Goods transportation network space-time distribution data integration method

Abstract

The invention is suitable for the technical field of resource allocation, and provides a cargo transportation network space-time distribution data integration method, which comprises the following steps: S1, investigation statistics: carrying out investigation statistics on logistics nodes, logistics channels and logistics facility data of a predetermined region, carrying out classification arrangement on all the logistics nodes of the predetermined region, and storing the logistics nodes of the predetermined region; classifying according to logistics node levels; s2, spatial analysis: mining some potential information of a spatial target through conjoint analysis of spatial data and a spatial model; s3, establishing a complex network, and identifying key nodes in the highway-railway combined transportation network; and S4, comprehensive evaluation: making a node transfer capability evaluation strategy by means of the current layout situation of highway and railway facilities and channel accessibility analysis, and evaluating the road-railway combined transportation node transfer capability, thereby laying a foundation for solving the problem of intelligent scheduling of a certain area, improving the road-railway combined transportation efficiency of the certain area and establishing a scheduling system of the certain area.

Description

Technical Field

[0001] The present invention relates to the technical field of resource allocation, and in particular to a method for integrating spatiotemporal distribution data of a freight transportation network. Background Art

[0002] Although intermodal transport has been strongly supported by national policies, it still faces problems such as irrational resource allocation and uneven spatial and temporal distribution in actual operation, which to a certain extent restricts the improvement of intermodal transport service efficiency and the reduction of transportation costs. Existing research focuses on the operation mode, policy mechanism, and efficiency evaluation of intermodal transport, while there are relatively few studies on the temporal and spatial distribution characteristics of intermodal transport resources. In particular, there is a lack of systematic research on the efficiency and optimization path of intermodal transport resources in different time periods and regions, which is of great significance for guiding practical work and optimizing resource allocation.

[0003] In summary, the prior art obviously has inconveniences and defects in practical use, so it is necessary to improve it. Summary of the invention

[0004] In view of the above-mentioned defects, the purpose of the present invention is to provide a method for integrating the spatiotemporal distribution data of a freight transportation network, which can conduct spatiotemporal analysis on logistics nodes, logistics channels and logistics facilities based on an in-depth investigation of road-rail intermodal materials in a certain area, and use the kernel density, spatial autocorrelation and accessibility in spatial analysis theory to study resource distribution and draw a spatiotemporal distribution map of logistics resources. Through a comparative study of resource distribution in different years, a preliminary summary of the changing trend of road-rail intermodal logistics resources in a certain area is made. A road-rail intermodal network in a certain area is constructed, and with the help of complex network theory, the network centrality index is analyzed to divide cohesive subgroups. A network evolution model is constructed to study the changing trend of road-rail intermodal resources in a certain area. This topic deeply explores the road-rail intermodal resources in a certain area, and conducts in-depth research on the spatiotemporal distribution characteristics of road-rail resources, in order to solve the problem of intelligent scheduling in a certain area, to subsequently improve the efficiency of road-rail intermodal transport in a certain area, and to lay the foundation for establishing a scheduling system in a certain area.

[0005] In order to achieve the above object, the present invention provides a method for integrating temporal and spatial distribution data of a freight transportation network, comprising the following steps:

[0006] S1. Research and statistics: research and statistics on the logistics nodes, logistics channels and logistics facilities in the designated area, classify and sort all logistics nodes in the designated area, and classify them according to the logistics node level;

[0007] S2, spatial analysis, mining some potential information of spatial targets through joint analysis of spatial data and spatial models;

[0008] S3. Complex network establishment, identifying key nodes in the road-rail transport network, dividing the road-rail transport network into cohesive subgroups, and analyzing key nodes within different cohesive subgroups;

[0009] S4. Comprehensive evaluation: with the help of the current layout of road and railway facilities and channel accessibility analysis, formulate a node transfer capacity assessment strategy and evaluate the transfer capacity of road-rail intermodal nodes.

[0010] The method for integrating the spatiotemporal distribution data of a freight transportation network according to the present invention includes, in step S2, the following analysis methods:

[0011] S2.1, Kernel density analysis, within the scope of spatial analysis, creates a kernel for each point on the map, and calculates the density value of each location based on the number and distance of neighboring kernels, and then creates a continuous density surface based on the difference of the density value of each location, which can intuitively reflect the spatial aggregation of the analysis target;

[0012] S2.2, Spatial autocorrelation analysis, the principle of spatial autocorrelation originates from the concept of spatial statistics, and the similarity between spatial units is determined by analyzing the spatial values ​​between spatial units and adjacent units. First, the proximity relationship between spatial units is defined to construct a spatial weight matrix, and then the spatial autocorrelation index is calculated based on the spatial weight matrix, and finally a statistical significance test is performed to determine whether the calculation results are statistically significant;

[0013] S2.3 Accessibility analysis: Use the transportation network to calculate indicators such as the time, distance and cost required to reach the destination to analyze the accessibility pattern within the region.

[0014] According to the method for integrating the spatiotemporal distribution data of the freight transportation network of the present invention, since the road-rail transport network is an undirected network, the weight matrix in the network is symmetric, that is, w ij =w ji At the same time, there is no situation in the road-rail transport network where there is no node that is not directly connected to itself, and there is no self-loop, that is, w ii =0; In addition, the initial highway-railway intermodal transport network constructed is a global coupling network, that is, each node is directly or indirectly connected to other nodes, forming a highly dense connection structure, which is convenient for studying the overall properties and dynamic evolution process of the network.

[0015] According to the method for integrating the spatiotemporal distribution data of the freight transportation network of the present invention, based on the above model description, the specific steps of establishing the weighted BBV network evolution model are as follows:

[0016] (1) Initialization

[0017] Here we use the network construction representation in 4.1. The network nodes are represented by a set V: V = {vi |i = 1, 2, ..., n}; The edges are represented by the set E; E = {e ij |(v i , v j ), i, j = 1, 2, ..., n and i ≠ j}; The freight intensity between two urban nodes is represented by the matrix W, W = {w ij |i, j = 1, 2, ..., n and i ≠ j}. The initial network is a globally coupled network containing m0 nodes, and the initial value of the weight of each edge is set to w0;

[0018] (2) Inserting new nodes

[0019] For each newly added node a in the network, connect node a to the existing m nodes in the network. That is, for each added node, m edges are added to the network, where m ≤ m0.

[0020] (3) Dynamic evolution of edge weights

[0021] Each time a new node is added to the network, new edges are formed, and these new edges are assigned an initial weight w0. To simplify the model, the newly added edge e ai only causes the weight of the edge between node i and its neighbor node j to change, where node i receives additional traffic and the edges connected to node i will adjust the traffic distribution according to their original weights w ij . The adjustment rules are as follows:

[0022] w ij →w ij + Δw ij (5 - 3)

[0023]

[0024] where δ i represents the additional traffic burden brought by each newly introduced edge e ai to node i; Δw ij represents the weight change amount. Therefore, the point intensity of node i is adjusted as follows:

[0025] s i →s i + w0 + δ i (5 - 5)

[0026] According to the method for integrating spatio - temporal distribution data of the freight transportation network of the present invention, based on the above - established evolution model, the following evolution algorithm is designed:

[0027] Step 1: Parameter setting, set the total number of nodes N, the number of nodes m0 in the initial network, the number of nodes m preferentially connected, the initial value w0 of the edge connection weight, and the additional traffic burden δ brought by adding new edgesi ;

[0028] Step 2: Initialize the network, select m0 nodes, connect them two by two to form a global coupling network, and the initial weight value of each edge is set to w0;

[0029] Step 3: Add new nodes, adding 1 new node a at each time step;

[0030] Step 4: Determine the priority connection and calculate the probability that the new node a is connected to the original node i in the network And sort in descending order to get the priority connection sequence {Π1, Π2, ..., Π n}, n is the total number of nodes before node a joins the network;

[0031] Step 5: Add edges. Select the first m nodes from the priority connection sequence calculated in step 4 and connect them to the new node a. That is, add m edges to the evolving network. Each time a new edge e is added, ai are all assigned weight w0;

[0032] Step 6: Update the edge weight and node strength, and update the edge weight between the original node i and its neighbor node j to At the same time, adjust the strength of the original node i to s i +w0+δ i ;

[0033] Step 7: Terminate the evolution and continue to add new nodes and edges according to the above steps until all nodes are added to the evolved network;

[0034] Step 8: Calculate the eigenvalues, calculate the relevant statistical eigenvalues ​​of the final evolved network, and conduct comparative analysis of network characteristics.

[0035] According to the method for integrating the spatiotemporal distribution data of the freight transportation network of the present invention, a gravity model is introduced to determine the order in which nodes are added. The essence of the gravity model is that the interaction force between two economies is proportional to their respective economic scales (generally expressed in terms of regional GDP) and inversely proportional to the distance between them. The city with greater attraction to the Xinjiang Uygur Autonomous Region is more likely to join the Xinjiang highway-rail transport network. Therefore, the order in which nodes are added can be determined by calculating the gravity value between the city node to be added and the Xinjiang Uygur Autonomous Region. The gravity model formula between two cities can generally be expressed as:

[0036]

[0037] Among them, F ij is the gravity value between the two cities; Q1 and Q2 are the economic scales of the two cities respectively. In this section, GDP is selected as the measurement indicator; D ijis the distance between the two cities, G is the gravity index, usually 1.

[0038] According to the method for integrating the spatiotemporal distribution data of the freight transportation network of the present invention, the changes of the node degree, node strength and edge weight are as follows:

[0039] (1) Node degree

[0040] The maximum degree of the nodes in the initial network is 15, and the node degrees are mostly concentrated in 1, 2, and 3; in the first stage of evolution, the maximum degree of the nodes in the network is 12, and the node degree distribution develops towards a dispersed trend compared to time T0', but there is still aggregation near 2, 3, and 4; in the second stage of evolution, the maximum degree of the nodes in the network is 11, and the node degree distribution develops towards a clustered trend. The distribution of degree values ​​has not changed significantly compared to the first stage of evolution, and a large amount of aggregation reappears near K=2.

[0041] (2) Node strength

[0042] Node strength refers to the sum of the weights of all connections of a node, and node strength can more comprehensively reflect the importance of a node. From the node strength distribution diagram at the initial moment, the first stage of evolution, and the second stage of evolution, it can be seen that the node strength in the network is mainly concentrated between 0 and 0.5, among which the probability of a node strength of 0.2 is the largest in the three moments, which shows that the activity or participation of most nodes is at a medium level.

[0043] (3) Edge weight

[0044] It can be seen from the network characteristic diagrams at three moments that the edge weights in the network are mainly concentrated between 0 and 0.1, and as the number of nodes increases, the distribution of edge weights gradually becomes uniform. Combining the evolutionary network diagrams of road-rail transport at different moments, it can be seen that with the addition of new nodes, the previously high edge weights in Xinjiang have decreased. This is because the addition of new nodes may provide new path options, which disperses the traffic originally concentrated on certain edges to more edges, thereby reducing the weights of the original edges.

[0045] The present invention provides a method for integrating temporal and spatial distribution data of a freight transportation network, comprising the following steps:

[0046] S1, survey and statistics, survey and statistics on the logistics nodes, logistics channels and logistics facilities data of the predetermined area, classify and organize all logistics nodes in the predetermined area, and classify them according to the logistics node level; S2, spatial analysis, through the joint analysis of spatial data and spatial models to mine some potential information of spatial targets; S3, complex network establishment, identify key nodes in the road-rail transport network, divide the cohesive subgroups through the road-rail transport network, and analyze the key nodes inside different cohesive subgroups; S4, comprehensive evaluation, with the help of the layout status of highway and railway facilities and channel accessibility analysis, formulate a node transfer capacity assessment strategy, and evaluate the transfer capacity of road-rail transport nodes. In summary, the technical effect produced by the present invention is to conduct spatiotemporal analysis on logistics nodes, logistics channels and logistics facilities based on the in-depth survey of road-rail transport materials in a certain area, and study resource distribution with the help of kernel density, spatial autocorrelation and accessibility in spatial analysis theory, and draw a spatiotemporal distribution map of logistics resources. Through the comparative study of resource distribution in different years, the trend of changes in road-rail transport logistics resources in a certain area is preliminarily summarized. Construct a regional road-rail transport network, analyze the network centrality index with the help of complex network theory, and divide the cohesive subgroups. Construct a network evolution model to study the trend of road-rail transport resources in a certain region. This project deeply explores the road-rail transport resources in a certain region and studies the spatiotemporal distribution characteristics of road-rail resources in depth, in order to solve the problem of intelligent scheduling in a certain region, to improve the efficiency of road-rail transport in a certain region in the future, and to lay the foundation for establishing a scheduling system in a certain region. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the overall control flow structure of the present invention;

[0048] Figure 2 It is a schematic diagram of the algorithm flow structure of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0050] See also Figure 1 The present invention provides a method for integrating temporal and spatial distribution data of a freight transportation network, the method comprising the following steps:

[0051] S1. Research and statistics: research and statistics on the logistics nodes, logistics channels and logistics facilities in the designated area, classify and sort all logistics nodes in the designated area, and classify them according to the logistics node level;

[0052] S2, spatial analysis, mining some potential information of spatial targets through joint analysis of spatial data and spatial models;

[0053] S3. Complex network establishment, identifying key nodes in the road-rail transport network, dividing the road-rail transport network into cohesive subgroups, and analyzing key nodes within different cohesive subgroups;

[0054] S4. Comprehensive evaluation: with the help of the current layout of road and railway facilities and channel accessibility analysis, formulate a node transfer capacity assessment strategy and evaluate the transfer capacity of road-rail intermodal nodes.

[0055] Preferably, in step S2 of the present invention, the following analysis method is included:

[0056] S2.1, Kernel density analysis, within the scope of spatial analysis, creates a kernel for each point on the map, and calculates the density value of each location based on the number and distance of neighboring kernels, and then creates a continuous density surface based on the difference of the density value of each location, which can intuitively reflect the spatial aggregation of the analysis target;

[0057] S2.2, Spatial autocorrelation analysis. The principle of spatial autocorrelation originates from the concept of spatial statistics. The similarity between spatial units is determined by analyzing the spatial values ​​between spatial units and adjacent units. First, the proximity relationship between spatial units is defined to construct a spatial weight matrix. Then, based on the spatial weight matrix, the spatial autocorrelation index is calculated. Finally, a statistical significance test is performed to determine whether the calculation results are statistically significant.

[0058] S2.3 Accessibility analysis: Use the transportation network to calculate indicators such as the time, distance and cost required to reach the destination to analyze the accessibility pattern within the region.

[0059] In addition, since the road-rail transport network of the present invention is an undirected network, the weight matrix in the network is symmetrical, that is, w ij =w ji At the same time, there is no situation in the road-rail transport network where there is no node that is not directly connected to itself, and there is no self-loop, that is, w ii =0; In addition, the initial highway-railway intermodal transport network constructed is a global coupling network, that is, each node is directly or indirectly connected to other nodes, forming a highly dense connection structure, which is convenient for studying the overall properties and dynamic evolution process of the network.

[0060] Based on the above model description, the specific steps for establishing the weighted BBV network evolution model are as follows:

[0061] (1) Initialization

[0062] The notations for network construction in 4.1 are adopted here. Among them, the network nodes are represented by the set V: V = {v i | i = 1, 2,..., n}; the connecting edges are represented by the set E; E = {e ij | (v i , v j ), i, j = 1, 2,..., n and i ≠ j}; the freight intensity between two urban nodes is represented by the matrix W, W = {w ij | i, j = 1, 2,..., n and i ≠ j}. The initial network is a globally coupled network containing m0 nodes, and the initial value of the weight of each edge is set to w0;

[0063] (2) Inserting new nodes

[0064] For each newly added node a in the network, node a is connected to the existing m nodes in the network. That is, for each added node, m edges are added to the network, where m ≤ m0.

[0065] (3) Dynamic evolution of edge weights

[0066] Each time a new node is added to the network, new edges are formed, and these new edges are assigned an initial weight w0. To simplify the model, the newly added edge e ai only causes the weight of the edge between node i and its neighbor node j to change. Among them, node i receives additional traffic, and the edges connected to node i will adjust the traffic distribution according to their original weights w ij . The adjustment rule is as follows:

[0067] w ij → w ij + Δw ij (5-3)

[0068]

[0069] Among them, δ i represents the additional traffic burden brought by each newly introduced edge e ai to node i; Δw ij represents the weight change amount. Therefore, the point intensity of node i is adjusted as follows:

[0070] s i → s i + w0 + δ i (5-5)

[0071] Furthermore, according to the evolutionary model established above in the present invention, the following evolutionary algorithm is designed:

[0072] Step 1: Parameter setting, set the total number of nodes N, the initial number of network nodes m0, the number of nodes to be connected first m, the initial value of the edge weight w0, and the additional traffic burden δ caused by adding new edges i ;

[0073] Step 2: Initialize the network, select m0 nodes, connect them two by two to form a global coupling network, and the initial weight value of each edge is set to w0;

[0074] Step 3: Add new nodes, adding 1 new node a at each time step;

[0075] Step 4: Determine the priority connection and calculate the probability that the new node a is connected to the original node i in the network And sort in descending order to get the priority connection sequence {Π1, Π2, ..., Π n}, n is the total number of nodes before node a joins the network;

[0076] Step 5: Add edges. Select the first m nodes from the priority connection sequence calculated in step 4 and connect them to the new node a. That is, add m edges to the evolving network. Each time a new edge e is added, ai are all assigned weight w0;

[0077] Step 6: Update the edge weight and node strength, and update the edge weight between the original node i and its neighbor node j to At the same time, adjust the strength of the original node i to s i +w0+δ i ;

[0078] Step 7: Terminate the evolution and continue to add new nodes and edges according to the above steps until all nodes are added to the evolved network;

[0079] Step 8: Calculate the eigenvalues, calculate the relevant statistical eigenvalues ​​of the final evolved network, and conduct comparative analysis of network characteristics.

[0080] Better yet, the present invention introduces a gravity model to determine the order in which nodes are added. The essence of the gravity model is that the interaction force between two economies is proportional to their respective economic scales (generally expressed in terms of regional GDP) and inversely proportional to the distance between them. The city with greater attraction to the Xinjiang Uygur Autonomous Region is more likely to join the Xinjiang highway-rail transport network. Therefore, the order in which nodes are added can be determined by calculating the gravity value between the city node city to be added and the Xinjiang Uygur Autonomous Region. The gravity model formula between two cities can generally be expressed as:

[0081]

[0082] Among them, F ijis the gravity value between the two cities; Q1 and Q2 are the economic scales of the two cities respectively. In this section, GDP is selected as the measurement indicator; D ij is the distance between the two cities, G is the gravity index, usually 1.

[0083] According to the method for integrating the spatiotemporal distribution data of the freight transportation network of the present invention, the changes of the node degree, node strength and edge weight are as follows:

[0084] (1) Node degree

[0085] The maximum degree of the nodes in the initial network is 15, and the node degrees are mostly concentrated in 1, 2, and 3; in the first stage of evolution, the maximum degree of the nodes in the network is 12, and the node degree distribution develops towards a dispersed trend compared to time T0', but there is still aggregation near 2, 3, and 4; in the second stage of evolution, the maximum degree of the nodes in the network is 11, and the node degree distribution develops towards a clustered trend. The distribution of degree values ​​has not changed significantly compared to the first stage of evolution, and a large amount of aggregation reappears near K=2.

[0086] (2) Node strength

[0087] Node strength refers to the sum of the weights of all connections of a node, and node strength can more comprehensively reflect the importance of a node. From the node strength distribution diagram at the initial moment, the first stage of evolution, and the second stage of evolution, it can be seen that the node strength in the network is mainly concentrated between 0 and 0.5, among which the probability of a node strength of 0.2 is the largest in the three moments, which shows that the activity or participation of most nodes is at a medium level.

[0088] (3) Edge weight

[0089] It can be seen from the network characteristic diagrams at three moments that the edge weights in the network are mainly concentrated between 0 and 0.1, and as the number of nodes increases, the distribution of edge weights gradually becomes uniform. Combining the evolutionary network diagrams of road-rail transport at different moments, it can be seen that with the addition of new nodes, the previously high edge weights in Xinjiang have decreased. This is because the addition of new nodes may provide new path options, which disperses the traffic originally concentrated on certain edges to more edges, thereby reducing the weights of the original edges.

[0090] In summary, the present invention provides a method for integrating temporal and spatial distribution data of a freight transportation network, comprising the following steps:

[0091] S1, survey and statistics, survey and statistics on the logistics nodes, logistics channels and logistics facilities data of the predetermined area, classify and organize all logistics nodes in the predetermined area, and classify them according to the logistics node level; S2, spatial analysis, through the joint analysis of spatial data and spatial models to mine some potential information of spatial targets; S3, complex network establishment, identify key nodes in the road-rail transport network, divide the cohesive subgroups through the road-rail transport network, and analyze the key nodes inside different cohesive subgroups; S4, comprehensive evaluation, with the help of the layout status of highway and railway facilities and channel accessibility analysis, formulate a node transfer capacity assessment strategy, and evaluate the transfer capacity of road-rail transport nodes. In summary, the technical effect produced by the present invention is to conduct spatiotemporal analysis on logistics nodes, logistics channels and logistics facilities based on the in-depth survey of road-rail transport materials in a certain area, and study resource distribution with the help of kernel density, spatial autocorrelation and accessibility in spatial analysis theory, and draw a spatiotemporal distribution map of logistics resources. Through the comparative study of resource distribution in different years, the trend of changes in road-rail transport logistics resources in a certain area is preliminarily summarized. Construct a regional road-rail transport network, analyze the network centrality index with the help of complex network theory, and divide the cohesive subgroups. Construct a network evolution model to study the trend of road-rail transport resources in a certain region. This project deeply explores the road-rail transport resources in a certain region and studies the spatiotemporal distribution characteristics of road-rail resources in depth, in order to solve the problem of intelligent scheduling in a certain region, to improve the efficiency of road-rail transport in a certain region in the future, and to lay the foundation for establishing a scheduling system in a certain region.

[0092] Of course, the present invention may have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may make various corresponding changes and modifications based on the present invention, but these corresponding changes and modifications should all fall within the scope of protection of the claims attached to the present invention.

[0093] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and the like are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", and the like may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

Claims

1. A method for integrating spatiotemporal distribution data of a freight transportation network, characterized in that: The following steps are involved: S1. Research and statistics: research and statistics on the logistics nodes, logistics channels and logistics facilities in the designated area, classify and sort all logistics nodes in the designated area, and classify them according to the logistics node level; S2, spatial analysis, mining some potential information of spatial targets through joint analysis of spatial data and spatial models; S3. Complex network establishment, identifying key nodes in the road-rail transport network, dividing the road-rail transport network into cohesive subgroups, and analyzing key nodes within different cohesive subgroups; S4. Comprehensive evaluation: with the help of the current layout of road and railway facilities and channel accessibility analysis, formulate a node transfer capacity assessment strategy and evaluate the transfer capacity of road-rail intermodal nodes.

2. The method for integrating temporal and spatial distribution data of a freight transportation network according to claim 1, characterized in that: In step S2, the following analysis methods are included: S2.1, Kernel density analysis, within the scope of spatial analysis, creates a kernel for each point on the map, and calculates the density value of each location based on the number and distance of neighboring kernels, and then creates a continuous density surface based on the difference of the density value of each location, which can intuitively reflect the spatial aggregation of the analysis target; S2.2, Spatial autocorrelation analysis. The principle of spatial autocorrelation originates from the concept of spatial statistics. The similarity between spatial units is determined by analyzing the spatial values ​​between spatial units and adjacent units. First, the proximity relationship between spatial units is defined to construct a spatial weight matrix. Then, based on the spatial weight matrix, the spatial autocorrelation index is calculated. Finally, a statistical significance test is performed to determine whether the calculation results are statistically significant. S2.3 Accessibility analysis: Use the transportation network to calculate indicators such as the time, distance and cost required to reach the destination to analyze the accessibility pattern within the region.

3. The method for integrating temporal and spatial distribution data of a freight transportation network according to claim 1, characterized in that: In step S3, the following analysis methods are included: S3.

1. Network centrality analysis. Network centrality includes degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, etc. Through network centrality analysis, the key nodes identified are used for network optimization and risk assessment. S3.

2. Cohesive subgroup analysis. Cohesive subgroups are subgroups with more connections in the network, and the connections between subgroups are relatively few. Cohesive subgroup analysis can identify the modular structure and functional distribution in the network. Common cohesive subgroup algorithms include: modularity-based methods, spectral clustering methods, and hierarchical clustering methods. Cohesive subgroup analysis can better understand the structure and function of the network and identify key subgroups and nodes.

4. The method for integrating temporal and spatial distribution data of a freight transportation network according to any one of claims 1 to 3, characterized in that: Since the road-rail transport network is an undirected network, the weight matrix in the network is symmetric, that is, w ij =w ji At the same time, there is no situation in the road-rail transport network where there is no node that is not directly connected to itself, and there is no self-loop, that is, w ii =0; In addition, the initial road-rail transport network constructed is a global coupled network, that is, each node is directly or indirectly connected to other nodes, forming a highly dense connection structure, which is convenient for studying the overall properties and dynamic evolution process of the network.

5. The method for integrating temporal and spatial distribution data of a freight transportation network according to claim 3, characterized in that: Based on the above model description, the specific steps for establishing the weighted BBV network evolution model are as follows: (1) Initialization The representation of network construction in 4.1 is adopted here. Among them, the network nodes are represented by the set V: V = {v i | i = 1, 2,..., n}; the connecting edges are represented by the set E; E = {e ij | (v i , v j ), i, j = 1, 2,..., n and i ≠ j}; the freight intensity between two city nodes is represented by the matrix W, W = {w ij | i, j = 1, 2,..., n and i ≠ j}. The initial network is a globally coupled network containing m0 nodes, and the initial value of the weight of each edge is set to w0; (2) Insert a new node Every time a new node a is added to the network, it is connected to the existing m nodes in the network. That is, every time a node is added, m edges are added to the network, where m≤m0. (3) Dynamic Evolution of Edge Weights Each time a new node is added to the network, a new edge is formed, and these new edges are assigned an initial weight w0. To simplify the model, the newly added edge e ai It only causes the weight of the edge between node i and its neighbor node j to change, where node i receives additional traffic and the edges connected to node i are changed according to their original weights w ij To adjust the distribution of traffic. The adjustment rules are as follows: In ij →in ij +Δw ij (5-3) Among them, δ i Represented as each newly introduced edge e ai The additional traffic burden brought to node i; Δw ij represents the weight change. Therefore, the point strength of node i is adjusted as follows: s i →s i +w0+δ i (5-5) 6. The method for integrating temporal and spatial distribution data of a freight transportation network according to claim 4, characterized in that: According to the evolutionary model established above, the evolutionary algorithm is designed as follows: Step 1: Parameter setting, set the total number of nodes N, the initial number of network nodes m0, the number of nodes to be connected first m, the initial value of the edge weight w0, and the additional traffic burden δ caused by adding new edges i ; Step 2: Initialize the network, select m0 nodes, connect them two by two to form a global coupling network, and the initial weight value of each edge is set to w0; Step 3: Add new nodes, adding 1 new node a at each time step; Step 4: Determine the priority connection and calculate the probability that the new node a is connected to the original node i in the network And sort in descending order to get the priority connection sequence {Π1, Π2, ..., Π n }, n is the total number of nodes before node a joins the network; Step 5: Add edges. Select the first m nodes from the priority connection sequence calculated in step 4 and connect them to the new node a. That is, add m edges to the evolving network. Each time a new edge e is added, ai are all assigned weight w0; Step 6: Update the edge weight and node strength, and update the edge weight between the original node i and its neighbor node j to At the same time, adjust the strength of the original node i to s i +w0+δ i ; Step 7: Terminate the evolution and continue to add new nodes and edges according to the above steps until all nodes are added to the evolved network; Step 8: Calculate the eigenvalues, calculate the relevant statistical eigenvalues ​​of the final evolved network, and conduct comparative analysis of network characteristics.

7. The method for integrating temporal and spatial distribution data of a freight transportation network according to claim 5, characterized in that: In order to determine the order of node joining, the gravity model is introduced. The essence of the gravity model is that the interaction force between two economies is proportional to their respective economic scales (generally expressed in terms of regional GDP) and inversely proportional to the distance between them. The city with greater attraction to the Xinjiang Uygur Autonomous Region is more likely to join the Xinjiang highway-railway transport network. Therefore, the order of node joining can be determined by calculating the gravity value between the city node to be joined and the Xinjiang Uygur Autonomous Region. The gravity model formula between two cities can usually be expressed as: where F ij is the gravity value between the two cities; Q1 and Q2 are the economic scales of the two cities respectively. In this section, GDP is selected as the measurement indicator; D ij is the distance between the two cities, G is the gravity index, usually 1.

8. The method for integrating spatiotemporal distribution data of a freight transportation network according to claim 6, characterized in that: The changes in node degree, node strength, and edge weight are as follows: (1) Node degree The maximum degree of the nodes in the initial network is 15, and the node degrees are mostly concentrated in 1, 2, and 3; in the first stage of evolution, the maximum degree of the nodes in the network is 12, and the node degree distribution develops towards a dispersed trend compared to time T0', but there is still aggregation near 2, 3, and 4; in the second stage of evolution, the maximum degree of the nodes in the network is 11, and the node degree distribution develops towards a clustered trend. The distribution of degree values ​​has not changed significantly compared to the first stage of evolution, and a large amount of aggregation reappears near K=2. (2) Node strength Node strength refers to the sum of the weights of all connections of a node, and node strength can more comprehensively reflect the importance of a node. From the node strength distribution diagram at the initial moment, the first stage of evolution, and the second stage of evolution, it can be seen that the node strength in the network is mainly concentrated between 0 and 0.5, among which the probability of a node strength of 0.2 is the largest in the three moments, which shows that the activity or participation of most nodes is at a medium level. (3) Edge weight It can be seen from the network characteristic diagrams at three moments that the edge weights in the network are mainly concentrated between 0 and 0.1, and as the number of nodes increases, the distribution of edge weights gradually becomes uniform. Combining the evolutionary network diagrams of road-rail transport at different moments, it can be seen that with the addition of new nodes, the previously high edge weights in Xinjiang have decreased. This is because the addition of new nodes may provide new path options, which disperses the traffic originally concentrated on certain edges to more edges, thereby reducing the weights of the original edges.

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