Multi-Mode Public Transportation Network Voronoi Diagram Construction and Geographic Analysis Method

Abstract

The present invention provides a method for constructing a Voronoi diagram and performing geographical analysis of a multi-modal public transportation network, which includes loading a multi-modal public transportation network, including a road network and an original public transportation network; initializing the multi-modal public transportation network, adding the same virtual node to the generator points in sequence, and searching for the nearest transportation station to the generator points; searching for the shortest paths starting from the transportation stations on the public transportation network, including searching for the shortest paths from the nearest transportation station to the generator points to all other transportation stations; searching for the shortest paths of the multi-modal network, including calculating the shortest paths of all nodes on the multi-modal network, which includes road nodes and transportation stations; constructing a Voronoi diagram of the multi-modal public transportation network to generate an accessible area for each generator point. The method of the present invention can provide an efficient Voronoi diagram generation scheme for geographical space analysis such as service area estimation, infrastructure accessibility assessment, network space optimization, location-based services, etc.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent application analysis of traffic data, and particularly to a method for constructing a Voronoi diagram and geographical analysis of a multi-modal public transport network. Background Art

[0002] The Voronoi diagram (abbreviated as VD diagram), also known as the Thiessen polygon, is a geometric structure widely used in the analysis of geospatial phenomena. Given a finite set of points (referred to as generators), the VD diagram divides the geospatial into a set of sub-regions such that each sub-region contains only one generator and all points within the region are closest to the generator point. The VD plays a key role in various spatial analysis applications, such as service area estimation, accessibility assessment, network space optimization, location-based services, etc. However, the traditional road traffic network Voronoi diagram (abbreviated as RVD diagram) takes the single-modal traffic mode as the research object, ignoring the diversity of traffic trips in the urban environment, especially multi-modal public transport, such as subways, buses, etc. The traditional RVD diagram algorithm cannot be applied to multi-modal public transport networks due to the inherently complex organizational structure of public transport, namely the diversity of travel modes, such as the combination of subways and buses, and the dynamic nature of travel time. In many cities in China, especially large cities, the multi-modal public transport network is the main mode, and there is a lack of an effective method for constructing a multi-modal public transport network Voronoi diagram (abbreviated as TVD diagram) to analyze geographical phenomena under the multi-modal public transport network, such as the service area of a supermarket and the accessibility of infrastructure.

[0003] Related literature includes:

[0004] Okabe, A., Satoh, T. and Furuta, A., 2008, Generalized network Voronoi diagrams: Concepts, computational methods, and applications. International Journal of Geographical Information Science, 22, pp. 965-994.

[0005] Ai, T., Yu, W. and He, Y., 2015, Generation of constrained network Voronoi diagram using linear tessellation and expansion method. Computers, Environment and Urban Systems, 51, pp. 83-96. Summary of the Invention

[0006] The present invention proposes the concept and construction method of a multi-modal public transportation Voronoi diagram to solve the geographical analysis problem under a multi-modal public transportation network.

[0007] The technical solution of the present invention provides a method for constructing a multi-modal public transportation network Voronoi diagram, including the following steps:

[0008] Step 1: Load a multi-modal public transportation network, including a road network and an original public transportation network;

[0009] Step 2: Initialize the multi-modal public transportation network, add the same virtual node to the generator points in sequence, and search for the nearest transportation station to the generator points;

[0010] Step 3: Search for the shortest paths starting from the transportation stations on the public transportation network, including searching for the shortest paths from the nearest transportation station to the generator points to all other transportation stations;

[0011] Step 4: Search for the shortest paths in the multi-modal network, including calculating the shortest paths of all nodes in the multi-modal network, including road nodes and transportation stations;

[0012] Step 5: Construct the Voronoi diagram of the multi-modal public transportation network to generate an accessible area for each generator point.

[0013] Moreover, the implementation method of Step 2 is as follows:

[0014] Let a given set L = {l1,..., l n} containing n generator points and a virtual node be set. For each generator point l u ∈L (1 ≤ u ≤ n), construct a virtual edge pointing to the virtual node l0 Set the walking time of the virtual edge Solve for the maximum walking time Within the range, find the path from the virtual node to the nearest station And save the time information from the virtual node to this station Denoted by where l u Represents the nearest generator node to this station y a Represents the arrival time from the generator point l u To this station And Is the minimum total travel time from the generator point l u To the station On the multi-modal public transportation network; For the total walking time during the journey from the generator point l u to the site ; Indicates the path information from the generator point l u to the site ; Then, delete the virtual nodes and virtual edges.

[0015] Moreover, the implementation of step 3 is as follows,

[0016] For each nearest site Calculate the arrival time from the generator point to as where y0 is the departure time from the generator point, is the walking time from the generator point l u to the site ; For each nearest site Calculate the journey time information from the site to each site as where y av represents the arrival time from the generator point l u to this site , is the minimum total journey time on the multimodal public transport network from the generator point l u to the site , is the total walking time during the journey from the generator point l u to the site ; is the path information from the generator point l u to the site ;

[0017] If there is a path from another generator point l to the site u1 , denoted as the journey time information is where, y av `, respectively represent the arrival time using the path from the generator point l u1 to the site point l u1 , the total journey time, the total walking time and the path information; then compare the two journey time information;

[0018] If y av `<y av , it indicates that there is a path from l u1 to the current site If the travel time is less, update the travel time information of the current station with Update the original

[0019] Otherwise, no operation is required.

[0020] Moreover, the implementation of step 4 is as follows.

[0021] For each station with a walking time less than the maximum walking time of calculate the coverage on the multi-modal public transportation network. When calculating, initialize the distance value of each station to the total walking time and save the path information of each node on the multi-modal public transportation network; add all stations with a walking time less than the maximum walking time of to the calculation queue and sort them in ascending order according to the total travel time. Each station in the queue contains travel time information to obtain its walking time to the adjacent node and a travel time information where represents the edge connecting station and road node ;

[0022] If t represents the walkable time, it is necessary to determine whether node n j has travel time information:

[0023] If there is no travel time information, set the travel time information of

[0024] If there is another path from the generator node l u1 to reach denoted as whose travel time information is then compare the two travel time information. If it means that the travel time of the new travel path to the current station is less, then update the travel time information of the current node with update the original

[0025] Otherwise, no operation is required;

[0026] If it means that within the time threshold range, from station Unreachable node On an edge Find a point such that and add to the multimodal network, set the travel time information to be

[0027] Moreover, the implementation of step 5 is as follows.

[0028] For each generator point in the set L of generator points, a set is formed by the points and edges in the corresponding sub-region of the Voronoi diagram.

[0029] Let for any point on the multimodal transportation network Record a generator point l that is the closest in the travel time information k , and according to the generator point information, add the node to the corresponding set;

[0030] For each edge W in the road network G Judge the set to which the current edge belongs according to the generator point information in the travel time information of the head and tail nodes respectively.

[0031] Through the above process, the rapid construction of the Voronoi diagram of the multimodal public transportation network is realized.

[0032] On the other hand, the present invention also provides a geographical analysis method for a multimodal public transportation network, which realizes geographical analysis according to a method for constructing a Voronoi diagram of a multimodal public transportation network as described above.

[0033] Moreover, it includes the following steps:

[0034] Step 1, load the multimodal public transportation network, including the road network and the original public transportation network;

[0035] Step 2, initialize the multimodal public transportation network, sequentially add the same virtual node to the generator points, and search for the nearest transportation station to the generator points;

[0036] Step 3, search for the shortest path starting from the transportation station on the public transportation network, including searching for the shortest paths from the nearest transportation station to the generator points to all other transportation stations;

[0037] Step 4, search for the shortest path of the multimodal network, including calculating the shortest paths of all nodes on the multimodal network, which includes road nodes and transportation stations;

[0038] Step 5: Construct the Voronoi diagram of the multi-modal public transportation network to generate an accessible area for each generator point.

[0039] Step 6: Conduct relevant geographical analysis, including, based on the Voronoi diagram obtained under the multi-modal public transportation network, for each sub-region, statistically analyze relevant geographical data information and extract the differences in the geographical data information within the sub-region.

[0040] Moreover, it is used for geospatial analysis, and the geospatial analysis is service area estimation, infrastructure accessibility assessment, network space optimization, or location-based services.

[0041] Compared with the prior art, the present invention has the following differences and advantages:

[0042] It can quickly complete the construction of the Voronoi diagram under the multi-modal public transportation network, while the traditional Voronoi diagram construction method cannot construct the multi-modal public transportation Voronoi diagram.

[0043] The method of the present invention can provide an efficient automated construction method of the Voronoi diagram for geospatial analysis such as service area estimation, infrastructure accessibility assessment, network space optimization, and location-based services, and supports intelligent geographical analysis, having prospects for business and popularization applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flowchart of an embodiment of the present invention;

[0045] Figure 2 It is the construction result of the Voronoi diagram in the schematic diagram of an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] The technical solution of the present invention will be further processed below in conjunction with the drawings and embodiments.

[0047] The processing object of the present invention is the urban multi-modal public transportation network; it can quickly construct the Voronoi diagram of the multi-modal public transportation network and conduct relevant geographical analysis, including service area estimation, accessibility of facilities, location-based services, network optimization, etc.

[0048] The present invention provides a method for constructing a multi-modal public transportation Voronoi diagram, which uses travel time to measure spatial distance. That is, within the urban space range, given a finite set of points (referred to as generators), the TVD diagram divides the urban space into a set of sub-regions, such that each sub-region contains only one generator and the travel time of all points within the region to the generator point on the multi-modal public network is minimized.

[0049] Embodiment 1

[0050] See Figure 1 , a method for constructing a multi - mode public transport Voronoi diagram provided by the embodiment provides a fast construction scheme of a Voronoi diagram for a multi - mode public transport network, including the following steps:

[0051] Step 1, load the multi - mode public transport network, including the bus network and the road network;

[0052] In the embodiment, the specific implementation manner of Step 1 is preferably as follows. The multi - mode public transport network is represented by G M =(G W , G B ), where the superscript M represents the multi - mode public transport network, the superscript W represents the road network, and the superscript B represents the original public transport network.

[0053] Among them, G W (N W , A W ) represents the road network, which contains a set of sets representing road nodes a set representing the pedestrian road edges each edge connects the i - th road node and the j - th road node allows two - way walking and has a travel time, denoted as

[0054] The other is the original public transport network G B =(N B , R B , S B , Π B ), where, represents the set of traffic stops (subway stations or bus stations); represents the set of traffic lines ; represents the set of service timetables of traffic lines , p represents the label of the traffic route; represents the set of transfer edges between different traffic stops, represents the transfer edge from the v - th traffic stop to the w - th traffic stop . Each traffic stop has a set of pedestrian transfer edge sets, denoted by , which connects adjacent nodes (such as road intersections) or traffic stops. In addition, each traffic stop has a set of traffic lines, denoted by Indicates that p represents the number of the station traffic route. Each traffic route There is a set timetable. Indicates that transportation services can be provided at different times.

[0055] Step 2: Initialize the multimodal network and add the same virtual node to each generator point in turn. Search for the nearest transportation station to the generator point.

[0056] In the embodiment, the specific implementation of step 2 is preferably as follows:

[0057] Assume that any point in the multimodal public transportation network is a generator point. First, a generator is given. For example, three parks are respectively regarded as a generator point to form a set, namely, a generator.

[0058] Suppose there is a set of n generator points L = {l1,...,l n} and a virtual node. For each generator point l u ∈L(1≤u≤n) constructs a virtual edge pointing to the virtual node l0 Set the walking time for the virtual edge Solving the Maximum Walking Time Using the Improved Dijkstra Algorithm From the virtual node to the nearest site within range The path and save the travel time information at each nearest station, that is, from the virtual node to this station Time information, use Indicates that, where l u Indicates the distance from this site The nearest generator node, y a Represents the point l from the generator u Go to this site Arrival time, From the generator point l u To the site Minimum total journey time on a multimodal public transport network. Some journeys may require a combination of walking and public transport, so the total journey time will include both the walking and public transport components. From the generator point l u To the site Total walking time in the trip, Represents the point l from the generator u To the site Path information (including walking part and public transportation part). Compared with the standard Dijkstra algorithm, the improved Dijkstra algorithm only calculates the time range less than Then, delete the virtual nodes and virtual edges.

[0059] Step 3: Search for the shortest paths starting from transportation stations on the public transportation network, including searching for the shortest paths from the transportation station closest to the generator point to all other transportation stations;

[0060] In the embodiment, the specific implementation method of Step 3 is preferably as follows

[0061] For each nearest station The arrival time can be calculated, that is, the arrival time from the generator point to its nearest transportation station is where y0 is the departure time from the generator point, is the walking time from the generator point l u to the station Next, for each nearest station The shortest path algorithm for the bus network is used to calculate the travel time information from the station to each station The travel time information is denoted as where y av represents the arrival time from the generator point l u to this station is the minimum total travel time on the multimodal public transportation network from the generator point l to the station u to the station is the total walking time in the travel from the generator point l to the station u to the station is the path information. If there is a path from another generator point l to the station u to the station denoted as and its travel time information is u1 where, y is the travel time information, where, y av `, respectively represent the arrival time, total travel time, total walking time, and path information using the path from the generator point l u1 to the station point l u1 Then compare the two travel time information. If:

[0062] y av `<y av it indicates that there is a path from l u1 to the current station such that the travel time is less, then update the travel time information of the current station, that is, use Update the original

[0063] On the contrary, no operation is required.

[0064] Step 4, search for the shortest path in the multi-modal network, including calculating the shortest paths of all nodes in the multi-modal network, which includes road nodes and transportation stations;

[0065] In the embodiment, the specific implementation method of Step 4 is preferably as follows

[0066] For each transportation station The total walking time is saved in its travel time information and denoted as For each station with a walking time less than the maximum walking time of the station Its coverage in the multi-modal public transportation network can be further calculated. The embodiment uses an improved Dijkstra algorithm to complete this task. Compared with the classical Dijkstra algorithm, the present invention has made two improvements:

[0067] 1. During the initialization process of the improved Dijkstra algorithm, the distance value of each station is initialized to the total walking time, and the path information of each node in the multi-modal public transportation network is saved;

[0068] 2. During the initialization process of the improved Dijkstra algorithm, all stations with a walking time less than the maximum walking time of the station are added to the calculation queue and sorted in ascending order according to the total travel time. Next, for each station in the queue which contains travel time information the walking time to its adjacent node can be obtained, that is (this information has been loaded in the road network), and a travel time information, where represents the edge connecting station and road node of the edge.

[0069] For this travel time information, there are the following two possibilities

[0070] (1) If t represents the walkable time (if the total walking time exceeds the upper limit, the user may not be willing to adopt this itinerary), then it is necessary to judge whether node n j has travel time information: if there is no travel time information, then set the travel time information of to be, If there is another path from the generator node l u1 to reach denoted as and its travel time information is then compare the two travel time information

[0071] If it indicates that the travel time of the new travel path to the current station is less, then update the travel time information of the current node, that is, use to update the original

[0072] Otherwise, no operation is required.

[0073] (2) If it indicates that within the time threshold range, it is impossible to reach the node from the station A point can be found on the edge such that and add to the multi-modal network, set The travel time information is

[0074] Step 5, construct the Voronoi diagram of the multi-modal public transport network to generate an accessible area for each generator point.

[0075] In the embodiment, the preferred specific implementation of step 5 is as follows

[0076] For each generator point l in the generator point set L k ∈ L, it can be set that l k In the corresponding sub-region of the Voronoi diagram, the points and edges form a set, denoted as Ω k k For any point on the multi-modal transport network

[0077] including nodes and transportation stations, a generator point closest to this point is recorded in its travel time information, denoted as l k , thus, each node can be added to the corresponding set according to the generator point information, that is ∪ represents the set operation, add.

[0078] Next, for each edge W in the road network G The set to which the current edge belongs can be determined according to the generator point information in the travel time information of its head and tail nodes respectively. Let the generator point information of its head and tail nodes be l(n​​i ) and l(n j ), the following judgments can be made:

[0079] 1. l(n i ) = l(n j ) = l k (l k ∈L), then belongs to the set Ω k .

[0080] 2. Or then no operation is required. Among them, represents empty.

[0081] Through the above process, the rapid construction of the Voronoi diagram of the multi-modal public transportation network can be automatically carried out. Refer to the construction result legend in Figure 2 , and the corresponding sub-regions are generated for the generator points 1 and 2 respectively.

[0082] Example 2

[0083] Example 2 provides a geographical analysis method based on the construction of the Voronoi diagram of the multi-modal public transportation network. First, perform the same steps 1-5 as in Example 1, and then perform step 6:

[0084] Step 6, relevant geographical analysis.

[0085] Based on steps 1-5, the Voronoi diagram under the multi-modal public transportation network can be obtained, that is, the entire multi-modal public transportation network is divided into different sub-regions so that each sub-region only contains one generator point l n ∈L, and the travel time of any point in each sub-region to the generator point on the multi-modal network is the shortest. For each sub-region Ω u , its relevant geographical data information can be statistically analyzed, the differences in the geographical data information within the sub-region can be extracted, and targeted geographical analysis, evaluation or optimization can be carried out.

[0086] For example, if analyzing the service scope of infrastructure (such as parks) in the urban area, the generator points are all parks in the city. According to steps 1-5, the sub-regions of each park on the multi-modal public transportation network can be obtained, and the total path distance within the sub-region can be statistically analyzed. In this way, the service area differences of parks in the city can be reflected, and based on the differences in the service areas of each park, automatic alarms can be provided or users can be prompted to make targeted optimization adjustments.

[0087] In specific implementation, the method proposed by the technical solution of the present invention can be automatically run by those skilled in the art using computer software technology. The system device for implementing the method, such as a computer-readable storage medium storing the corresponding computer program of the technical solution of the present invention and a computer device including the operation of the corresponding computer program, should also be within the protection scope of the present invention.

[0088] In some possible embodiments, a multi-modal public transportation network Voronoi diagram construction system is provided, including a processor and a memory. The memory is used to store program instructions, and the processor is used to call the stored instructions in the memory to execute a multi-modal public transportation network Voronoi diagram construction method as described above.

[0089] In some possible embodiments, a multi-modal public transportation network Voronoi diagram construction system is provided, including a readable storage medium. A computer program is stored on the readable storage medium, and when the computer program is executed, a multi-modal public transportation network Voronoi diagram construction method as described above is implemented.

[0090] The specific embodiments described herein are merely illustrative of the spirit of the present invention. Those skilled in the art of the present invention can make various modifications or supplements to the described specific embodiments or use similar ways to replace them, but will not deviate from the spirit of the present invention or exceed the scope defined by the appended claims.

Claims

1. A method for constructing a Voronoi diagram of a multimodal public transportation network, characterized in that: It includes the following steps: Step 1, load the multi-modal public transportation network, including the road network and the original public transportation network; Step 2, initialize the multi-modal public transportation network, sequentially add the same virtual node to the generator points, and search for the nearest transportation station of the generator points; Step 3, search for the shortest paths starting from the transportation stations on the public transportation network, including searching for the shortest paths from the nearest transportation station of the generator points to all other transportation stations; Step 4, search for the shortest paths of the multi-modal network, including calculating the shortest paths of all nodes on the multi-modal network, where the nodes include road nodes and transportation stations; Step 5, construct the Voronoi diagram of the multi-modal public transportation network, and generate an accessible area for each generator point; The implementation method of Step 2 is as follows, Suppose there is a set of n generator points L = {l1,...,l n } and a virtual node, for each generator point l u ∈L(1≤u≤n) constructs a virtual edge pointing to the virtual node l0 Set the walking time for the virtual edge Solve for the maximum walking time From the virtual node to the nearest site within range The path from the virtual node to this site is saved Time information, use Indicates that, where l u Indicates the distance from this site The nearest generator node, y a Represents the point l from the generator u Go to this site Arrival time, From the generator point l u To site Minimum total journey time on a multimodal public transport network; From the generator point l u To site Total walking time in the trip, Represents the point l from the generator u To site Path information; then, delete the virtual nodes and virtual edges; The implementation method of Step 3 is as follows, For each of the nearest stations calculate the arrival time from the generator point to as where y0 is the departure time from the generator point, is the walking time from the generator point l u to the station For each of the nearest stations calculate the travel time information from the station to each station where y av represents the arrival time at this station from the generator point l u to is the minimum total travel time on the multimodal public transport network from the generator point l u to the station is the total walking time in the journey from the generator point l u to the station is the path information from the generator point l u to the station ;​​​​ If the site There is a path from another generator point l u1 Arriving at, denoted as The travel time information is Wherein Respectively represent the arrival time using the path From the generator point l u1 To the arrival point of the station point l u1 The arrival time, the total travel time, the total walking time and the path information; then compare the two travel time information; If y av ` <y av , it means there is a path from l u1 Go to current site To make the travel time shorter, the travel time information of the current station is updated and the Update the original Otherwise, no operation is required.

2. The method for constructing a Voronoi diagram of a multimodal public transportation network according to claim 1, wherein: The implementation method of Step 4 is as follows, For each walking time less than the maximum walking time Site Calculate coverage on a multimodal public transportation network, initializing each station during the calculation The distance value is the total walking time, and the path information of each node on the multi-modal public transportation network is saved; all nodes with walking time less than the maximum walking time are Site Add to the calculation queue and sort in ascending order according to the total travel time. Each station in the queue Include travel time information Get its adjacent nodes Walking time and a travel time information in, Indicates a connected site and road nodes edge; If t represents the walkable time, then it is necessary to determine whether node n j has travel time information: If there is no travel time information, set the travel time information of If there is another link from the generator node l u1 Path arrival Recorded as Its travel time information is, Then compare the two travel time information, if This indicates the new route to the current station. If the travel time of the node is less, the travel time information of the current node is updated and the Update the original ; Otherwise, no operation is required; If it indicates that within the time threshold range, from the site it is impossible to reach the node Find a point on the edge such that and add to the multi-mode network, and set the travel time information as ​ 3. The method for constructing a Voronoi diagram of a multi-modal public transportation network according to claim 2, wherein: The implementation method of Step 5 is as follows, For each generator point in the generator point set L, a set is formed by the points and edges in the corresponding sub-region of the Voronoi diagram; Let for any point on the multi-modal transportation network record a generator point l that is the closest in the travel time information k , and according to the generator point information, add the node to the corresponding set; For the road network G W For each edge Based on the generator point information in the travel time information of the head and tail nodes respectively, determine the set to which the current edge belongs; Through the above process, the rapid construction of the Voronoi diagram of the multi-modal public transportation network is realized.

4. A geographical analysis method for a multi-mode public transport network, characterized in that: Geographical analysis is realized according to a method for constructing the Voronoi diagram of a multi-modal public transportation network as described in any one of claims 1-3.

5. The multi-modal public transportation network geographical analysis method according to claim 4, wherein: It includes the following steps: Step 1, load the multi-modal public transportation network, including the road network and the original public transportation network; Step 2, initialize the multi-modal public transportation network, sequentially add the same virtual node to the generator points, and search for the nearest transportation station of the generator points; Step 3, search for the shortest paths starting from the transportation stations on the public transportation network, including searching for the shortest paths from the nearest transportation station of the generator points to all other transportation stations; Step 4, search for the shortest paths of the multi-modal network, including calculating the shortest paths of all nodes on the multi-modal network, where the nodes include road nodes and transportation stations; Step 5, construct the Voronoi diagram of the multi-modal public transportation network, and generate an accessible area for each generator point; Step 6, relevant geographical analysis, including according to the obtained Voronoi diagram under the multi-modal public transportation network, for each sub-region, statistically analyze relevant geographical data information, and extract the differences in the geographical data information within the sub-region.

6. The geographical analysis method for the multi-modal public transport network according to claim 4, characterized in that: For geospatial analysis, the geospatial analysis is service area estimation, infrastructure accessibility assessment, network space optimization, or location-based service.

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