular multimode public transportation network is represented as follows:
wherein N represents the number of nodes of the multi-mode public transport network;
step S2: calculating a topological importance index of the multi-mode public transport network;
(1) calculating extremely heterogeneous weight by the node degree of the multi-mode public transport network; calculating the number of adjacent nodes aiming at any node in the multi-mode public transport network and using kiIs shown, i.e.
Ai.jIs an element in the adjacency matrix A; due to the difference of network structures, the small nodes in the network have strong transmission capability and the large nodes have strong intermediary capability, so that even if two points have paths with the same length, the transmission capability has very large difference, and in order to make up for the defect, the degree isomerism of the nodes is further performed, that is, the square mean value of the node degree values and the square mean value of the node degree values are represented by the following formula:
in the formula, HkiRepresenting the heterogeneous degree of the multi-mode public transport network website degree;<ki 2>a mean value representing the square of the station degrees of the multi-mode public transportation network;<ki>2representing the square of the mean of the multi-mode public transportation network stations;
(2) calculating H-index of the multi-mode public transport network and heterogeneous weight of the H-index; the node H-index in the network is calculated according to the node neighbor degree, and the calculation is as follows: neighborhood of multi-mode public transport network node iThe degree of residence is sequentially
And calculating the H-index of the specific node i:
in the formula, hiH-index representing a multimode public transport network node i;
representing the values of n neighbors of the node i, wherein H represents the summation of the number of the neighbors not less than a certain value;
and consistent with the step 2, further performing H-index isomerism on the nodes, specifically as follows:
in the formula, HhiRepresenting the heterogeneous degree of the multi-mode public transport network site H-index;<hi 2>representing the mean of the squares of the H-index of the multi-mode public transport network station;<hi＞2representing the square of the mean of the H-index of the multi-mode public transport network station;
(3) considering node structure importance indexes influenced by node information propagation weak relation; in order to suppress nodes with larger values in the network, a penalty factor alpha is adopted for constraint, and a calculation formula of the importance of the structural level of the network node specifically considering the weak relationship is as follows:
in the formula, S represents the structural importance of the multi-mode public transport network node considering the weak relationship influence; | E | represents the number of multi-mode public transportation network edges; alpha represents a penalty factor, the optimal alpha value is obtained according to specific network training, and the specific process is as follows:
1) dividing the network edge set E into 90% training sets ET10% of test set EPIf E is equal to ET+EP,
Simultaneous definition of
The data set is a complementary set of E, and the data set is randomly divided for 10 times during calculation;
2) according to the node influence calculation model considering the weak relation, S which is the possibility of the occurrence of the connecting edge between two points is calculatedi,j＝Si+SjIn the formula Si,SjIs the influence of node i, j, Si,jA likelihood value of an edge; first, at ETTraining the model according to the probability value of the continuous edge, and then randomly selecting EPAnd
each one of them is extracted and their likelihood values are compared, if from EPIs greater than the edge connecting probability value from
If the number of the edges is equal to the number of the edges, adding 1 to the counting number, if the number of the edges is equal to the number of the edges, adding 0.5 to the counting number, and otherwise, adding 0 to the counting number; therefore, the model accuracy is expressed by AUC as follows:
wherein n is the number of times of extraction, and n' is derived from EPIs greater than the edge connecting probability value from
The run-to-run probability value of (1); n' represents a group derived from EPIs equal to the edge connecting probability value from
The run-to-run probability value of (1);
3) sequentially bringing the value of alpha from [ -1,1] to the model according to the interval of 0.01, calculating the precision of the model, and obtaining the optimal alpha value when the precision of the model is the highest;
step S3: acquiring the passenger flow of the multi-mode public transport network station according to the multi-mode public transport card swiping data;
the passenger flow of the multi-mode public transport network station is one of key factors for constructing the multi-mode public transport network; in order to depict the passenger flow attribute of the station, the passenger flow volume of each station of the multi-mode public transport network is extracted and normalized through the card swiping data of the multi-mode public transport network, and the method specifically comprises the following steps:
in the formula, Ψ represents the normalized site passenger volume; x is the number of1,x2,...,xnRepresenting the number of the station-in and station-out of the multimode public transportation network station 1 to station n;
step S4: according to the calculation indexes in the steps 2 and 3, considering the influence of weak node relation, and constructing a multi-mode public transport network key station identification method based on local heterogeneous influence;
the method has the advantages that the defects of single network topological index in node importance research are explored, the meaning of the topological structure index of the multi-mode public transport network and the passenger flow of a station are combined, the weak relation influence in the node information transmission process is considered, and the multi-mode public transport network key station identification method based on the local heterogeneous influence of the node is creatively constructed;
according to the influence factors which influence the identification of the key stations of the multi-mode public transport network and are obtained in the steps 1, 2 and 3, a multi-mode public transport network key station identification model based on local influence is constructed
The following were used:
in the formula (I), the compound is shown in the specification,
representing the local influence of the multi-mode public transport network node;
step S5: and calculating the local influence of each node of the multi-mode public transport network, and sequencing the nodes from large to small according to the influence, wherein the higher the sequencing is, the more important the position of the node in the network is, namely the key node in the network.
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| CN116701495A (en) * | 2023-08-07 | 2023-09-05 | 南京邮电大学 | Subway-bus composite network key station identification method |
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