Station passenger flow monitoring point site selection method and system based on complex network

Abstract

The invention provides a station passenger flow monitoring point site selection method and system based on a complex network, and belongs to the technical field of rail transit station safety management. Based on the constructed station passenger flow collecting and distributing network, calculating centrality indexes of network nodes, including passenger flow point degree centrality, passenger flow intermediary centrality and passenger flow approaching centrality; constructing a passenger flow monitoring node site selection optimization model based on the constructed station passenger flow collection and distribution network in combination with the calculated centrality index of the network node; and solving the constructed passenger flow monitoring node site selection optimization model to obtain a station passenger flow monitoring point site selection scheme. By analyzing the station passenger flow collection and distribution network, indexes representing passenger flow collection and distribution monitoring demand differences are realized, a station passenger flow monitoring point site selection model and method are constructed, and establishment of a scientific and effective station passenger flow monitoring system is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of rail transit station safety management, and in particular to a method and system for selecting a site for a passenger flow monitoring point in a station based on a complex network. Background Art

[0002] Passenger flow monitoring equipment plays an important role in ensuring passenger safety and improving station operation efficiency. Passenger flow monitoring equipment first provides key data support for the work arrangement of station staff by real-time monitoring of the passenger flow of the station, helping the staff to make reasonable and correct decisions. However, there are still some problems in the layout of passenger flow monitoring equipment:

[0003] (1) Incomplete monitoring coverage: At present, the layout and coverage of passenger flow monitoring equipment in some stations are obviously limited. The deployment of monitoring equipment is often only targeted at individual entrances and exits, so not all areas and channels can obtain real-time and accurate passenger flow data monitoring, which affects the comprehensive analysis and accurate judgment of the overall passenger flow situation;

[0004] (2) Data accuracy is not high: The effect of passenger flow monitoring data is affected by the monitoring range and the accuracy of the monitoring equipment. The detection range refers to whether the covered area can specifically reflect the passenger flow situation in the area. The detection accuracy refers to when the coverage range is within the equipment capacity, the detection accuracy is relatively high, otherwise it is relatively low. Therefore, if you want to obtain a better monitoring effect, you need to take into account both the detection range and the monitoring accuracy;

[0005] (3) Chaotic layout: The location of monitoring equipment is not reasonably planned, resulting in occlusion or overlap between devices, affecting the accuracy and efficiency of data collection. Due to the uneven distribution of equipment and chaotic layout, monitoring blind spots may appear, making it impossible to fully obtain passenger flow information in the station. To solve this problem, the location of equipment should be scientifically planned to ensure that the coverage of each monitoring point is complementary and non-overlapping, thereby improving the efficiency and accuracy of data collection;

[0006] The function of the monitoring equipment is to monitor basic conditions such as passenger flow at the station and whether any emergency incidents occur. The layout of the monitoring equipment will be different for different monitoring situations. The location of the monitoring equipment should be based on the differences in passenger flow monitoring needs to come up with different layout plans for passenger flow monitoring equipment. Summary of the invention

[0007] The purpose of the present invention is to provide a method for selecting a site for passenger flow monitoring points at a station based on a complex network, so as to solve at least one technical problem existing in the above-mentioned background technology.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] In a first aspect, the present invention provides a method for selecting a station passenger flow monitoring point based on a complex network, comprising:

[0010] Construct a station passenger flow distribution network based on the station passenger flow lines;

[0011] Based on the constructed station passenger flow distribution network, the centrality indexes of network nodes are calculated, including passenger flow point degree centrality, passenger flow betweenness centrality and passenger flow proximity centrality;

[0012] Based on the constructed station passenger flow distribution network and the calculated centrality index of the network nodes, a passenger flow monitoring node location optimization model is constructed;

[0013] Solve the constructed passenger flow monitoring node location optimization model and obtain the station passenger flow monitoring point location plan.

[0014] Furthermore, the passenger flow point degree centrality is calculated by weighted calculation based on the calculation method of the ordinary node structure point degree centrality by considering the influence of passenger flow. The calculation formula of the passenger flow point degree centrality D(i) of the station passenger flow distribution network node i is as follows:

[0015] D(i)=∑ i≠j x ij Q(i);

[0016] Where D(i) is the degree centrality of the passenger flow of node i; Q(i) is the passenger flow of node i that can be monitored; i, j are nodes in the station passenger flow distribution network; x ij -Connect variables, i and j are connected and take 1, otherwise they take 0.

[0017] Furthermore, the passenger flow intermediary centrality is obtained by considering the influence of passenger flow on the basis of the network structure intermediary centrality. The calculation formula of the passenger flow intermediary center of node i is as follows:

[0018]

[0019] B(i)=B'(i)Q(i)

[0020] Where B'(i) represents the structural betweenness centrality of node i; B(i) represents the betweenness centrality of node i after passenger flow weighting; Q(i) represents the passenger flow of node i available to the monitoring equipment; i, t, s represent the nodes in the station passenger flow distribution network diagram; I represents the set of all nodes in the station passenger flow distribution network diagram; σ st represents the number of shortest paths from node s to node t; σ st (i) represents the number of shortest paths from node s to node t that pass through point i.

[0021] Furthermore, the closeness centrality value of a node indicates the degree to which the node is "close to the center" in the network structure. Considering the impact of passenger flow, the calculation formula for the node passenger flow closeness centrality is as follows:

[0022] C′(i)=(N-1) / ∑d(i,j)

[0023] C(i)=C′(i)Q(i)

[0024] Where C′(i) represents the structural closeness centrality value of node i; C(i) represents the closeness centrality of node i after passenger flow weighting; Q(i) represents the passenger flow of node i available to the monitoring device; i represents a node in the network graph; N represents the number of nodes; d(i, j) represents the shortest path length between node i and node j; ∑d(i, j) represents the sum of the shortest path lengths from node i to all other nodes (j) in the network.

[0025] Furthermore, combining the passenger flow point degree centrality, passenger flow betweenness centrality and passenger flow proximity centrality, the specific evaluation calculation formula for passenger flow monitoring effect is as follows:

[0026]

[0027] In the formula, M represents the passenger flow monitoring effect; N represents the number of nodes; represents the weight parameter of passenger flow point degree centrality; represents the weight parameter of passenger flow betweenness centrality; Indicates that passenger flow is close to the centrality weight parameter; in order to be able to measure on the same scale, They are the normalized passenger flow point degree centrality, betweenness centrality and closeness centrality respectively.

[0028] Furthermore, the established passenger flow monitoring node location optimization model is:

[0029] Objective function: max M

[0030] Constraints: y i p i ≤H

[0031] In the formula, y i represents the decision variable. If we choose node i to install monitoring facilities, then y i =1 otherwise 0; p i - the cost of deploying passenger flow monitoring equipment at node i; H - the total budget for deploying passenger flow monitoring equipment.

[0032] In a second aspect, the present invention provides a station passenger flow monitoring point site selection system based on a complex network, comprising:

[0033] The first building module is used to build a station passenger flow distribution network;

[0034] The first calculation module is used to calculate the centrality indexes of network nodes based on the constructed station passenger flow distribution network, including passenger flow point degree centrality, passenger flow betweenness centrality and passenger flow proximity centrality;

[0035] The second construction module is used to construct a passenger flow monitoring node location optimization model based on the constructed station passenger flow distribution network and the calculated centrality index of the network nodes;

[0036] The second calculation module is used to solve the constructed passenger flow monitoring node location optimization model and obtain the station passenger flow monitoring point location plan.

[0037] In a third aspect, the present invention provides a non-transitory computer-readable storage medium, which is used to store computer instructions. When the computer instructions are executed by a processor, the method for selecting a station passenger flow monitoring point based on a complex network as described in the first aspect is implemented.

[0038] In a fourth aspect, the present invention provides a computer device comprising a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the method for selecting station passenger flow monitoring points based on a complex network as described in the first aspect.

[0039] In a fifth aspect, the present invention provides an electronic device comprising: a processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes instructions for implementing the method for site selection of station passenger flow monitoring points based on a complex network as described in the first aspect.

[0040] The beneficial effects of the present invention are as follows: by analyzing the station passenger flow distribution network, an index representing the differences in passenger flow distribution monitoring needs is realized, a station passenger flow monitoring point site selection optimization model and method are constructed, and a scientific and effective station passenger flow monitoring system is helped to be established.

[0041] Additional advantages of the present invention will be more clearly given in the following description or learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0043] Figure 1 This is a flow chart of a method for selecting a site for passenger flow monitoring points at a station based on a complex network according to an embodiment of the present invention.

[0044] Figure 2 This is a schematic diagram of the CQDZ exit transfer layer network according to an embodiment of the present invention.

[0045] Figure 3 This is a schematic diagram of a site selection scheme based on monitoring passenger flow conflicts according to an embodiment of the present invention.

[0046] Figure 4 This is a schematic diagram of a site selection scheme based on monitoring passenger flow direction according to an embodiment of the present invention.

[0047] Figure 5 This is a schematic diagram of a site selection scheme based on evaluating network convenience according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below by the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0049] It should be understood by those skilled in the art that unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs.

[0050] It should also be understood that terms, such as those defined in commonly used dictionaries, should be understood to have a meaning consistent with that in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless as defined herein.

[0051] Those skilled in the art will appreciate that, unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements and / or groups thereof.

[0052] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. Different embodiments or examples described in this specification and features of different embodiments or examples may be combined and combined by those skilled in the art without contradiction.

[0053] To facilitate understanding of the present invention, the present invention is further explained below with reference to specific embodiments in conjunction with the accompanying drawings, and the specific embodiments do not constitute a limitation on the embodiments of the present invention.

[0054] Those skilled in the art should understand that the drawings are merely schematic diagrams of embodiments, and the components in the drawings are not necessarily necessary for implementing the present invention.

[0055] Example 1

[0056] In this embodiment 1, a station passenger flow monitoring point site selection system based on a complex network is first provided, and the system includes: a first construction module, which is used to construct a station passenger flow distribution network; a first calculation module, which is used to calculate the centrality indicators of network nodes based on the constructed station passenger flow distribution network, including passenger flow point degree centrality, passenger flow betweenness centrality and passenger flow proximity centrality; a second construction module, which is used to construct a passenger flow monitoring node site selection optimization model based on the constructed station passenger flow distribution network and the calculated centrality indicators of the network nodes; a second calculation module, which is used to solve the constructed passenger flow monitoring node site selection optimization model to obtain a station passenger flow monitoring point site selection plan.

[0057] In this embodiment, the above-mentioned system is used to implement a method for site selection of station passenger flow monitoring points based on a complex network, including: using a first construction module to construct a station passenger flow distribution network; using a first calculation module to calculate the centrality indicators of network nodes based on the constructed station passenger flow distribution network, including passenger flow point degree centrality, passenger flow betweenness centrality and passenger flow proximity centrality; using a second construction module to construct a passenger flow monitoring node site selection optimization model based on the constructed station passenger flow distribution network and the calculated centrality indicators of the network nodes; using a second calculation module to solve the constructed passenger flow monitoring node site selection optimization model to obtain a station passenger flow monitoring point site selection plan.

[0058] Constructing a station passenger flow distribution network plays a key role in the layout of passenger flow monitoring facilities. First, the station passenger flow distribution network can provide a comprehensive perspective when analyzing flow lines and selecting monitoring nodes, and can accurately grasp the overall structure and spatial layout of the station. Analyzing the passenger flow lines of the station through the station passenger flow distribution network can better understand the path planning of passengers in activities such as entering, exiting, and transferring, and then determine the reasonable location of monitoring facilities. The station passenger flow distribution network clearly shows the relationship and connection method of various areas within the station, providing a basis for the effective planning of the layout of monitoring facilities. Secondly, the station passenger flow distribution network helps to conduct in-depth research on the activity trajectory and behavioral characteristics of passengers, so as to better understand the needs and characteristics of different passenger flow monitoring.

[0059] In this embodiment, the passenger flow point degree centrality is calculated by weighted calculation based on the ordinary node structure point degree centrality calculation method, taking into account the influence of passenger flow. The passenger flow point degree centrality D(i) of the station passenger flow distribution network node i is calculated as follows:

[0060] D(i)=∑ i≠j x ij Q(i);

[0061] Where D(i) is the degree centrality of the passenger flow of node i; Q(i) is the passenger flow of node i that can be monitored; i, j are nodes in the station passenger flow distribution network; x ij -Connect variables, i and j are connected and take 1, otherwise they take 0.

[0062] The passenger flow intermediary centrality is obtained by considering the influence of passenger flow on the basis of the network structure intermediary centrality. The calculation formula of the passenger flow intermediary center of node i is as follows:

[0063]

[0064] B(i)=B′(i)Q(i)

[0065] Where B′(i) represents the structural betweenness centrality of node i; B(i) represents the betweenness centrality of node i after passenger flow weighting; Q(i) represents the passenger flow of node i available to the monitoring equipment; i, t, s represent the nodes in the station passenger flow distribution network diagram; I represents the set of all nodes in the station passenger flow distribution network diagram; σ st represents the number of shortest paths from node s to node t; σ st (i) represents the number of shortest paths from node s to node t that pass through point i.

[0066] The closeness centrality value of a node indicates the degree to which the node is "close to the center" in the network structure. Considering the impact of passenger flow, the calculation formula for the node passenger flow closeness centrality is as follows:

[0067] C(i)=(N-1) / ∑d(i,j)

[0068] C(i)=C′(i)Q(i)

[0069] Where C′(i) represents the structural closeness centrality value of node i; C(i) represents the closeness centrality of node i after passenger flow weighting; Q(i) represents the passenger flow of node i available to the monitoring device; i represents a node in the network graph; N represents the number of nodes; d(i, j) represents the shortest path length between node i and node j; ∑d(i, j) represents the sum of the shortest path lengths from node i to all other nodes (j) in the network.

[0070] Combining the passenger flow point degree centrality, passenger flow betweenness centrality and passenger flow proximity centrality, the specific evaluation calculation formula for passenger flow monitoring effect is as follows:

[0071]

[0072] In the formula, M represents the passenger flow monitoring effect; N represents the number of nodes; represents the weight parameter of passenger flow point degree centrality; represents the weight parameter of passenger flow betweenness centrality; Indicates that passenger flow is close to the centrality weight parameter; In order to be able to measure on the same scale, They are the normalized passenger flow point degree centrality, betweenness centrality and closeness centrality respectively.

[0073] The established passenger flow monitoring node location optimization model is:

[0074] Objective function: max M

[0075] Constraints: y i p i ≤H

[0076] In the formula, y i represents the decision variable. If we choose node i to install monitoring facilities, then y i =1 otherwise 0; p i - the cost of deploying passenger flow monitoring equipment at node i; H - the total budget for deploying passenger flow monitoring equipment.

[0077] If you need to focus on monitoring the conflict of passenger flow at the station, you can use the passenger flow point centrality weight parameter The value is set relatively high. If you need to focus on monitoring the flow direction and propagation path of station passenger flow, you can use the passenger flow intermediary centrality weight parameter The value is set relatively high. If you want to focus on monitoring the average travel distance of station passenger flow, path selection, and other factors to evaluate the convenience and efficiency of the network, you can use the passenger flow proximity centrality weight parameter

[0078] Example 2

[0079] This implementation 2 provides a station passenger flow monitoring point site selection method based on a complex network, and the problem to be solved is the station passenger flow monitoring point site selection problem considering the differences in passenger flow monitoring needs.

[0080] The station passenger flow monitoring system realizes the monitoring of the station passenger flow status. However, the passenger flow status is reflected by different indicators, such as passenger flow conflict, passenger flow OD flow, and passenger flow efficiency, etc. Different passenger flow monitoring needs lead to different station passenger flow monitoring system deployment plans. Therefore, it is necessary to analyze the station passenger flow distribution network, propose indicators representing the differences in passenger flow distribution monitoring needs, and construct a station passenger flow monitoring point site selection model and method to help establish a scientific and effective station passenger flow monitoring system.

[0081] The site selection method for station passenger flow monitoring points based on complex networks described in this embodiment first constructs a station passenger flow distribution network according to the station passenger flow lines; then calculates the centrality index of the network nodes; then, constructs a passenger flow monitoring node site selection optimization model; finally, proposes a heuristic algorithm for solving the model.

[0082] Step 1: Build a station passenger flow distribution network

[0083] Constructing a station passenger flow distribution network plays a key role in the layout of passenger flow monitoring facilities. First, the station passenger flow distribution network can provide a comprehensive perspective when analyzing flow lines and selecting monitoring nodes, and can accurately grasp the overall structure and spatial layout of the station. Analyzing the passenger flow lines of the station through the station passenger flow distribution network can better understand the path planning of passengers in activities such as entering, exiting, and transferring, and then determine the reasonable location of monitoring facilities. The station passenger flow distribution network clearly shows the relationship and connection method of various areas within the station, providing a basis for the effective planning of the layout of monitoring facilities. Secondly, the station passenger flow distribution network helps to conduct in-depth research on the activity trajectory and behavioral characteristics of passengers, so as to better understand the needs and characteristics of different passenger flow monitoring.

[0084] Step 2: Passenger flow monitoring effect evaluation

[0085] According to the passenger flow distribution network, the location selection problem of passenger flow monitoring facilities is converted into the selection problem of passenger flow monitoring points, which is conducive to analyzing the problem and finding the key. The selection of passenger flow monitoring points mainly considers the differences in indicators such as passenger flow point centrality, passenger flow intermediary centrality and passenger flow proximity centrality of the nodes, and proposes a comprehensive evaluation method for passenger flow monitoring effect.

[0086] 2.1 Passenger flow point centrality

[0087] Point degree centrality can also be understood as connection centrality, which is the sum of the direct connections between a node and other nodes in the network diagram. Passenger flow point degree centrality is calculated by weighted calculation based on the calculation method of ordinary node structure point degree centrality, taking into account the influence of passenger flow. The calculation formula of passenger flow point degree centrality D(i) of node i in the station passenger flow distribution network is as follows:

[0088] D(i)=∑ i≠j x ij Q(i) (1)

[0089] Where D(i) is the degree centrality of the passenger flow of node i; Q(i) is the passenger flow of node i that can be monitored; i, j are nodes in the station passenger flow distribution network; x ij -Connection variables, i and j are connected to get 1, otherwise it is 0;

[0090] Since the node passenger flow degree centrality is a comprehensive indicator to measure the number of connecting edges and passenger flow around the node, deploying monitoring facilities at nodes with high node passenger flow degree centrality can help monitor the conflict of passenger flow in different areas of the station distribution network.

[0091] 2.2 Passenger flow betweenness centrality

[0092] In network analysis, betweenness centrality is an important metric used to measure the control and communication capabilities of a node in a network. Similarly, passenger flow betweenness centrality is calculated based on the network structure betweenness centrality by considering the impact of passenger flow. The passenger flow betweenness centrality of node i is calculated as follows:

[0093]

[0094] B(i)=B′(i)Q(i) (3)

[0095] Where B′(i)-structural betweenness centrality of node i;

[0096] B(i) - betweenness centrality of node i after passenger flow weighting;

[0097] Q(i) - passenger flow of node i available to the monitoring device;

[0098] i, t, s - nodes in the network graph;

[0099] I - the set of all nodes in the network graph;

[0100] σ st -The number of shortest paths from node s to node t;

[0101] σst (i) - the number of shortest paths from node s to node t that pass through point i.

[0102] From the calculation method of betweenness centrality, it can be seen that deploying passenger flow detection equipment at nodes with relatively high node passenger flow betweenness centrality can better monitor passenger flow direction and propagation. After obtaining the passenger flow data of these nodes, the passenger flow status of the entire station passenger flow distribution network can be monitored through decoupling or passenger flow OD data.

[0103] 2.3 Passenger flow proximity centrality

[0104] Closeness centrality is a metric used to measure the importance of nodes in a network. It takes into account the average shortest distance from a node to other nodes in the network. The closeness centrality value of a node indicates how "close to the center" the node is in the network structure. Considering the impact of passenger flow, the calculation formula for node passenger flow closeness centrality is as follows:

[0105] C′(i)=(N-1) / ∑d(i,j) (4)

[0106] C(i)=C′(i)Q(i) (5)

[0107] Where C′(i)-the structure of node i is close to the centrality value;

[0108] C(i) - the closeness centrality of node i after passenger flow weighting;

[0109] Q(i) - passenger flow of node i available to the monitoring device;

[0110] i - a node in a network graph;

[0111] N – number of nodes;

[0112] d(i, j) - the shortest path length between node i and node j;

[0113] ∑d(i,j) – the sum of the shortest path lengths from node i to all other nodes (j) in the network.

[0114] The greater the degree centrality of passenger flow, the more passenger flow passes through the geometric center node in the distribution network, that is, the closer the average distance between passenger flow and other nodes is. Therefore, the deployment of monitoring equipment at nodes with high degree centrality can be used to monitor and evaluate the convenience of the station passenger flow distribution network.

[0115] 2.4 Passenger flow monitoring effect evaluation method

[0116] Based on the above three indicators, the specific evaluation formula for passenger flow monitoring effect is as follows:

[0117]

[0118] Where M is the passenger flow monitoring effect; N is the number of nodes; -Passenger flow point degree centrality weight parameter; - passenger flow betweenness centrality weight parameter; -The passenger flow is close to the centrality weight parameter. In order to be able to measure on the same scale, They are respectively the normalized passenger flow point degree centrality, betweenness centrality and closeness centrality. The calculation method is as follows:

[0119]

[0120] Step 3: Model building

[0121] The selection of passenger flow monitoring points not only considers the passenger flow monitoring effect but is also limited by the construction cost of the passenger flow monitoring system. Therefore, the passenger flow monitoring node location optimization model established in this paper is as follows:

[0122] Objective function: max M (10)

[0123] Constraints: y i p i ≤H (11)

[0124] Where y i -Decision variables, if node i is selected to install monitoring facilities, then y i =1 otherwise 0; p i - the cost of deploying passenger flow monitoring equipment at node i; H - the total budget for deploying passenger flow monitoring equipment.

[0125] If you need to focus on monitoring the conflict of passenger flow at the station, you can use the passenger flow point centrality weight parameter The value is set relatively high. If you need to focus on monitoring the flow direction and propagation path of station passenger flow, you can use the passenger flow intermediary centrality weight parameter The value is set relatively high. If you want to focus on monitoring the average travel distance of station passenger flow, path selection, and other factors to evaluate the convenience and efficiency of the network, you can use the passenger flow proximity centrality weight parameter

[0126] Example verification

[0127] In this embodiment, a certain city east station (CQDZ) was selected to study the deployment plan of passenger flow monitoring equipment. The study specifically took the CQDZ exit transfer layer as an example for verification. This layer has a total of six exits to the transfer hall. There are four subway entrances in the hall for subway transfers. From the hall, you can transfer to a taxi to the north; to the south, you can transfer to a bus, a long-distance coach, and a direct airport link; to the west, you can go to Kaicheng Road; to the east, you can go to Shujie Road. When drawing the passenger flow distribution network of the exit transfer layer, you first need to determine the relative positions of important nodes such as each exit, subway station entrance, etc. Since this plan will serve as the centrality index of the nodes in the study station and the basic map for the selection of passenger flow monitoring points, it is necessary to draw the paths where there may be passenger flow lines between the nodes in the network diagram. The passenger flow distribution network diagram of the CQDZ exit transfer layer is shown below. Figure 2 shown.

[0128] The nodes represented by the numbers in the boxes in the figure are all at the intersections of passenger flow lines or road forks, etc. The numbers 5, 7, 15, 17, 25, and 27 in the boxes represent the railway passenger exits, and the numbers in the circles represent the passengers' destinations, of which 1, 2, 3, 4, and 5 represent arrival at Shujie Road; 14, 15, 16, 17, and 18 represent arrival at Kaicheng Road; 12, 22, and 32 represent transfer to taxis; 18 and 28 represent transfer to buses; and 0 and 8 represent arrival at city terminals and long-distance bus yards, all of which are classified as long-distance passenger transport. At this point, the station passenger flow distribution network is complete.

[0129] The passenger flow centrality index of each node can be calculated through the adjacency matrix of the station passenger flow distribution network graph. The passenger flow point degree centrality index, betweenness centrality index and proximity centrality index will be calculated respectively below. Tables 1 to 3 are the calculation results of the passenger flow point degree centrality index, passenger flow betweenness centrality index and passenger flow proximity centrality index of different nodes respectively.

[0130] Table 1 Node passenger flow point degree centrality values

[0131]

[0132]

[0133] Table 2 Node betweenness centrality values ​​after passenger flow weighting

[0134]

[0135]

[0136]

[0137] Table 3 Node proximity centrality values ​​after passenger flow weighting

[0138]

[0139]

[0140] According to the table data, nodes 16, 20, 10, 15, and 17 have high proximity centrality. According to the definition of proximity centrality, the closer the average distance from a node to the rest of the nodes in the network, the higher the proximity centrality. In contrast, the above nodes are located in the center of the station in the network, so the calculation results are in line with reality.

[0141] The average price of passenger flow monitoring equipment is 4,000 yuan per unit. The budget for building passenger flow monitoring equipment at the CQDZ exit transfer layer is 60,000 yuan, that is, C' = 60,000. According to the target function and constraints, the selection of deployment nodes for monitoring different passenger flow characteristics can be obtained. The following is a passenger flow monitoring equipment deployment plan calculated for the purpose of monitoring passenger flow conflicts at key stations, namely According to the site selection model for passenger flow monitoring equipment, it is calculated that the passenger flow monitoring nodes include 9, 15, 17, 11, 25, 16, 27, 7, 5, 26, 19, 21, 23, 13, and 14. The site selection plan is as follows: Figure 3 shown.

[0142] If you want to focus on monitoring the flow of passengers in the station, you can set the node's betweenness centrality parameter weight to the highest, that is, According to the site selection model for passenger flow monitoring equipment, it is calculated that the passenger flow monitoring nodes include 9, 15, 16, 11, 17, 26, 19, 21, 10, 20, 25, 27, 13, 7, and 5. The site selection plan is as follows: Figure 4 shown.

[0143] If you want to evaluate the convenience and accessibility of the passenger flow network of this layer by monitoring the passenger flow, you need to give priority to the points with higher proximity centrality after weighting the passenger flow. Then, you can set the node's proximity centrality parameter weight to the highest, that is, According to the site selection model for passenger flow monitoring equipment, it is calculated that the passenger flow monitoring nodes include 15, 9, 17, 11, 25, 16, 27, 13, 26, 7, 14, 23, 5, 19, and 21. Figure 5 shown.

[0144] Example 3

[0145] This embodiment 3 provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the method for selecting a station passenger flow monitoring point based on a complex network as described above is implemented. The method includes:

[0146] Construct a station passenger flow distribution network based on the station passenger flow lines;

[0147] Based on the constructed station passenger flow distribution network, the centrality indexes of network nodes are calculated, including passenger flow point degree centrality, passenger flow betweenness centrality and passenger flow proximity centrality;

[0148] Based on the constructed station passenger flow distribution network and the calculated centrality index of the network nodes, a passenger flow monitoring node location optimization model is constructed;

[0149] Solve the constructed passenger flow monitoring node location optimization model and obtain the station passenger flow monitoring point location plan.

[0150] Example 4

[0151] This embodiment 4 provides a computer device, including a memory and a processor, the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the above-mentioned station passenger flow monitoring point site selection method based on a complex network, the method comprising:

[0152] Construct a passenger flow distribution network at stations;

[0153] Based on the constructed station passenger flow distribution network, the centrality indexes of network nodes are calculated, including passenger flow point degree centrality, passenger flow betweenness centrality and passenger flow proximity centrality;

[0154] Based on the constructed station passenger flow distribution network and the calculated centrality index of the network nodes, a passenger flow monitoring node location optimization model is constructed;

[0155] Solve the constructed passenger flow monitoring node location optimization model and obtain the station passenger flow monitoring point location plan.

[0156] Example 5

[0157] This embodiment 5 provides an electronic device, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes instructions for implementing the above-mentioned method for selecting a station passenger flow monitoring point based on a complex network, the method comprising:

[0158] Construct a passenger flow distribution network at stations;

[0159] Based on the constructed station passenger flow distribution network, the centrality indexes of network nodes are calculated, including passenger flow point degree centrality, passenger flow betweenness centrality and passenger flow proximity centrality;

[0160] Based on the constructed station passenger flow distribution network and the calculated centrality index of the network nodes, a passenger flow monitoring node location optimization model is constructed;

[0161] Solve the constructed passenger flow monitoring node location optimization model and obtain the station passenger flow monitoring point location plan.

[0162] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0163] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0164] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0165] These computer program instructions can also be loaded onto a computer or other programmable data processing device, and a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0166] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative work on the basis of the technical solution disclosed in the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for selecting station passenger flow monitoring points based on complex networks, characterized in that: include: Construct a station passenger flow distribution network based on the station passenger flow lines; Based on the constructed station passenger flow distribution network, the centrality indexes of network nodes are calculated, including passenger flow point degree centrality, passenger flow betweenness centrality and passenger flow proximity centrality; Based on the constructed station passenger flow distribution network and the calculated centrality index of the network nodes, a passenger flow monitoring node location optimization model is constructed; Solve the constructed passenger flow monitoring node location optimization model and obtain the station passenger flow monitoring point location plan.

2. The site selection method for station passenger flow monitoring points based on complex networks according to claim 1 is characterized in that: The passenger flow point degree centrality is calculated by weighted calculation based on the calculation method of the ordinary node structure point degree centrality by considering the influence of passenger flow. The calculation formula of the passenger flow point degree centrality D(i) of the station passenger flow distribution network node i is as follows: D(i)=∑ i≠j x ij Q(i); Where D(i) is the degree centrality of the passenger flow of node i; Q(i) is the passenger flow of node i that can be monitored; i, j are nodes in the station passenger flow distribution network; x ij -Connect variables, i and j are connected and take 1, otherwise they take 0.

3. The site selection method for station passenger flow monitoring points based on complex networks according to claim 2 is characterized in that: The passenger flow intermediary centrality is obtained by considering the influence of passenger flow on the basis of the network structure intermediary centrality. The calculation formula of the passenger flow intermediary center of node i is as follows: B(i)=B'(i)Q(i) In the formula, B'(i) represents the structural betweenness centrality of node i; B(i) represents the betweenness centrality of node i after passenger flow weighting; Q(i) represents the passenger flow of node i that can be obtained by the monitoring equipment; i, t, s represent the nodes in the station passenger flow distribution network diagram; I represents the set of all nodes in the station passenger flow distribution network diagram; σ st represents the number of shortest paths from node s to node t; σ st (i) represents the number of shortest paths from node s to node t that pass through point i.

4. The site selection method for station passenger flow monitoring points based on complex networks according to claim 3 is characterized in that: The closeness centrality value of a node indicates the degree to which the node is "close to the center" in the network structure. Considering the impact of passenger flow, the calculation formula for the node passenger flow closeness centrality is as follows: C'(i)=(N-1) / ∑d(i,j) C(i)=C'(i)Q(i) In the formula, C'(i) represents the structural proximity centrality value of node i; C(i) represents the proximity centrality of node i after passenger flow weighting; Q(i) represents the passenger flow of node i that can be obtained by the monitoring device; i represents a node in the network graph; N represents the number of nodes; d(i,j) represents the shortest path length between node i and node j; ∑d(i,j) represents the sum of the shortest path lengths from node i to all other nodes (j) in the network.

5. The method for selecting a station passenger flow monitoring point based on a complex network according to claim 4 is characterized in that: Combining the passenger flow point degree centrality, passenger flow betweenness centrality and passenger flow proximity centrality, the specific evaluation calculation formula for passenger flow monitoring effect is as follows: In the formula, M represents the passenger flow monitoring effect; N represents the number of nodes; represents the weight parameter of passenger flow point degree centrality; represents the weight parameter of passenger flow betweenness centrality; Indicates that passenger flow is close to the centrality weight parameter; in order to be able to measure on the same scale, They are the normalized passenger flow point degree centrality, betweenness centrality and closeness centrality respectively.

6. The method for selecting a station passenger flow monitoring point based on a complex network according to claim 5 is characterized in that: The established passenger flow monitoring node selection model is: Objective function: max M Constraints: y i p i ≤H In the formula, y i represents the decision variable. If we choose node i to install monitoring facilities, then y i =1 otherwise 0; p i - the cost of deploying passenger flow monitoring equipment at node i; H - the total budget for deploying passenger flow monitoring equipment.

7. A station passenger flow monitoring point site selection system based on complex network, characterized in that: include: The first construction module is used to construct a station passenger flow distribution network based on the station passenger flow lines; The first calculation module is used to calculate the centrality indexes of network nodes based on the constructed station passenger flow distribution network, including passenger flow point degree centrality, passenger flow betweenness centrality and passenger flow proximity centrality; The second construction module is used to construct a passenger flow monitoring node location optimization model based on the constructed station passenger flow distribution network and the calculated network node centrality index; The second calculation module is used to solve the constructed passenger flow monitoring node location optimization model and obtain the station passenger flow monitoring point location plan.

8. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the station passenger flow monitoring point site selection method based on a complex network as described in any one of claims 1-6 is implemented.

9. A computer device, characterized in that: It includes a memory and a processor, the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the station passenger flow monitoring point site selection method based on a complex network as described in any one of claims 1-6.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes instructions for implementing the method for selecting a station passenger flow monitoring point based on a complex network as described in any one of claims 1-6.

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