Railway axle-spoke type network construction method and related equipment

Abstract

The invention relates to the technical field of railway network construction, in particular to a railway axle-spoke type network construction method and related equipment. The method comprises the following steps: acquiring node information of a plurality of railway nodes and average travel time among the railway nodes; the node information comprises one or a combination of more of gathering capability information, service level information, facility equipment information and development potential information; determining a node score of each railway node according to the node information of the railway nodes; determining the node type of the railway node according to the node score; the node types comprise hub nodes and spoke nodes; determining the attraction range of the hub node to the spoke node according to the node information and the average travel time between the railway nodes; and constructing the railway axle-spoke network based on the attraction range of the hub nodes to the spoke nodes.

Description

Technical Field

[0001] The present invention relates to the technical field of railway network construction, and particularly relates to a method for constructing a railway hub-and-spoke network and related equipment. Background Art

[0002] As one of the representative modes of long-distance transportation, railway passenger travel has the characteristics of long travel distance, a lot of luggage carried, and difficulty in getting on and off the train. Railway stations have an obvious effect of hierarchical concentration and dispersion of passenger flow. The high-speed rail passenger transport network is gradually changing from the traditional "point-to-point" structure to the "hub-and-spoke" structure within the region, showing the characteristic that passenger flows in all directions within the region gather at some stations and then disperse to other stations. An important factor for not applying the hub-and-spoke network to the high-speed rail field currently is that the connections between nodes in the high-speed rail network are distributed in a "band" rather than a "point" shape. Compared with air transportation and logistics transportation, there are many intermediate stations between two high-speed rail hubs, which does not conform to the typical "point-to-point" transportation characteristics. Therefore, the hub-and-spoke network division methods in fields such as aviation and logistics cannot be applied to railways. Summary of the Invention

[0003] In view of this, the present application provides a method for constructing a railway hub-and-spoke network and related equipment, realizing the division of the railway into a hub-and-spoke network.

[0004] In a first aspect, an embodiment of the present invention provides a method for constructing a railway hub-and-spoke network, including:

[0005] Obtaining node information of multiple railway nodes and the average travel time between the railway nodes; the node information includes one or a combination of aggregation ability information, service level information, facility and equipment information, and development potential information;

[0006] Determining a node score for each of the railway nodes according to the node information of the railway nodes;

[0007] Determining the node type of the railway nodes according to the node scores; the node types include hub nodes and spoke nodes;

[0008] Determining the attraction range of the hub nodes to the spoke nodes according to the node information and the average travel time between the railway nodes;

[0009] Constructing a railway hub-and-spoke network based on the attraction range of the hub nodes to the spoke nodes.

[0010] In a possible implementation, the node information includes multiple evaluation metrics. The evaluation metrics corresponding to the agglomeration ability information include: the passenger flow of the node and the annual growth rate of the passenger flow of the node; the evaluation metrics corresponding to the service level information include: the number of stations at the node, the number of connecting stations at the node, the number of train arrivals and departures at the node, and the number of connecting lines at the node; the evaluation metrics corresponding to the facility and equipment information include: the highest-level line connected to the node, the number of platforms at the node, the number of arrival and departure lines at the node, and the node capacity; the evaluation metrics corresponding to the development potential information include: the per capita gross domestic product, the total permanent population of the city where the node is located, and the per capita disposable income of the city where the node is located.

[0011] In a possible implementation, determining the node score of each railway node according to the node information of the railway node includes:

[0012] Calculating the node importance vector of each railway node based on the PageRank algorithm and the multiple evaluation metrics;

[0013] Determining the node score of each railway node according to the node importance vector.

[0014] In a possible implementation, calculating the node importance vector of each railway node based on the PageRank algorithm and the multiple evaluation metrics includes:

[0015] Performing dimensionless normalization processing on the multiple evaluation metrics to obtain multiple normalized metrics corresponding to the multiple evaluation metrics, and obtaining the dimensionless metrics of each railway node;

[0016] Obtaining the comprehensive passenger flow acquisition ability value of each railway node based on the multiple normalized metrics and the dimensionless metrics of each railway node;

[0017] Obtaining the passenger transfer probability between each railway node according to the comprehensive passenger flow acquisition ability value of each railway node;

[0018] Obtaining the node importance vector of each railway node according to the passenger transfer probability between each railway node.

[0019] In a possible implementation, obtaining the comprehensive passenger flow acquisition ability value of each railway node based on the multiple normalized metrics and the dimensionless metrics of each railway node includes:

[0020] Constructing a covariance matrix according to the multiple normalized metrics;

[0021] Determining the contribution rate of each normalized metric based on the covariance matrix;

[0022] Sort the normalized indicators in descending order according to the contribution rate to obtain a first sorting result;

[0023] Determine the first m normalized indicators in the first sorting result; wherein, the sum of the contribution rates of the m normalized indicators is greater than or equal to a first threshold;

[0024] Obtain the comprehensive flow acquisition ability value of each railway node according to the values of the m normalized indicators of the railway node and the dimensionless indicators of each railway node.

[0025] In a possible implementation manner, the hub node includes a first-level hub node and a second-level hub node, and determining the node type of the railway node according to the node score includes:

[0026] Sort the railway nodes in descending order according to the node score to obtain a second sorting result;

[0027] Determine the first n railway nodes in the second sorting result as the first-level hub nodes, and determine the (n + 1)-th railway node to the m-th railway node as the second-level hub nodes; wherein, m > n + 1, and m < the total number of railway nodes;

[0028] Determine the railway nodes after the m-th railway node in the second sorting result as the spoke nodes.

[0029] In a possible implementation manner, constructing a railway hub-and-spoke network based on the attraction range of the hub node to the spoke node includes:

[0030] Determine the axis according to the high-speed railway lines between the hub nodes;

[0031] Determine the spoke network according to the high-speed railway lines between the hub node and the spoke nodes within the corresponding attraction range;

[0032] Obtain the railway hub-and-spoke network according to the hub node, the spoke node, the axis, and the spoke network.

[0033] In a second aspect, an embodiment of the present invention provides a device for constructing a railway hub-and-spoke network, including:

[0034] An acquisition module, configured to acquire node information of multiple railway nodes; the node information includes one or a combination of multiple items of agglomeration ability information, service level information, facility and equipment information, and development potential information;

[0035] A scoring module, configured to determine the node score of each railway node according to the node information of the railway node;

[0036] A node type determination module, configured to determine the node type of the railway node according to the node score; the node type includes a hub node and a spoke node;

[0037] An attraction range determination module, configured to determine the attraction range of the hub node to the spoke node according to the node information and the average travel time between the railway nodes;

[0038] A construction module, configured to construct a railway hub-and-spoke network based on the attraction range of the hub node to the spoke node.

[0039] In a third aspect, an embodiment of the present invention provides an electronic device, including:

[0040] At least one processor; and

[0041] At least one memory communicatively connected to the processor, wherein:

[0042] The memory stores program instructions executable by the processor, and the processor can execute the method described in the first aspect by invoking the program instructions.

[0043] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where the computer-readable storage medium stores computer instructions, and the computer instructions cause the computer to execute the method described in the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0045] Figure 1 is a flowchart of a method for constructing a railway hub-and-spoke network provided by an embodiment of the present invention;

[0046] Figure 2 is a schematic diagram of the visualization result of the attraction range provided by an embodiment of the present invention;

[0047] Figure 3 is a schematic diagram of a railway hub-and-spoke network provided by an embodiment of the present invention;

[0048] Figure 4 is a schematic diagram of the structure of a device for constructing a railway hub-and-spoke network provided by an embodiment of the present invention;

[0049] Figure 5 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] To better understand the technical solution of the present application, the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0051] It should be clear that the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the scope of protection of the present application.

[0052] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms of "a", "the" and "said" used in the embodiments of the present application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0053] It should be understood that the term " / and / " used herein is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " herein generally represents an "or" relationship between the associated objects before and after.

[0054] To solve the problem that the partitioning method of the hub-and-spoke network in the aviation and logistics fields cannot be applied to the railway field in the prior art, the embodiments of the present invention provide a method for constructing a railway hub-and-spoke network, which can realize the partitioning of the railway into a hub-and-spoke network. Figure 1 This is a flowchart of a method for constructing a railway hub-and-spoke network provided by the embodiments of the present invention. As Figure 1 shown, the method includes:

[0055] Step 101, obtain the node information of multiple railway nodes and the average travel time between the railway nodes. The node information includes one or a combination of multiple items such as agglomeration ability information, service level information, facility and equipment information, and development potential information.

[0056] Among them, the railway node specifically refers to a railway station. A city can have multiple railway nodes. For example, for Beijing, it has multiple railway stations such as Beijing Station, Beijing West Station, Beijing South Station, Beijing North Station, and Beijing Fengtai Station, and each railway station is an independent railway node. By obtaining the information of the railway node in four dimensions of agglomeration ability, service level, facility and equipment, and development potential, it is used to calculate the node score of the railway node.

[0057] The average travel time between railway nodes can be obtained by collecting multiple actual travel times between two railway nodes and taking the average. When there is a direct railway connection between railway node A and railway node B, the average travel time between railway node A and railway node B can be obtained by collecting the actual times of multiple trains traveling from railway node A to railway node B and taking the average. If there is no direct railway connection between railway node A and railway node B and a transfer through railway node C is required, the average travel time between railway node A and railway node C can be obtained by collecting the actual times of multiple trains traveling from railway node A to railway node C and taking the average. Also, the average travel time between railway node C and railway node B can be obtained by collecting the actual times of multiple trains traveling from railway node C to railway node B and taking the average. Then, by adding the average travel time between railway node A and railway node C and the average travel time between railway node C and railway node B, the average travel time between railway node A and railway node B can be obtained.

[0058] In some embodiments, the node information of railway nodes includes multiple evaluation indicators, and the railway nodes are quantitatively scored based on the specific values of the respective evaluation indicators of each railway node. Specifically, the evaluation indicators corresponding to the agglomeration ability information include: node passenger flow (X 1 / person-times) and the annual growth rate of node passenger flow (X 2 / %). The evaluation indicators corresponding to the service level information include: the number of node stations (X3 / units), the number of connected stations of the node (X 4 / units), the number of train arrivals and departures at the node (X 5 / trips), and the number of connected lines of the node (X 6 / lines). The evaluation indicators corresponding to the facility and equipment information include: the highest-level connected line of the node (X 7 ), the number of platforms at the node (X 8 / units), the number of arrival and departure lines at the node (X 9 / lines), and the node capacity (X 10 / person-times). The evaluation indicators corresponding to the development potential information include: per capita GDP (X 11 / 10,000 yuan), the total permanent population of the city where the node is located (X 12 / 10,000 people), and the per capita disposable income of the city where the node is located (X 13 / 10,000 yuan).

[0059] By obtaining the information of the above 13 evaluation indicators X 1 ~X 13 , the node score of each railway node is calculated, thereby realizing the quantitative evaluation of railway nodes. Among them, the calculation method of the above X 2 is: X2 = (Total passenger flow of railway nodes in the past year - Total passenger flow in the base year) / (Total passenger flow in the base year) × 100%. And X 7 needs to be specifically quantified. Specifically, the current speed grades of railway lines mainly include 350 km / h, 300 km / h, 250 km / h, and 200 km / h. The train service levels represented by lines of different speed grades are different. Taking a 10-point system as an example, a line with a speed of 350 km / h can correspond to 10 points. A line with a speed of 300 km / h can correspond to 8 points, a line with a speed of 250 km / h can correspond to 4 points, and a line with a speed of 200 km / h can correspond to 2 points, so as to quantify the score of the node connecting the highest-level line. Except for X 2 and X 7 and others, the rest of the evaluation indicators can be directly obtained by counting each node.

[0060] In this embodiment, through the comprehensive quantification of indicators such as the node line level, node passenger flow, and node accommodation capacity of the node, the quantitative scoring of the railway node is realized, providing a basis for dividing the node type of the railway node.

[0061] Step 102, determine the node score of each railway node according to the node information of the railway node.

[0062] In some embodiments, the specific values of the above 13 evaluation indicators for each railway node can be weighted and calculated to obtain the node score of each railway node.

[0063] Step 103, determine the node type of the railway node according to the node score. The node types include hub nodes and spoke nodes.

[0064] Among them, the railway nodes can be sorted in descending order of the node score, and then the first n railway nodes in the sorting are determined as hub nodes (i.e., axis nodes), and the remaining railway nodes are determined as spoke nodes.

[0065] Optionally, the hub nodes can also be further divided into first-level hub nodes and second-level hub nodes, so as to make the node types more abundant and the finally obtained railway axis-spoke network more accurate. Then the node types are in descending order of node level: First-level hub node > Second-level hub node > Spoke node. Specifically, when dividing, the railway nodes can be sorted in descending order of the node score to obtain the second sorting result. Then the first n railway nodes in the second sorting result are determined as first-level hub nodes, and the (n + 1)-th railway node to the m-th railway node are determined as second-level hub nodes. Among them, m > n + 1 and m < the total number of railway nodes. Finally, the railway nodes after the m-th railway node in the second sorting result are determined as spoke nodes.

[0066] Step 104: Determine the attraction range of the hub node to the spoke node according to the node information and the average travel time between railway nodes.

[0067] Among them, the attraction range refers to the lower-level nodes (i.e., spoke nodes) that can be attracted by the upper-level node (i.e., the hub node). Among them, the attraction ability of each upper-level node to itself can be determined from the perspective of the lower-level node, so as to obtain the upper-level node with the maximum attraction ability corresponding to each spoke node, and then obtain the upper-level node to which each lower-level node belongs. In this way, the attraction range of each level of nodes can be obtained.

[0068] In some embodiments, for the scenario where the hub nodes are divided into primary hub nodes and secondary hub nodes. For the secondary hub nodes, the attraction ability of each primary hub node to itself can be determined according to the node information and the average travel time between railway nodes, and then the primary hub node with the maximum attraction ability is determined as the node to which the secondary hub node belongs. For the spoke nodes, the attraction ability of each primary hub node and secondary hub node to itself can be determined according to the node information and the average travel time between railway nodes, and then the primary hub node or secondary hub node with the maximum attraction ability is determined as the node to which the spoke node belongs.

[0069] Step 105: Construct a railway hub-and-spoke network based on the attraction range of the hub node to the spoke node.

[0070] Among them, the axis can be determined first according to the high-speed railway lines between the hub nodes. Then, the spoke network can be determined according to the high-speed railway lines between the hub node and the spoke nodes within the corresponding attraction range. Finally, the railway hub-and-spoke network is obtained according to the hub nodes, spoke nodes, axis, and spoke network. Optionally, if the hub nodes are divided into primary hub nodes and secondary hub nodes, the primary axis can be obtained based on the connection of the high-speed railway lines between the primary hub nodes and other primary hub nodes, and the secondary axis can be obtained from the connection of the high-speed railway lines between the primary hub nodes and the secondary hub nodes. The spoke network can be obtained from the connection of the high-speed railway lines between the primary hub nodes and each spoke node, and between the secondary hub nodes and each spoke node. If the hub nodes are connected by the same railway line, these hub nodes belong to the same axis, and the other nodes along the line also belong to this axis. If there are multiple railway lines between the hub nodes, the railway line with the shortest average travel time can be selected as the main axis. Accordingly, the division of the railway hub-and-spoke network can be realized.

[0071] In some embodiments, the railway network consists of stations and the connection relationships between them. The process of passengers moving from one station to another is similar to the mutual links between web pages. Based on this feature, this embodiment improves the classic PageRank algorithm to achieve railway network construction. Among them, the node importance vector of each railway node can be calculated first based on the PageRank algorithm and the above-mentioned multiple evaluation indicators. The recurrence calculation formula of the PageRank algorithm is: PR = (1 - d)E + dM·PR. Where PR is the node importance vector. M is the passenger transfer matrix, which represents the probability of passengers flowing from the current railway node to other railway nodes. d is the damping factor, generally taking a value of 0.85. E is the passenger random jump matrix. This matrix is a 01 matrix. In this matrix, if the current railway node can directly reach other railway nodes, the value is 1. If it cannot directly reach, the value is 0.

[0072] All railway nodes initially have the same PR value. After multiple iterations, the PR value will converge. Nodes with larger PR values indicate a more important position in the hub-and-spoke network. Therefore, after calculating the node importance vector of each railway node, the node importance vector is determined as the node score of each railway node.

[0073] In the above embodiment, the core of the PageRank algorithm is the passenger transfer matrix M. The essence of the passenger transfer matrix M is the probability of passenger flow transferring from one railway node to another. And the stronger the comprehensive ability of a railway node to attract passenger flow, the higher the transfer probability between this railway node and other railway nodes. Therefore, in order to obtain the passenger transfer matrix M, it is necessary to first quantify the comprehensive passenger flow attraction ability of railway nodes. Based on the data analysis and empirical observation of railway nodes, if a railway node has stronger aggregation ability, higher service level, more complete facilities and equipment, and greater development potential, it indicates that the railway node has stronger comprehensive ability to attract passenger flow. Therefore, in the embodiments of the present invention, the comprehensive passenger flow acquisition ability of railway nodes is judged through the above 13 evaluation indicators.

[0074] First, each evaluation indicator is distributed in multiple dimensions, and there may be a strong correlation between different evaluation indicators. For example, the per capita GDP of X 11 and X 13There is a strong correlation with the per capita disposable income. If the weighted algorithm is directly used to calculate the weights of the above 13 evaluation indicators, the weighted result may be inaccurate. Therefore, it is possible to first perform dimensionless normalization processing on multiple evaluation indicators to obtain multiple normalized indicators corresponding to the multiple evaluation indicators, as well as the dimensionless indicators of each railway node. After that, based on the multiple normalized indicators and the dimensionless indicators of each railway node, the comprehensive flow acquisition ability value of each railway node is obtained. Finally, according to the comprehensive flow acquisition ability value of each railway node, the passenger transfer probability between each railway node is obtained. And according to the passenger transfer probability between each railway node, the node importance vector of each railway node is obtained.

[0075] Among them, the maximum-minimum method (Max-Min method) can be used to perform dimensionless normalization processing on the 13 evaluation indicators Xi (X 1 ~X 13 ), and convert them into xi (i.e., the normalized indicators), where i = 1, 2, 3...P. Through the dimensionless normalization processing, the dimensionless indicators of each railway node can also be obtained. Among them, for the jth evaluation indicator of railway node K, its dimensionless indicator can be obtained through formula (1)

[0076] Formula (1): xk(j) = x kj / (x jmax - x jmin ).

[0077] Among them, x kj is the actual value of the jth evaluation indicator of railway node K, x jmax is the maximum value of the jth evaluation indicator among all sampled railway nodes. x jmin is the minimum value of the jth evaluation indicator among all sampled railway nodes. Then for railway node K, a dimensionless indicator can be calculated for each evaluation indicator. Since the above evaluation indicators are 13, 13 dimensionless indicators of the railway node under 13 evaluation indicators can be obtained, which are xk(1)~xk(13) respectively.

[0078] After that, in order to eliminate the influence of the correlation between indicators, a covariance matrix can be constructed based on multiple normalized indicators. Then, based on the covariance matrix, the contribution rate of each normalized indicator is determined. Then, the normalized indicators are sorted in descending order of the contribution rate to obtain the first sorting result. And the first m normalized indicators in the first sorting result are determined. Among them, the sum of the contribution rates of the m normalized indicators is greater than or equal to the first threshold. Finally, according to the m evaluation indicators of the railway node and the dimensionless indicators of each railway node, the comprehensive flow acquisition ability value of each railway node is obtained.

[0079] Specifically, after constructing the covariance matrix, the correlation coefficients between the normalized indicators can be calculated based on the covariance matrix. Specifically, the correlation coefficient r between the normalized indicator i and the normalized indicator j can be calculated using Equation (2). ij .

[0080]

[0081] In the above equation, N is the total number of railway nodes. x ki is the value of the i-th normalized indicator of the k-th railway node. x kj is the value of the j-th normalized indicator of the k-th railway node. refers to the average value of the i-th normalized indicator. refers to the average value of the j-th normalized indicator.

[0082] After obtaining the correlation coefficient r between the normalized indicators ij , the contribution rate c of each normalized indicator can be calculated using Equation (3). j .

[0083]

[0084] Among them, λ j is the eigenvalue of the above covariance matrix.

[0085] After calculating the contribution rate of each normalized indicator, the normalized indicators are sorted in descending order of the contribution rate, and then the contribution rates are accumulated according to the sorting result until the sum of the accumulated contribution rates is greater than or equal to the first threshold and then the accumulation stops. Among them, the first threshold can take a value of 0.9. Alternatively, the specific value of the first threshold can be determined according to actual needs. After stopping the accumulation, m normalized indicators that have been accumulated are obtained. The comprehensive flow acquisition ability value z of each railway node can be obtained by multiplying the values of the railway node in the m normalized indicators and the dimensionless indicators of the railway node in the m normalized indicators and then summing them up. k . Specifically, z k can be calculated using Equation (4).

[0086]

[0087] For example, there are 4 railway nodes, namely a, b, c, and d. m is 2, namely the passenger flow of the node and the number of platforms of the node. The value of the passenger flow of the node of railway node a after dimensionless normalization is c 1 (a), and the value of the number of platforms of the node is c 8 (a). The value of the passenger flow of the node of railway node b after dimensionless normalization is c 1(b), the value of the number of node platforms is c 8 (b). The value of the node passenger flow of railway node c after dimensionless normalization is c 1 (c), the value of the number of node platforms is c 8 (c). The value of the node passenger flow of railway node d after dimensionless normalization is c 1 (d), the value of the number of node platforms is c 8 (d).

[0088] The dimensionless index of the node passenger flow of railway node a is x a (1), and the dimensionless index of the number of node platforms is x a (8). The dimensionless index of the node passenger flow of railway node b is x b (1), and the dimensionless index of the number of node platforms is x b (8). The dimensionless index of the node passenger flow of railway node c is x c (1), and the dimensionless index of the number of node platforms is x c (8). The dimensionless index of the node passenger flow of railway node d is x d (1), and the dimensionless index of the number of node platforms is x d (8).

[0089] Then the comprehensive flow acquisition ability value of railway node a is: c 1 (a)×x a (1)+c 8 (a)×x a (8). The comprehensive flow acquisition ability value of railway node b is: c 1 (b)×x b (1)+c 8 (b)×x b (8). The comprehensive flow acquisition ability value of railway node c is: c 1 (c)×x c (1)+c 8 (c)×x c (8). The comprehensive flow acquisition ability value of railway node d is: c 1 (d)×x d (1)+c 8 (d)×x d (8).

[0090] In some embodiments, the calculated comprehensive flow acquisition ability value z k may be negative, while each element in the passenger transfer matrix M is non - negative. Therefore, it is necessary to process the obtained comprehensive flow acquisition ability value z kIf a transformation is carried out, then for railway node k, the probability that its passenger flow transfers to railway node t is as shown in formula (5).

[0091]

[0092] Among them, δ(k, t) indicates whether there is a direct railway connection between railway node k and railway node t. If there is a direct connection, the value of δ(k, t) is 1. If there is no direct connection, the value of δ(k, t) is 0.

[0093] After obtaining the transfer probabilities between each railway node through formula (5), the passenger transfer matrix M can be obtained. Substitute the obtained passenger matrix M into the recurrence calculation formula of the PageRank algorithm for iterative calculation until convergence. The PR values of each railway node can be obtained. After obtaining the PR values of each railway node, the railway nodes can be divided into first-level hub nodes, second-level hub nodes, and radiation nodes.

[0094] After completing the division of node types, it is necessary to determine the attraction range of the upper-level node to the lower-level node. Specifically, the attraction value between the upper-level node i and the lower-level node j can be calculated by formula (6) in an analogous way to universal gravitation.

[0095]

[0096] Among them, z i is the comprehensive passenger acquisition ability value of the upper-level node i, and z j is the comprehensive passenger acquisition ability value of the lower-level node j. t ij is the average travel time between the upper-level node i and the lower-level node j. When the upper-level node i is a first-level hub node, the lower-level node j can be a second-level hub node or a radiation node. When the upper-level node i is a second-level hub node, the lower-level node j is a radiation node.

[0097] Calculate the attraction value between nodes through the above formula (6), so as to obtain the upper-level node with the maximum attraction corresponding to each lower-level node. Furthermore, the attraction range of each level of node can be obtained. Finally, according to the division result of node types and the attraction range of each level of node, the division of the railway hub-and-spoke network can be realized.

[0098] Taking the construction of the railway hub-and-spoke network for the high-speed rail network in Northeast China as an example, all stations are classified and merged according to urban nodes, and finally 137 railway nodes are determined as the nodes of the hub-and-spoke network in the Northeast Railway Region. According to the actual investigation and data sampling results, and the public data of each railway node, the evaluation index data (X 1 ~X 13 ) and the average travel time t ij between railway nodes are sorted out.

[0099] After completing data sampling, calculate the comprehensive current acquisition capacity of each railway node through the above steps, construct a passenger transfer matrix M based on the comprehensive current acquisition capacity, and substitute it into the recurrence calculation formula of the PageRank algorithm for iterative calculation to obtain the PR values of each railway node. Based on the obtained PR values, divide the 137 railway nodes into node types. The division results are as follows: Primary hub nodes: Shenyang, Harbin, Beijing, Changchun. Secondary hub nodes are: Mudanjiang, Tangshan, Xinmin North, Dunhua, Liaoning, Chaoyang, Linghai South, Kazuo, Yingkou East, Panjin North, Dalian North. The remaining railway nodes are spoke nodes.

[0100] After completing the division of railway node types, determine the attraction values between the superior nodes and the inferior nodes according to the above steps. For each spoke node, it will belong to the primary hub node or the secondary hub node with the greatest attraction to it. For each secondary hub node, it will belong to the primary hub node with the greatest attraction to it. The attraction range of the superior node consists of the superior node and the inferior nodes belonging to it. Figure 2 It is a schematic diagram of the visualization result of the attraction range provided by the embodiment of the present invention. As Figure 2 shown, the graph of the primary hub node is the largest, the graph of the secondary hub node is smaller, and the graph of the spoke node is the smallest. The names of the railway nodes are marked on both the primary hub node and the secondary hub node. The position of the spoke node is only marked by the graph.

[0101] Based on the above-divided node types and the attraction ranges of each level of nodes, combined with the railway connection relationships between railway nodes, the Figure 3 railway hub-and-spoke network shown in Figure 3 can be obtained. As shown in

[0102] The railway hub-and-spoke network provided by the embodiments of the present invention combines passenger flow aggregation, service level, facilities and equipment, and the development potential of the station city, and innovates the evaluation index system of railway network nodes. The hub-and-spoke network is constructed based on the PageRank algorithm, which not only considers the importance of the nodes themselves, but also takes into account the importance of the associated nodes, making the division of railway hub-and-spoke network nodes more reasonable and scientific. The method for constructing a railway hub-and-spoke network provided by the embodiments of the present invention fully considers the zonal distribution characteristics of railway line stations, constructs the axis with the shortest average travel time between the hub nodes, and at the same time the axis is connected with the spoke nodes along the way. Therefore, the constructed hub-and-spoke network is different from the existing aviation and logistics hub-and-spoke networks. This hub-and-spoke network has the characteristics of the railway network structure and can better support the needs of on-site technicians.

[0103] Corresponding to the above method for constructing a railway hub-and-spoke network, the embodiments of the present invention provide a device for constructing a railway hub-and-spoke network. Figure 4 The following is a schematic structural diagram of a device for constructing a railway hub-and-spoke network provided by the embodiments of the present invention. As Figure 4 shown in the figure, the device includes: an acquisition module 401, a scoring module 402, a node type determination module 403, an attraction range determination module 404, and a construction module 405.

[0104] The acquisition module 401 is configured to acquire node information of a plurality of railway nodes. The node information includes one or more combinations of aggregation ability information, service level information, facilities and equipment information, and development potential information.

[0105] The scoring module 402 is configured to determine a node score for each railway node according to the node information of the railway node.

[0106] The node type determination module 403 is configured to determine the node type of the railway node according to the node score. The node types include hub nodes and spoke nodes.

[0107] The attraction range determination module 404 is configured to determine the attraction range of the hub node to the spoke node according to the node information and the average travel time between railway nodes.

[0108] The construction module 405 is configured to construct a railway hub-and-spoke network based on the attraction range of the hub node to the spoke node.

[0109] Figure 4 The device for constructing a railway hub-and-spoke network provided by the shown embodiment can be used to execute Figures 1-3 the technical solution of the method embodiment shown. The implementation principle and technical effects can be further referred to the relevant descriptions in the method embodiment.

[0110] Figure 5 The following is a schematic structural diagram of an electronic device provided by the embodiments of the present invention. As Figure 5As shown, the above-mentioned electronic device may include at least one processor and at least one memory communicatively connected to the above-mentioned processor, where: the memory stores program instructions executable by the processor, and the above-mentioned processor can execute the present specification by invoking the above-mentioned program instructions Figures 1-3 The railway hub-and-spoke network construction method provided by the embodiment shown.

[0111] As Figure 5 As shown, the electronic device is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: one or more processors 510, a communication interface 520, and a memory 530, and a communication bus 540 connecting different system components (including the memory 530, the communication interface 520, and the processor 510).

[0112] The communication bus 540 represents one or more of several types of bus structures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any bus structure in a variety of bus structures. For example, these architectures include, but are not limited to, Industry Standard Architecture (hereinafter referred to as: ISA) bus, Micro Channel Architecture (hereinafter referred to as: MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (hereinafter referred to as: VESA) local bus, and Peripheral Component Interconnection (hereinafter referred to as: PCI) bus.

[0113] The electronic device typically includes a variety of computer system-readable media. These media can be any available media accessible by the electronic device, including volatile and non-volatile media, removable and non-removable media.

[0114] The memory 530 may include computer system-readable media in the form of volatile memory, such as random access memory (hereinafter referred to as: RAM) and / or cache memory. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. The memory 530 may include at least one program product having a set (for example, at least one) of program modules configured to perform the functions of the embodiments of the present specification.

[0115] A program / util utility having a set (at least one) of program modules can be stored in the memory 530. Such program modules include - but are not limited to - an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment. The program modules generally perform the functions and / or methods in the embodiments described in this specification.

[0116] The processor 510 executes various functional applications and data processing by running the programs stored in the memory 530, such as implementing the railway hub-and-spoke network construction method provided by the embodiments shown in this specification. Figures 1-3 The embodiments shown provide a railway hub-and-spoke network construction method.

[0117] The embodiments of this specification provide a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the railway hub-and-spoke network construction method provided by the embodiments shown in this specification. Figures 1-3 The embodiments shown provide a railway hub-and-spoke network construction method.

[0118] The embodiments of this specification provide a computer-readable storage medium. The computer-readable storage medium stores computer instructions that cause the computer to execute the railway hub-and-spoke network construction method provided by the embodiments shown in this specification. Figures 1-3 The embodiments shown provide a railway hub-and-spoke network construction method.

[0119] The above computer-readable storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example - but not limited to - an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (hereinafter referred to as ROM), an erasable programmable read-only memory (hereinafter referred to as EPROM) or flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0120] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the acts or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0121] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this specification. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0122] Furthermore, the terms "first" and "second" are used for descriptive purposes only and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of this specification, "a plurality of" means at least two, such as two, three, etc., unless otherwise specifically and explicitly defined.

[0123] Any process or method description in a flowchart or described otherwise herein can be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a customized logical function or process, and the scope of the preferred embodiments of this specification includes additional implementations, where the functions may be performed in a manner not shown or discussed, including in a substantially simultaneous manner according to the involved functions or in a reverse order, which should be understood by those skilled in the art to which the embodiments of this specification pertain.

[0124] Depending on the context, the word "if" as used herein can be interpreted as "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detecting (stated condition or event)" can be interpreted as "when determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)".

[0125] It should be noted that the devices involved in the embodiments of this specification may include, but are not limited to, personal computers (hereinafter referred to as PCs), personal digital assistants (hereinafter referred to as PDAs), wireless handheld devices, tablet computers, mobile phones, MP3 displays, MP4 displays, etc.

[0126] In several embodiments provided in this specification, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0127] In addition, in each embodiment of this specification, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of a combination of hardware and software functional units.

[0128] The above-mentioned integrated units implemented in the form of software functional units can be stored in a computer-readable storage medium. The above-mentioned software functional units stored in a storage medium include several instructions to enable a computer device (which can be a personal computer, a connector, or a network device, etc.) or a processor to execute some steps of the methods described in each embodiment of this specification. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (hereinafter referred to as ROMs), random access memories (hereinafter referred to as RAMs), magnetic disks, or optical discs that can store program codes.

[0129] The above is only the preferred embodiment of this specification and is not intended to limit this specification. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of this specification shall be included within the scope of protection of this specification.

[0130] For the same or similar parts among the various embodiments in this specification, reference can be made to each other. In particular, for the apparatus embodiments and terminal embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and reference can be made to the description in the method embodiments for the relevant parts.

Claims

1. A railway hub-and-spoke network construction method, characterized in that: include: Obtaining node information of a plurality of railway nodes and average travel time between the railway nodes; the node information includes one or more combinations of aggregation capacity information, service level information, facility and equipment information, and development potential information; Determine a node score of each railway node according to the node information of the railway node; Determining the node type of the railway node according to the node score; the node type includes a hub node and a spoke node; Determine the attraction range of the hub node to the spoke node according to the node information and the average travel time between the railway nodes; A railway hub-and-spoke network is constructed based on the attraction range of the hub node to the spoke nodes.

2. The method according to claim 1, characterized in that The node information includes multiple evaluation indicators. The evaluation indicators corresponding to the aggregation capacity information include: node passenger flow and annual growth rate of node passenger flow; the evaluation indicators corresponding to the service level information include: number of node stations, number of node connected stations, number of node train arrivals and departures, and number of node connected lines; the evaluation indicators corresponding to the facilities and equipment information include: the highest level line connected to the node, the number of node platforms, the number of node arrival and departure lines, and node capacity; the evaluation indicators corresponding to the development potential information include: per capita GDP, the total number of permanent residents in the city where the node is located, and the per capita disposable income of the city where the node is located.

3. The method according to claim 2, characterized in that The determining the node score of each railway node according to the node information of the railway node comprises: Calculating the node importance vector of each railway node based on the PageRank algorithm and the multiple evaluation indicators; The node score of each railway node is determined according to the node importance vector.

4. The method according to claim 3, characterized in that The calculating of the node importance vector of each railway node based on the PageRank algorithm and the multiple evaluation indicators includes: Performing dimensionless normalization processing on the multiple evaluation indicators to obtain multiple normalized indicators corresponding to the multiple evaluation indicators, and obtaining dimensionless indicators of each railway node; Obtaining a comprehensive flow acquisition capacity value of each railway node based on the multiple normalized indicators and the dimensionless indicators of each railway node; Obtaining the passenger transfer probability between each railway node according to the comprehensive flow acquisition capacity value of each railway node; The node importance vector of each railway node is obtained according to the passenger transfer probability between each railway node.

5. The method according to claim 4, characterized in that The comprehensive flow acquisition capacity value of each railway node is obtained based on the multiple normalized indicators and the dimensionless indicators of each railway node, including: constructing a covariance matrix according to the plurality of normalized indices; Determining the contribution rate of each normalized indicator based on the covariance matrix; Sorting the normalized indicators in descending order of contribution rate to obtain a first sorting result; Determine the first m normalized indicators in the first sorting result; wherein the sum of the contribution rates of the m normalized indicators is greater than or equal to a first threshold; The comprehensive flow acquisition capacity value of each railway node is obtained according to the values ​​of the m normalized indicators of the railway node and the dimensionless indicator of each railway node.

6. The method according to claim 1, characterized in that The hub node includes a primary hub node and a secondary hub node, and the node type of the railway node is determined according to the node score, including: Sorting the railway nodes in descending order of the node scores to obtain a second sorting result; Determine the first n railway nodes in the second sorting result as the first-level hub nodes, and determine the n+1th to mth railway nodes as the second-level hub nodes; wherein m>n+1, and m<the total number of railway nodes; The railway nodes after the m-th railway node in the second sorting result are determined as the spoke nodes.

7. The method according to claim 1, characterized in that The construction of a railway hub-and-spoke network based on the attraction range of the hub node to the spoke node includes: Determine the axis according to the high-speed rail lines between the hub nodes; Determine the spoke network based on the high-speed rail lines between the hub node and the spoke nodes within the corresponding attraction range; The railway hub-and-spoke network is obtained according to the hub node, the spoke node, the axis and the spoke network.

8. A railway hub-and-spoke network construction device, characterized in that: include: An acquisition module, used to acquire node information of multiple railway nodes; the node information includes one or more combinations of aggregation capacity information, service level information, facility and equipment information, and development potential information; A scoring module, used to determine a node score of each railway node according to the node information of the railway node; A node type determination module, used to determine the node type of the railway node according to the node score; the node type includes a hub node and a spoke node; An attraction range determination module, used to determine the attraction range of the hub node to the spoke node according to the node information and the average travel time between the railway nodes; A construction module is used to construct a railway hub-and-spoke network based on the attraction range of the hub node to the spoke node.

9. An electronic device, characterized in that: include: at least one processor; as well as at least one memory in communication with the processor, wherein: The memory stores program instructions executable by the processor, and the processor can execute the method according to any one of claims 1 to 7 by calling the program instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the method according to any one of claims 1 to 7.

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