Public transport network analysis method and system

Abstract

The invention relates to a public transport network analysis method and system, and the method comprises the steps: carrying out the segmentation of a bus road network and a subway road network in a preset region, and obtaining bus road network nodes, subway road network nodes, and subway line sections; based on the bus road network nodes, the subway road network nodes and the subway line sections, line matching between the bus road network and the subway road network is carried out, and a line matching result is obtained; and based on the line matching result, analyzing the line repetition degree of the public transport network to obtain the line repetition degree in the preset area. According to the method and the device, the position and the length of the repeated route between the two road networks are quickly and accurately identified, the road network repetition coefficient is further obtained according to the length of the repeated route, a theoretical basis is provided for future subway route planning or bus route planning, and the problem of how to accurately analyze a complex road traffic network is solved.

Description

Technical Field

[0001] The present application relates to the field of public transportation technology, and in particular to a public transportation network analysis method and system. Background Art

[0002] Public transportation network evaluation indicators can be used to reflect the development of urban public transportation. Specific indicators include line length, network density, line repetition coefficient, line non-linearity coefficient, network site density, network site spacing, network site coverage, etc. Among them, the line repetition coefficient mainly refers to the bus line repetition coefficient, which reflects the density of the bus network, and then judges whether the planning of the bus network is reasonable.

[0003] However, as subways are opened in major cities one after another, public transportation networks are becoming increasingly complex, with different public transportation networks crisscrossing each other. The general line repetition coefficient is only analyzed from the bus network level, making it difficult to analyze the relationship between different bus networks.

[0004] Currently, no effective solution has been proposed for the problem of how to accurately analyze complex road traffic networks in related technologies. Summary of the invention

[0005] The embodiments of the present application provide a public transportation network analysis method and system to at least solve the problem of how to accurately analyze a complex road traffic network in the related art.

[0006] In a first aspect, an embodiment of the present application provides a method for analyzing a public transportation network, the method comprising: The bus network and the subway network in the preset area are divided respectively to obtain bus network nodes and subway network maps, wherein the subway network map includes subway network nodes and subway line sections; Based on the bus network nodes, the subway network nodes and the subway line sections, line matching is performed between the bus network and the subway network to obtain a line matching result; Based on the route matching result, the route duplication degree of the public transportation network is analyzed to obtain the route duplication degree in the preset area.

[0007] In some embodiments, performing route matching between the bus network and the subway network based on the bus network nodes, the subway network nodes, and the subway line sections includes: Based on the subway network nodes, construct effective matching constraints; On the basis of the effective matching constraints, based on the projection points of the bus network nodes onto the subway line sections, the subway matching sections of each of the bus network nodes are determined from the subway line sections.

[0008] In some embodiments, based on the projection point from the bus network node to the subway line segment, determining the subway matching segment of each bus network node from the subway line segment includes: Based on the projection points of the bus network nodes onto the subway line sections, matching the candidate subway sections of each bus network node from the subway line sections; Under the framework of the hidden Markov model, the unique subway matching section of each bus network node is determined from the candidate subway sections by using the Viterbi algorithm.

[0009] In some embodiments, based on the projection point of the bus network node onto the subway line segment, matching the candidate subway segments of each bus network node from the subway line segment includes: Taking the bus network node as the center, a bus buffer zone with a preset radius is created, wherein the subway line section involved in the bus buffer zone is the initial subway candidate section; Constructing a projection point from the bus network node to the initial candidate subway section, wherein the projection distance of the projection point is the shortest distance from the bus network node to the candidate subway section; If the constructed projection point coincides with the endpoint of the initial candidate subway section, the candidate subway section is eliminated to obtain the candidate subway sections of each bus network node.

[0010] In some embodiments, under the framework of the hidden Markov model, determining the unique subway matching section of each bus network node from the subway candidate sections by using the Viterbi algorithm includes: Calculate the state transition probability and single state generation probability of each bus network node on the corresponding subway candidate section; Based on the state transition probability and the single state generation probability, a recursive calculation is performed on each bus network node through the Viterbi algorithm under the hidden Markov model framework to update F MAT and I MAT ,in, F MAT is a final state generation probability hash table used to store the final state generation probability, I MATGenerate an optimal index hash table for recording the probability of reaching the final state; based on F MAT and I MAT Determine the unique subway matching section for each of the bus network nodes.

[0011] In some embodiments, constructing effective matching constraints based on the subway network nodes includes: Based on the segmentation interval of the subway network nodes, a minimum number of continuous matching nodes is obtained as a constraint variable of the effective matching constraint; Among them, the effective matching constraint is: for the subway matching section matched by the bus network node, if the number of subway network nodes involved in the subway matching section is less than the minimum number of continuous matching nodes, the matched subway matching section is eliminated.

[0012] In some embodiments, after determining the subway matching section of each of the bus network nodes from the subway line section, the method includes: Based on the subway matching sections of each of the bus network nodes, matching bus sections and subway matching sections are obtained; The angle overlap between the bus route section and the subway matching route section is checked by the angle, and the subway matching route section whose angle overlap is less than a preset angle threshold is eliminated.

[0013] In some embodiments, based on the line matching results, the line duplication degree of the public transportation network is analyzed to obtain the line duplication degree in the preset area, including: The formula for calculating route duplication through the public transportation network is:

[0014] The line repetition degree in the preset area is calculated, wherein: φ j The first j The repetition coefficient of the subway lines, is the repetition coefficient of the subway network in the preset area, N The bus network in the preset area. K The subway network in the preset area. L j The first j The length of the subway line, For the i Bus routes and j Duplicate sections of subway lines p Length, For the i Bus routes and k Duplicate sections of subway lines p Length, It is the total length of the subway network in the preset area.

[0015] In some embodiments, before dividing the bus network in the preset area to obtain bus network nodes, the method includes: Based on the location of the subway network in a preset area, generating a subway buffer zone around each subway line in the subway network, wherein the subway buffer zone covers a preset distance range of the subway line; From the bus network in the preset area, the bus routes located in the subway buffer zone are screened out to obtain the bus network related to the subway network.

[0016] In a second aspect, an embodiment of the present application provides a public transportation network analysis system, which is used to execute the method described in the first aspect above, and the system includes a road network segmentation module, a route matching module and a repetition analysis module.

[0017] The road network segmentation module is used to segment the bus road network and the subway road network in the preset area respectively to obtain bus road network nodes and subway road network maps, wherein the subway road network map includes subway road network nodes and subway line sections; The line matching module is used to perform line matching between the bus network and the subway network according to the bus network nodes, the subway network nodes and the subway line sections to obtain a line matching result; The repetition analysis module is used to analyze the degree of line repetition of the public transportation network according to the line matching result, so as to obtain the line repetition in the preset area.

[0018] Compared with the related art, the embodiment of the present application provides a public transportation network analysis method and system, wherein the method obtains bus network nodes, subway network nodes and subway line sections by dividing the bus network and subway network in a preset area respectively; based on the bus network nodes, subway network nodes and subway line sections, line matching is performed between the bus network and the subway network to obtain line matching results; based on the line matching results, the line repetition degree of the public transportation network is analyzed to obtain the line repetition degree in the preset area, thereby realizing the segmentation of the bus network and the subway network, and matching the segmented bus network nodes to the subway network through map matching, which can quickly and accurately identify the location and length of repeated lines between the two networks, and then obtain the network repetition coefficient according to the length of the repeated lines, thereby providing a theoretical basis for future subway line planning or bus line planning, and solving the problem of how to accurately analyze complex road traffic networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 is a flowchart of the steps of the public transportation network analysis method according to an embodiment of the present application; Figure 2 is a schematic diagram of bus network screening according to an embodiment of the present application; Figure 3 is a schematic diagram of an incorrect match according to an embodiment of the present application; Figure 4 is a schematic diagram of determining candidate subway sections according to an embodiment of the present application; Figure 5 is a schematic diagram of angle inspection according to an embodiment of the present application; Figure 6 It is a schematic diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated 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 application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0021] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

[0022] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0023] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantity limitation, and may indicate the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and / or B" can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0024] The present application embodiment provides a public transportation network analysis method. Figure 1 is a flowchart of the steps of the public transportation network analysis method according to an embodiment of the present application, such as Figure 1As shown, the method comprises the following steps: Step S102, dividing the bus network and the subway network in the preset area respectively to obtain bus network nodes and a subway network map, wherein the subway network map includes subway network nodes and subway line sections; Specifically, step S102 divides the bus network nodes into "track points" according to a given distance threshold, and divides the subway lines into sections, thereby obtaining bus network nodes and subway network maps. In other words, the original input data is the bus network and the subway network, and after segmentation, the bus network nodes are obtained as the point set to be matched. The field table of the bus network nodes is shown in Table 1; Table 1 Serial number Fields type meaning 1 point_id Int ID of the point to be matched 2 line_id String The bus line ID to which the matching point belongs 3 line_name String The name of the bus line to which the point to be matched belongs 4 length Float The length of the bus line to which the matching point belongs, in km 5 lng Float Longitude of the point to be matched 6 lat Float Latitude of the point to be matched 7 time String Positioning timestamp After the subway network is segmented, a subway network graph is formed. The nodes of the graph are the nodes of the subway network (subway network nodes), and the edges of the graph are the subway line sections between the corresponding nodes (subway line sections). The field tables of the subway network graph are shown in Tables 2 and 3.

[0025] Table 2 Serial number Fields type meaning 1 node_id Int Node unique code, must be a positive integer greater than 0 2 geometry Geometry Node geometry type Table 3 Serial number Fields type meaning 1 link_id Int The unique code of the subway line must be a positive integer greater than 0 2 from_node Int The node number of the starting point of the road section topology, which must be a positive integer greater than 0 3 to_node Int The node number of the starting point of the road section topology, which must be a positive integer greater than 0 4 dir Int Road section direction, the value is 0 or 1, 0 represents two-way traffic, 1 represents the traffic direction is the forward direction of the road section topology 5 length Float Section length, in m 6 geometry Geometry Segment geometry type In some specific embodiments, before step S102 of dividing the bus network in the preset area into bus network nodes, the method further includes step S101: Figure 2 is a schematic diagram of bus network screening according to an embodiment of the present application, such as Figure 2 As shown, specifically: First, based on the location of the subway network in the preset area, a subway buffer zone is generated around each subway line in the subway network, wherein the subway buffer zone covers a preset distance range of the subway line; Secondly, from the bus network in the preset area, the bus routes located in the subway buffer zone are screened out to obtain the bus network related to the subway network.

[0026] It should be noted that the layout of subway lines is subject to high construction costs, usually presenting large intervals, and relying on transfer stations to achieve interconnection between different lines. In contrast, bus route planning utilizes existing road infrastructure, requires less engineering work, is low-cost, can reach every corner of the city, and is a more extensive and detailed service network. Therefore, a complete bus route is likely to match multiple subway lines in the direction of travel, but when passing through remote areas, there may be a situation where no matching subway line can be found. To solve this problem, it is necessary to set a distance threshold first to ensure that each bus route can find a corresponding subway line match within a reasonable range to avoid matching failures due to excessive geographical intervals.

[0027] In some preferred embodiments, for step S101, preferably, according to the relevant national standard GB / T51328-2018 "Urban Integrated Transportation System Planning Standard", the planning of urban rail transit (including express lines and trunk lines) needs to take into account both efficiency and coverage, especially emphasizing the development intensity and population concentration within 800 meters around the station, that is, theoretically starting from any subway station, walking for 10 minutes (about 600 meters to 900 meters, the standard recommends 800 meters) should be able to cover enough passenger sources or destinations. Based on this standard, firstly, GIS technology is used to generate an 800-meter buffer for all subway lines to define the effective service range of the subway station. Subsequently, each bus line is analyzed to check whether its path crosses these buffers. Whenever a bus line enters the boundary of the subway buffer, it is marked as a "simulated GPS track starting point", and the corresponding exit point is regarded as the "simulated GPS track end point", thereby forming a continuous, subway-related track segment. For those bus nodes that are completely located in the 800-meter area without subway coverage, since it is difficult to effectively connect to the subway service and there is no problem of line overlap, they are directly excluded from the subsequent process to reduce unnecessary computing burden and improve the accuracy and efficiency of the algorithm.

[0028] Step S104, performing route matching between the bus network and the subway network based on the bus network nodes, the subway network nodes and the subway line sections to obtain a route matching result; Step S104 specifically includes the following steps: Step S1041, constructing effective matching constraints based on subway network nodes; Specifically, step S1041 obtains the minimum number of consecutive matching nodes as a constraint variable of an effective matching constraint based on the segmentation interval of the subway network nodes; wherein the effective matching constraint is: for a subway matching section matched to a bus network node, if the number of subway network nodes involved in the subway matching section is less than the minimum number of consecutive matching nodes, the matched subway matching section is eliminated.

[0029] It should be noted that in order to ensure the accuracy and reliability of the matching, a minimum number of consecutive matching nodes needs to be set for the subway network nodes involved in the matched subway matching section. N As a constraint variable of the effective matching constraint, this is to avoid the situation where when a certain split point happens to be located at the intersection of a bus line and a subway line, there is a high probability in theory that it will be mistakenly associated with a subway line that does not actually overlap. Figure 3 is a schematic diagram of an incorrect match according to an embodiment of the present application, such as Figure 3 As shown. It can be seen that by introducing the minimum number of consecutive matching nodes N Even if the point temporarily "deviates" to the matching sequence of other lines due to its location characteristics, S Because it cannot subsequently maintain the minimum number of consecutive matching nodes N Such instantaneous mismatches will be automatically identified and excluded in the subsequent screening process, thereby ensuring the overall accuracy and effectiveness of line matching analysis. Specifically, the minimum number of consecutive matching nodes N The interval between the nodes of the subway network δ Regarding, the principle that the threshold increases as the spacing decreases, that is, the spacing δ The smaller, the corresponding N The larger the interval, for example, if the segmentation is performed at intervals of 10m, the lower limit of the number of consecutive nodes is set to 5. The implementation mechanism relies on the segment identifier in the matching results, so as to automatically identify and remove those matching items with less than 5 consecutive records on the same route.

[0030] Step S1042, based on the effective matching constraints, based on the projection points from the bus network nodes to the subway line sections, the subway matching sections of each bus network node are determined from the subway line sections.

[0031] Specifically, step S1042 matches the candidate subway sections of each bus network node from the subway line sections based on the projection points of the bus network nodes onto the subway line sections; and determines the unique subway matching section of each bus network node from the candidate subway sections through the Viterbi algorithm under the framework of the hidden Markov model.

[0032] In some preferred embodiments, step S1042 can be preferably divided into two stages: determining candidate subway sections and determining matching subway sections; Phase 1: ① With the bus network node as the center, a bus buffer zone with a preset radius is created, where the subway line section involved in the bus buffer zone is the initial subway candidate section; ② Construct the projection point from the bus network node to the initial subway candidate section, where the projection distance of the projection point is the shortest distance from the bus network node to the subway candidate section; ③ If the constructed projection point coincides with the endpoint of the initial subway candidate section, the subway candidate section is eliminated to obtain the subway candidate sections of each bus network node.

[0033] It should be noted that Figure 4 is a schematic diagram of determining candidate subway sections according to an embodiment of the present application, such as Figure 4 As shown, with the bus network node as the center, create a preset radius ( buffer ), the subway line section passing through the bus buffer is considered to be the associated initial subway candidate section. i Bus network nodes to be matched agent i , define its subway candidate section set as R i ,definition represent agent i Candidate subway lines in r j The projection point on the subway candidate section refers to the point with the minimum Euclidean distance to the bus network node. for agent i Candidate subway lines in r j projection distance.

[0034] It should be further specified that the matching between the bus network and the subway network is carried out within the framework of two known and precise coordinate systems (different from the matching of real-time GPS tracks with established networks). Their geographic information is usually based on high-precision map data or professional surveying and mapping, and has high positioning accuracy. In theory, there will be no random and non-directional position deviations, which can greatly limit the candidate range of potential matching sections. In view of this feature, the matching strategy is optimized accordingly: for all subway candidate sections of the bus network node, when the projection point of the bus network node on one of the subway candidate sections is at the endpoint of the subway candidate section, the subway candidate section is eliminated, thereby further improving the matching efficiency of the algorithm. Specifically, if Figure 4 As shown, They are bus network nodes agent 1 The lines connecting the projection points on the candidate subway sections ②, ④, and ⑥, since the projection points are located at the endpoints of the candidate subway sections, the corresponding candidate subway sections ②, ④, and ⑥ are eliminated here.

[0035] In some preferred embodiments, there may be multiple data formats based on bus network and subway network. Here, two processing logics are provided to implement "determining whether the projection point of the bus network node is located at the end point of the subway candidate section" Processing logic 1: Assume the coordinates of the bus network node to be 1 ,y 1 ), the two endpoints of the target segment are A(x 2 ,y 2 ) and B(x 3 ,y 3 ). Then we have:

[0036] When 0≤t≤1, the projection point Q is not point A or point B, and the horizontal coordinate Q of the projection point Q is x =x 2 +t*(x 3 -x 2 ), vertical coordinate Q y =y 2 +t*(y 3 -y 2 ), projection distance ; When t<0, the projection point Q is point A; when t>1, the projection point Q is point B.

[0037] Processing logic 2: Assume the longitude and latitude of the bus network node is , for the candidate subway sections r j , the number of inflection points is K , take out in turn according to the order of inflection points K +1 straight line segment, for each straight line segment: (1) The longitude and latitude of the starting point of the straight line segment Convert from degrees to radians:

[0038] (2) Calculate the azimuth from the start point to the end point of the straight line segment :

[0039] in, is the difference in longitude between the end point and the starting point of the line segment, is the latitude of the end point of the line segment.

[0040] (3) Calculate the starting point of the straight line segment and the bus network node agent Azimuth :

[0041] (4) Calculate the azimuth difference :

[0042] (5) Use the Haversian formula to calculate the starting point of the straight line segment and the bus network node agent The spherical distance between d :

[0043] in, and It is the starting point of the straight line segment and the bus network node agent The latitude and longitude differences are, and r is the average radius of the earth.

[0044] Finally, find all the calculated K +1 d Value, the minimum value recorded is the bus network node agent To the candidate subway line r j Projection distance on , while at the minimum d Find the projection point on the straight line segment corresponding to the value , the path distance from the projection point to the starting point of the road segment .

[0045] Phase 2: ① Calculate the state transition probability and single state generation probability of each bus network node on the corresponding subway candidate section; ② Based on the state transition probability and single state generation probability, the Viterbi algorithm is used to recursively calculate each bus network node in the hidden Markov model framework to update F MAT and I MAT ,in, F MAT is a final state generation probability hash table used to store the final state generation probability, I MAT Generate an optimal index hash table for recording the probability of reaching the final state; ③Based on F MAT and I MAT Determine the unique subway matching section for each bus network node.

[0046] It should be noted that the calculated state transition probability A MATand the probability of single state generation B MAT It may be very small, and direct multiplication may cause numerical underflow. Using natural logarithms to turn multiplication into addition can reduce this risk, and addition in logarithmic space is usually more stable and efficient than multiplication in probability space. Therefore, the calculation formula based on natural logarithms is:

[0047] Recursive calculation t and t index ,in, A MAT is the set of state transition probabilities, B MAT is the set of single-state generation probabilities, t is an array for temporarily storing probabilities, t index is the index of the maximum probability; Based on recursive calculation t and t index , by updating the formula renew F MAT , by updating the formula renew I MAT .

[0048] The Viterbi algorithm can effectively solve the problem of how to choose which candidate road segment to match the point to be matched to when there are multiple candidate roads. When obtaining the temporary probability generated by each point to be matched to the corresponding candidate road segment, the case with the maximum temporary probability is selected to find the most likely path in the map matching problem, that is, the path state sequence that best matches the observation data under a given observation sequence. This process not only considers the consistency between the observation point and the road network state, but also the continuity between the path states.

[0049] Step S1043, based on the subway matching sections of each bus network node, the matched bus sections and subway matching sections are obtained; the angle overlap between the bus sections and the subway matching sections is checked by angle, and the subway matching sections whose angle overlap is less than a preset angle threshold are eliminated.

[0050] It should be noted that after obtaining the matched bus sections and subway matching sections based on the subway matching sections of each bus network node, a mismatch check is required. In other words, after obtaining all the repeated sections used in the subway network and the bus network, these repeated sections must be finally checked and the matching accuracy verified to ensure that the overlap relationship between the bus lines and the subway lines is true and valid, so as to eliminate potential mismatches. Specifically, whether there is a potential overlap relationship between the distribution of bus lines and subway lines within a certain spatial range can be quickly predicted by analyzing the corresponding geometric relationship, and vector angle calculation is an effective geometric analysis method that can be used to detect the directional consistency between adjacent nodes, thereby helping to determine the degree of line overlap. Figure 5 is a schematic diagram of angle inspection according to an embodiment of the present application, such as Figure 5 As shown, for any two adjacent nodes in the bus network P 1 and P 2 , and the two adjacent nodes matched in the corresponding subway network Q 1 and Q 2 , we can define two vectors to represent the directions of the two lines:

[0051] Through the angle θ Set a reasonable threshold β (such as 30 degrees or 45 degrees). When the angle is less than the threshold, it can be considered that the bus line and the subway line have relatively consistent directions in this section, and there is a spatial overlap. The smaller the angle, the more consistent the directions of the two lines are, and the greater the possibility of overlap. Correspondingly, if the angle is large, it may be due to mismatching that the two lines are inconsistent in space. At this time, the matching relationship of this section of the line should be removed to improve the accuracy of the matching. The choice of threshold should be optimized according to the specific application scenario and actual needs.

[0052] Step S106: Based on the route matching result, the route duplication degree of the public transportation network is analyzed to obtain the route duplication degree in the preset area.

[0053] Specifically, step S106 uses the route duplication calculation formula of the public transportation network:

[0054] The line repetition within the preset area is calculated, where: φ j The first j The repetition coefficient of the subway lines, is the repetition coefficient of the subway network in the preset area, N The bus network in the preset area. K The subway network in the preset area. L j The first j The length of the subway line, For the i Bus routes and j Duplicate sections of subway lines p Length, For the i Bus routes and k Duplicate sections of subway lines p Length, is the total length of the subway network in the preset area. In addition, due to the limitations of the road network, buses may pass through multiple different lines in a certain area, while subways often only have one line passing through, so the repetition coefficient value in the area is generally greater than 1.

[0055] It should be noted that the calculation formula for the bus line repetition coefficient is the length of all lines (including repeated lines) divided by the length of the bus network. Therefore, the value is generally greater than 1. The larger the value, the higher the density of the bus network. In traffic engineering, it is mentioned that the line network repetition coefficient should be 1.25-2.5. The line repetition proposed in this embodiment is to measure the degree of repetition between subway lines and bus networks. In other words, the calculation formula for the line repetition proposed in this embodiment is the total length of the overlapping parts of different bus lines and subway lines in the preset area divided by the total length of the subway lines. Therefore, after completing the above steps, after obtaining and screening all subway lines containing repeated sections in the subway network, they can be classified according to the name of the subway line to which each line belongs, and the repetition coefficient of each subway line (that is, the repetition coefficient of the first subway line in the above preset area) can be calculated by the following formula. j The repetition coefficient of the subway lines φ j ), or it can be divided by region (i.e. the repetition coefficient of the subway network in the above preset area ).

[0056] Through the above embodiments, the segmentation of the bus network and the subway network is realized, and the segmented bus network nodes are matched to the subway network by map matching. The location and length of repeated lines between the two networks can be quickly and accurately identified, and then the subway lines that do not match the bus nodes are screened out, and finally several subway lines that overlap with the bus lines are obtained, and then the network repetition coefficient is obtained according to the length of the repeated lines, which provides a theoretical basis for future subway line planning or bus line planning and solves the problem of how to accurately analyze complex road traffic networks.

[0057] It should be noted that the steps shown in the above process or the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0058] The embodiment of the present application provides a public transportation network analysis system, which includes a road network segmentation module, a route matching module and a repetition analysis module; A road network segmentation module is used to segment the bus road network and the subway road network in a preset area respectively to obtain bus road network nodes and a subway road network map, wherein the subway road network map includes subway road network nodes and subway line sections; A route matching module is used to match routes between the bus network and the subway network according to bus network nodes, subway network nodes and subway line sections to obtain route matching results; The repetition analysis module is used to analyze the repetition degree of the routes of the public transportation network according to the route matching results, and obtain the repetition degree of the routes in the preset area.

[0059] Through the road network segmentation module, line matching module and repetition analysis module in the embodiment of the present application, the segmentation of the bus road network and the subway network is realized, and the segmented bus road network nodes are matched to the subway network by map matching. The location and length of repeated lines between the two networks can be quickly and accurately identified, and then the road network repetition coefficient can be obtained according to the length of the repeated lines, which provides a theoretical basis for future subway line planning or bus line planning and solves the problem of how to accurately analyze complex road traffic networks.

[0060] It should be noted that the above modules can be functional modules or program modules, and can be implemented by software or hardware. For modules implemented by hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0061] This embodiment further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0062] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0063] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0064] In addition, in combination with the public transportation network analysis method in the above embodiment, the embodiment of the present application can provide a storage medium for implementation. The storage medium stores a computer program; when the computer program is executed by a processor, any public transportation network analysis method in the above embodiment is implemented.

[0065] In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for analyzing a public transportation network is implemented. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball, or touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0066] In one embodiment, Figure 6 is a schematic diagram of the internal structure of an electronic device according to an embodiment of the present application, such as Figure 6 As shown, an electronic device is provided, which may be a server, and its internal structure diagram may be as shown in Figure 6As shown. The electronic device includes a processor, a network interface, an internal memory and a non-volatile memory connected through an internal bus, wherein the non-volatile memory stores an operating system, a computer program and a database. The processor is used to provide computing and control capabilities, the network interface is used to communicate with an external terminal through a network connection, the internal memory is used to provide an environment for the operation of the operating system and the computer program, the computer program is executed by the processor to implement a public transportation network analysis method, and the database is used to store data.

[0067] Those skilled in the art will understand that Figure 6 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0068] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0069] Those skilled in the art should understand that the various technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the various technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0070] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for analyzing a public transportation network, characterized in that: The method comprises: The bus network and the subway network in the preset area are divided respectively to obtain bus network nodes and subway network maps, wherein the subway network map includes subway network nodes and subway line sections; Based on the bus network nodes, the subway network nodes and the subway line sections, line matching is performed between the bus network and the subway network to obtain a line matching result; Based on the route matching result, the route duplication degree of the public transportation network is analyzed to obtain the route duplication degree in the preset area.

2. The method according to claim 1, characterized in that Based on the bus network node, the subway network node and the subway line section, performing line matching between the bus network and the subway network includes: Based on the subway network nodes, construct effective matching constraints; On the basis of the effective matching constraints, based on the projection points of the bus network nodes onto the subway line sections, the subway matching sections of each of the bus network nodes are determined from the subway line sections.

3. The method according to claim 2, characterized in that Based on the projection points of the bus network nodes onto the subway line sections, determining the subway matching sections of each of the bus network nodes from the subway line sections includes: Based on the projection points of the bus network nodes onto the subway line sections, matching the candidate subway sections of each bus network node from the subway line sections; Under the framework of the hidden Markov model, the unique subway matching section of each bus network node is determined from the candidate subway sections by using the Viterbi algorithm.

4. The method according to claim 3, characterized in that Based on the projection points of the bus network nodes onto the subway line section, matching the candidate subway sections of each bus network node from the subway line section includes: Taking the bus network node as the center, a bus buffer zone with a preset radius is created, wherein the subway line section involved in the bus buffer zone is the initial subway candidate section; Constructing a projection point from the bus network node to the initial candidate subway section, wherein the projection distance of the projection point is the shortest distance from the bus network node to the candidate subway section; If the constructed projection point coincides with the endpoint of the initial candidate subway section, the candidate subway section is eliminated to obtain the candidate subway sections of each bus network node.

5. The method according to claim 3, characterized in that: Under the framework of the hidden Markov model, the unique subway matching section of each bus network node is determined from the candidate subway sections by the Viterbi algorithm, including: Calculate the state transition probability and single state generation probability of each bus network node on the corresponding subway candidate section; Based on the state transition probability and the single state generation probability, a recursive calculation is performed on each bus network node through the Viterbi algorithm under the hidden Markov model framework to update F MAT and I MAT ,in, F MAT is a final state generation probability hash table used to store the final state generation probability, I MAT Generate an optimal index hash table for recording the probability of reaching the final state; based on F MAT and I MAT Determine the unique subway matching section for each of the bus network nodes.

6. The method according to claim 2, characterized in that Based on the subway network nodes, constructing effective matching constraints includes: Based on the segmentation interval of the subway network nodes, a minimum number of continuous matching nodes is obtained as a constraint variable of the effective matching constraint; Among them, the effective matching constraint is: for the subway matching section matched by the bus network node, if the number of subway network nodes involved in the subway matching section is less than the minimum number of continuous matching nodes, the matched subway matching section is eliminated.

7. The method according to claim 2, characterized in that After determining the subway matching section of each of the bus network nodes from the subway line section, the method includes: Based on the subway matching sections of each of the bus network nodes, matching bus sections and subway matching sections are obtained; The angle overlap between the bus route section and the subway matching route section is checked by the angle, and the subway matching route sections whose angle overlap is less than a preset angle threshold are eliminated.

8. The method according to claim 1, characterized in that Based on the line matching result, the line duplication degree of the public transportation network is analyzed to obtain the line duplication degree in the preset area, including: The formula for calculating route duplication through the public transportation network is: The line repetition degree in the preset area is calculated, wherein: φ j The first j The repetition coefficient of the subway lines, is the repetition coefficient of the subway network in the preset area, N The bus network in the preset area. K The subway network in the preset area. L j The first j The length of the subway line, For the i Bus routes and j Duplicate sections of subway lines p Length, For the i Bus routes and k Duplicate sections of subway lines p Length, It is the total length of the subway network in the preset area.

9. The method according to claim 1, characterized in that: Before dividing the bus network in the preset area into bus network nodes, the method includes: Based on the location of the subway network in a preset area, generating a subway buffer zone around each subway line in the subway network, wherein the subway buffer zone covers a preset distance range of the subway line; From the bus network in the preset area, the bus routes located in the subway buffer zone are screened out to obtain the bus network related to the subway network.

10. A public transportation network analysis system, characterized in that: The system is used to execute the method according to any one of claims 1 to 9, and the system includes a road network segmentation module, a line matching module and a repetition analysis module. The road network segmentation module is used to segment the bus road network and the subway road network in the preset area respectively to obtain bus road network nodes and subway road network maps, wherein the subway road network map includes subway road network nodes and subway line sections; The line matching module is used to perform line matching between the bus network and the subway network according to the bus network nodes, the subway network nodes and the subway line sections to obtain a line matching result; The repetition analysis module is used to analyze the degree of line repetition of the public transportation network according to the line matching result, so as to obtain the line repetition in the preset area.

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