A method, computing device and storage medium for calculating accessibility of integrated transportation multimodal network

Abstract

The present invention relates to a method, a computing device and a storage medium for calculating the accessibility of an integrated transportation multimodal network. The method comprises: constructing a network structure of an integrated transportation network, wherein the transportation network comprises a road network, a railway network and an aviation network; recording the latitude and longitude coordinates of the starting point and the end point; improving the heuristic function of the A* algorithm; a hierarchical search algorithm comprehensively considering the travel paths in the transportation network, including the intra-city travel paths of the cities where the starting point and the end point are located, and the inter-city travel paths connecting the cities where the starting point and the end point are located; calculating the total travel time; calculating the accessibility and diagnosing accessibility depressions. The present invention improves the path search algorithm with the goal of improving efficiency, and constructs a path search algorithm suitable for a multimodal transportation network from the two aspects of optimizing the search path and improving the search strategy. By introducing a hierarchical search strategy, unnecessary repeated searches are effectively reduced, and the search efficiency of the algorithm is improved. At the same time, the algorithm integrates the influence of the number of transfers and the train connection relationship on the travel path selection, and more accurately simulates the actual travel behavior of travelers.

Description

Technical Field

[0001] The present invention relates to the field of comprehensive traffic accessibility calculation, and in particular to a method, a calculation device and a storage medium for comprehensive traffic multi-modal network accessibility calculation. Background Art

[0002] In recent years, my country's transportation industry has developed rapidly, with a basic infrastructure network taking shape and a comprehensive transportation system constantly improving. However, this process still faces the reality of unbalanced and insufficient development, and the layout of the comprehensive transportation network is unable to effectively meet actual transportation needs.

[0003] We need to build a rapid transportation network centered around highways, high-speed railways, and civil aviation, enhance connectivity and strengthen the organic connection between various modes of transportation to improve travel efficiency. To achieve this goal, we need to implement quantitative accessibility evaluation and low-density diagnosis for the comprehensive transportation network.

[0004] Transport accessibility not only reflects the spatial interactions between cities but also reveals how easy it is to reach other areas from one region. It is a key indicator for measuring regional transport development and service quality. Currently, there is an imbalance and inadequacy between comprehensive transport development and local development levels. By assessing the degree to which accessibility matches regional needs, we can identify gaps in comprehensive transport development and services and improve these gaps to promote coordinated regional development. Summary of the Invention

[0005] In order to solve the above problems in the prior art, the present invention provides a method for calculating accessibility of a comprehensive transportation multimodal network, comprising the following steps:

[0006] S1. Build a comprehensive transportation network structure, which includes road network, railway network and aviation network; obtain the latitude and longitude coordinates of the starting and ending points, with the starting and ending points being the centers of each district and county.

[0007] S2. Improve the heuristic function of the A* algorithm; including:

[0008] S21. Search for a route with the least number of transfers between hub stations in the railway network and the aviation network and record the number of transfers.

[0009] S22. Calculate the intercity network characteristic indicators of the nodes, including node degree d(n) and betweenness centrality t(n). The calculation formula of betweenness centrality t(n) is as follows:

[0010]

[0011] Where M is the set of nodes in the network; n jk is the number of shortest paths between any two nodes j and k except node n; n jk(n) is the number of shortest paths between any two nodes j and k other than node n that pass through node n.

[0012] S23. Improve the heuristic function. The calculation formula of the heuristic function value f(n) is as follows:

[0013] f(n)=g(n)+h(n)*l(d(n) / p)*l(t(n)*q)

[0014] where n is the neighboring node of the current node m; f(n) is the heuristic function estimate of node n; g(n) is the travel cost from node n to the starting point; h(n) is the minimum number of transfers from node n to the destination; l() is a monotonically decreasing function; d(n) is the degree of node n; p and q are model parameters; and t(n) is the betweenness centrality of node n.

[0015] The calibration process of parameters p and q is as follows: generate several p and q parameter pairs according to the Latin hypercube sampling method, select hub stations in the study area as small cases, for example, select two hub stations in each province, search for the path with the least number of transfers between each hub station under each pair of parameter pairs, calculate the travel time of each path, and select the p and q pair with the least average travel time and number of transfers to search for the global path.

[0016] S3. Hierarchical search for travel paths in the integrated transportation network, including intra-city travel paths within the starting city and the end city, and inter-city travel paths between the starting city and the end city.

[0017] S4. Calculate the total travel time.

[0018] S5. Calculate accessibility and diagnose accessibility depressions.

[0019] Furthermore, S1 includes the following steps:

[0020] S11. Use highway network data, railway and air schedule information; highway network data includes each node of the road network, the length of roads between nodes and the road grade; railway and air schedule information includes the starting hub station, the ending hub station, the departure time and the arrival time.

[0021] S12. Record the starting point, end point and the longitude and latitude coordinates of each hub station.

[0022] S13. Construct a road network topology structure with road nodes as nodes and road section travel time as weight.

[0023] S14. Based on the Space-P method, a comprehensive transportation multimodal network G = {V, E, C, F, S} is constructed, where V and E are the set of travel nodes in the transportation network and the set of transport arcs between nodes, respectively; C is the set of transfer arcs; F is the set of starting points and ending points; and S is the set of transportation modes; the transfer arc set C includes transfers at the same station and transfers at different stations; the transportation mode set S is {H, R, A}, representing road, rail, and aviation, respectively.

[0024] Furthermore, the travel time of a road section is the ratio of the road section length to the average speed, and the road section types include expressways, national roads, provincial roads and fast roads; the speeds are preset as the first speed, the second speed, the third speed and the fourth speed respectively.

[0025] Furthermore, S3 also includes searching for a route with the least number of intercity transfers between the starting and ending cities according to rules, including:

[0026] The total number of transfers does not exceed the first threshold.

[0027] If there is no corresponding railway hub or aviation hub in the current city, choose a hub in a nearby city.

[0028] For routes within the city where the starting point is located and the route within the city where the end point is located, the default method of connection is to use a car.

[0029] The interval between the transfer service and the arrival service of the previous travel stage shall not be less than the sum of the advance waiting time and the car transfer travel time.

[0030] Furthermore, S3 also includes: taking the urban road network as the low-level network, using the Dijkstra algorithm to search for the shortest paths between the starting point and the end point and the optional hub station, and the hub station can be selected as any hub station in the city where the starting point and the end point are located; taking the intercity network as the high-level network, using the improved A* algorithm to search for the path with the least number of transfers; splicing the shortest paths of each travel plan in all levels to obtain the global optimal path.

[0031] Furthermore, S4 includes the following steps:

[0032] S41. Calculate the travel time within the city of the starting point and the travel time within the city of the end point respectively.

[0033] S42. Calculate the intercity route travel time between the starting and ending cities. Based on the route with the least number of transfers, calculate the travel time corresponding to different bus combinations to determine the shortest travel time.

[0034] S43. Calculate the total travel time using the following formula:

[0035]

[0036] Where T ij is the total travel time from region i to region j; The running time of selecting transport route k from node p to node q; It means that if both node p and node q are on transport route k, it is 1, otherwise it is 0; is the total transfer time from transport route k to transport route l at node p; If node p can realize the transfer between line k and line l, it is 1, otherwise it is 0; T α is the time that passengers usually arrive at the hub in advance when traveling between cities; N is the number of nodes in the network; n pq is the number of transport routes between node p and node q, n p is the number of transport routes at node p.

[0037] Furthermore, T α Including the first time preset by railways and the second time preset by aviation.

[0038] Furthermore, S5 includes the following steps:

[0039] S51. Use socioeconomic attribute data of each city, including GDP data and permanent population data.

[0040] S52. Calculate the reachability using the following formula:

[0041]

[0042] Where A i represents the shortest travel time accessibility value of county i; W is the number of destination areas; T ij is the total travel time from area i to area j; M j is the scale index of county j, G j is the annual GDP of the city in county j, P j is the permanent population of district or county j.

[0043] S53. Through regression fitting of the city's permanent population and accessibility, a combined distribution is obtained, and the area above the fitting line is judged as an accessibility depression.

[0044] The present invention also provides a computing device, comprising a processor and a memory, wherein the memory stores executable code, and when the executable code is executed by the processor, the processor executes the method described above.

[0045] The present invention also provides a non-transitory machine-readable storage medium having executable code stored thereon. When the executable code is executed by a processor of an electronic device, the processor is caused to execute the method described above.

[0046] The present invention comprehensively considers multi-modal travel modes and single-mode travel modes, making it more scientific and comprehensive. It searches for routes for different multi-modal and single-mode travel, calculates the travel time corresponding to the routes of different modes, and obtains the travel route of the optimal travel mode.

[0047] The proposed method improves the path search algorithm, making it more efficient and rational. It constructs a path search algorithm for multimodal transportation networks by optimizing search paths and improving search strategies. By introducing a hierarchical search strategy, unnecessary repeated searches are reduced, improving the algorithm's search efficiency. Taking into account the structural characteristics of intercity transportation networks, the method also incorporates the impact of transfer times and train connections on travel path selection, better simulating traveler behavior.

[0048] This invention considers the total travel time of the entire trip chain, including transfers, to align with actual travel times. A full trip chain is comprised of "intra-city trips in the departure city + intercity trips + intra-city trips in the destination city." The total travel time includes the sum of the traveler's customary pre-waiting time, transfer time, and travel time. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 is a flow chart of the method of the present invention;

[0051] Figure 2 This is a partial road network data table in an embodiment of the present invention;

[0052] Figure 3 This is a table of some railway and airline flight information in an embodiment of the present invention;

[0053] Figure 4 This is a table of latitude and longitude of some starting and ending points and hub stations in an embodiment of the present invention;

[0054] Figure 5 This is a table of the minimum number of transfers between some hub stations in an embodiment of the present invention;

[0055] Figure 6 This is a calibration table of some parameters in the embodiment of the present invention;

[0056] Figure 7 This is a partial intercity travel timetable in an embodiment of the present invention;

[0057] Figure 8This is a general travel timetable for some regions in an embodiment of the present invention;

[0058] Figure 9 This is a table of social and economic attributes of some regions in an embodiment of the present invention;

[0059] Figure 10 This is a table of accessibility of some areas in an embodiment of the present invention;

[0060] Figure 11 This is a bubble chart of accessibility of city cluster A in an embodiment of the present invention;

[0061] Figure 12 This is a bubble chart of accessibility of city cluster B in an embodiment of the present invention. DETAILED DESCRIPTION

[0062] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0063] Example 1:

[0064] like Figure 1 As shown, the method for calculating accessibility for an integrated multimodal transportation network in this embodiment includes the following steps: S1. Constructing an integrated transportation network structure; S2. Improving the A* algorithm by improving the heuristic function based on the characteristics of the intercity network; S3. Hierarchical search for travel paths based on the integrated transportation network; S4. Calculating the total travel time for intra-city and intercity trips; S5. Calculating accessibility and diagnosing accessibility depressions. This embodiment uses city clusters A and B as examples for illustration, and the specific implementation process is detailed below.

[0065] S1. Build a comprehensive transportation network structure, including obtaining road network data, railway and air schedule information. Road network data includes each node of the road network, the length of the road between nodes, and the road grade, such as Figure 2 Railway and air flight information includes the starting and ending hubs, departure time and arrival time, as shown in Figure 3 As shown; get the starting and ending points (starting and ending points) and the longitude and latitude coordinates of the hub station, as shown Figure 4 As shown in the figure, a road network topology is constructed with road nodes as nodes and road segment travel time as weight. The road segment travel time is the ratio of the road segment length to the average speed. The first speed, second speed, third speed and fourth speed used by expressways, national roads, provincial roads and expressways are 100 km / h respectively. -1 , 70km·h -1 , 50km·h -1, 80km·h -1 In other embodiments, different speeds can be set according to actual needs, or the user can set a specific speed. Based on the Space-P method, a comprehensive transportation multimodal network G = {V, E, C, F, S} is constructed, where V and E represent the set of travel nodes in the transportation network and the set of transportation arcs between nodes, C is the set of transfer arcs, F is the set of origin and destination points, and S is the set of transportation modes. The transfer arc set C includes two types of arcs: same-station transfers and different-station transfers; the origin and destination point set F is located in the road network and is represented as the geometric center of the geographic space unit; the transportation mode set S is {H, R, A}, representing the three modes of road, rail, and aviation, respectively.

[0066] S2. Improve the A* algorithm based on intercity network characteristics:

[0067] S21. In this embodiment, the weight of the road sections between directly accessible hub stations is set to 1, and the Dijkstra algorithm is used to search for the path with the least number of transfers between each hub station and record the number of transfers, such as Figure 5 As shown, in other embodiments, other methods may also be used to obtain the number of transfers.

[0068] S22. Obtain node intercity network characteristic indicators, including node degree d(n) and betweenness centrality t(n). The specific calculation formula is as follows:

[0069]

[0070] Where: M is the set of nodes in the network; n jk is the number of shortest paths between any two nodes j and k except node n; n jk (n) is the number of shortest paths between any two nodes j and k other than node n that pass through node n;

[0071] S23. Improve the heuristic function and give priority to selecting hubs with large degree and betweenness centrality as transfer hubs. The calculation formula of the heuristic function value f(n) is as follows:

[0072] f(n)=g(n)+h(n)*l(d(n) / p)*l(t(n)*q)

[0073] Where n is the neighboring node of the current node m; f(n) is the heuristic function valuation of node n; g(n) is the travel cost from node n to the starting point; h(n) is the minimum number of transfers from node n to the end point, calculated by S21; l(x) is a monotonically decreasing function, specifically an exponential function with a base less than 1, and the value range in (0,∞) is (0,1), meeting the basic requirement of monotonically decreasing; d(n) is the degree of node n; p and q are model parameters; t(n) represents the betweenness centrality of node n.

[0074] Calibrate the p and q parameters. This embodiment uses the Latin hypercube sampling method to generate 25 pairs of p and q parameter pairs. Two hub stations are selected in each province to form a small case. The path with the least number of transfers between each hub station under each pair of parameter pairs is searched. The travel time of each path is calculated. The p and q pairs with the least average travel time and number of transfers are selected to search the global path, such as Figure 6 shown.

[0075] S3. Search travel paths based on a hierarchical structure of the integrated transportation network: For the integrated multimodal network of step S1, the urban road network is the lower-level network. The Dijkstra algorithm is used to search for the shortest path between the starting and ending points obtained in step S1 and the selectable hub station. The selectable hub station is any hub station in the city where the starting and ending points are located; for the integrated multimodal network of step S1, the intercity network is the higher-level network. The improved A* algorithm of step S2 is used to search for the path with the least number of transfers; the shortest paths of each travel plan in all levels are spliced ​​together, that is, the shortest path of the three stages of "intra-city + intercity + intra-city", to obtain the global optimal path.

[0076] It also includes searching for the path with the least number of transfers between intercity hub stations according to the rules. The rules are as follows: during intercity travel, the total number of transfers does not exceed 3 times. In other embodiments, the first threshold of other times can also be preset, or set by the user; if there is no corresponding hub station in the current city, go to the hub station in the nearby city; in the city travel stage, the default method is to use a car to complete the transfer process; the interval time between the transfer flight and the arrival flight of the previous travel stage should not be less than the sum of the advance waiting time and the car transfer travel time.

[0077] S4. Calculate the total travel time for city travel and intercity travel: Calculate the city travel time, which is the travel time of the shortest path found in S3; calculate the intercity travel time, such as Figure 7 As shown in , considering travel efficiency and convenience, when searching for the shortest intercity travel route, based on the path with the least number of intercity transfers, the travel time corresponding to different combinations of trains is calculated to determine the shortest travel time; the total travel time is calculated, as shown in Figure 8 The following table shows the time for intra-city travel, inter-city travel, and the time it takes for passengers to arrive at the hub station in advance when traveling inter-city. The specific calculation is as follows:

[0078]

[0079] Where T ij is the total travel time from region i to region j; The running time of selecting transport route k from node p to node q; It means that if both node p and node q are on transport route k, it is 1, otherwise it is 0; is the total transfer time from transport route k to transport route l at node p; If node p can realize the transfer between line k and line l, it is 1, otherwise it is 0; T α The time that passengers usually arrive at the hub in advance when traveling between cities is preset as 30 minutes for high-speed rail and 60 minutes for aviation in this embodiment. In other embodiments, other first and second times can be preset or set by the user. N is the number of nodes in the network; n pq is the number of transport routes between node p and node q, n p is the number of transport routes at node p.

[0080] S5. Calculate accessibility and diagnose accessibility depressions: (1) Obtain socioeconomic attribute data for each region, such as Figure 9 As shown in , including GDP data and permanent population data; (2) based on the total travel time calculated in step S4 and the socioeconomic attribute data, calculate accessibility, as Figure 10 The specific calculation formula is as follows:

[0081]

[0082] Where: A i represents the shortest travel time accessibility value of county i. The smaller the calculated value, the higher the accessibility level of the node. W is the number of destination counties. T ij M is the total travel time from county i to county j, calculated based on step S4; j As the scale index of the target city district / county j, the city GDP (G j ) and the number of permanent residents (P j ) for comprehensive characterization.

[0083] Based on the balance between accessibility and local demand, the accessibility of each city in the urban agglomeration should be positively correlated with the number of permanent residents. Through the regression fitting of the number of permanent residents and accessibility, the combined distribution is obtained to identify areas in the current urban agglomeration where accessibility and travel demand are relatively unbalanced, that is, accessibility depressions, such as Figure 11 、 12 As shown in Figure 2, the larger the weighted average travel time accessibility value, the worse the accessibility. Therefore, the area below the fitted line indicates that accessibility can meet residents' travel needs, while the area above the line indicates the opposite and is considered an accessibility depression.

[0084] Example 2:

[0085] This embodiment is a computing device, including a processor and a memory, wherein the memory stores codes for executing the method in the above embodiment.

[0086] The processor may be a multi-core processor or may include multiple processors. In some embodiments, the processor may include a general-purpose main processor and one or more special coprocessors, such as a graphics processing unit (GPU), a digital signal processor (DSP), etc. In some embodiments, the processor may be implemented using customized circuits, such as application-specific integrated circuits (ASICs) or field-programmable gate arrays (FPGAs).

[0087] The memory may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. ROM may store static data or instructions required by the processor or other modules of the computer. The permanent storage device may be a readable and writable storage device. The permanent storage device may be a non-volatile storage device that does not lose stored instructions and data even if the computer is powered off. In some embodiments, the permanent storage device uses a large-capacity storage device (such as a magnetic or optical disk, flash memory) as the permanent storage device. In other embodiments, the permanent storage device may be a removable storage device (such as a floppy disk, optical drive). The system memory may be a readable and writable storage device or a volatile readable and writable storage device, such as dynamic random access memory. The system memory may store some or all instructions and data required by the processor during operation. In addition, the memory may include any combination of computer-readable storage media, including various types of semiconductor memory chips (DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and magnetic disks and / or optical disks may also be used. In some embodiments, the memory may include a readable and / or writable removable storage device, such as a compact disc (CD), a read-only digital versatile disc (e.g., DVD-ROM, dual-layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (e.g., SD card, mini SD card, Micro-SD card, etc.), a magnetic floppy disk, etc. Computer-readable storage media do not include carrier waves and transient electronic signals transmitted wirelessly or wired.

[0088] The memory stores executable codes thereon, and when the executable codes are executed by the processor, the processor is enabled to perform the above method.

[0089] Example 3:

[0090] This embodiment provides a non-transitory machine-readable memory having executable code stored thereon. When the executable code is executed by a processor of an electronic device, the processor is caused to execute the method in the above embodiment.

[0091] A non-transitory machine-readable memory (or computer-readable memory, or machine-readable memory) having executable code (or computer program, or computer instruction code) stored thereon, which, when executed by a processor of an electronic device (or computing device, server, etc.), enables the processor to perform the various steps of the above-mentioned method according to the present invention.

[0092] Those skilled in the art will further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or combinations of both.

[0093] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for calculating accessibility of a comprehensive transportation multimodal network, characterized in that: The steps include: S1. Build a comprehensive transportation network structure, including road network, railway network and aviation network; record the latitude and longitude coordinates of the starting and ending points, with the starting and ending points being the centers of each district and county; S2. Improve the heuristic function of the A* algorithm; including: S21, searching for a route with the least number of transfers between hub stations in the railway network and the aviation network and recording the number of transfers; S22. Calculate the intercity network characteristic indicators of the nodes, including node degree d(n) and betweenness centrality t(n). The calculation formula of betweenness centrality t(n) is as follows: Where M is the set of nodes in the network; n jk is the number of shortest paths between any two nodes j and k except node n; n jk (n) is the number of shortest paths between any two nodes j and k other than node n that pass through node n; S23. Improve the heuristic function. The calculation formula of the heuristic function value f(n) is as follows: f(n)=g(n)+h(n)*l(d(n) / p)*l(t(n)*q) Where n is the neighboring node of the current node m; f(n) is the heuristic function estimate of node n; g(n) is the travel cost from node n to the starting point; h(n) is the minimum number of transfers from node n to the destination; l() is a monotonically decreasing function; d(n) is the degree of node n; p and q are model parameters; t(n) is the betweenness centrality of node n; The calibration process of parameters p and q is as follows: generate several p and q parameter pairs according to the Latin hypercube sampling method, select hub stations in the study area as small cases, search for the path with the least number of transfers between hub stations under each parameter pair, calculate the travel time of each path, and select the p and q pair with the least average travel time and number of transfers to search for the global path; S3. Hierarchical search for travel paths in the integrated transportation network, including intra-city travel paths within the starting and ending cities, and inter-city travel paths between the starting and ending cities; S4. Calculate the total travel time; S5. Calculate accessibility and diagnose accessibility depressions.

2. The method for calculating accessibility of a comprehensive transportation multimodal network according to claim 1, characterized in that: S1 includes the following steps: S11. Use road network data, railway and airline flight information; road network data includes each node in the road network, the length of roads between nodes, and road grades; railway and airline flight information includes the starting hub, the ending hub, departure time, and arrival time; S12. Record the starting point, end point, and longitude and latitude coordinates of each hub station; S13, constructing a road network topology structure with road nodes as nodes and road section travel time as weight; S14. Based on the Space-P method, a comprehensive transportation multimodal network G = {V, E, C, F, S} is constructed, where V and E are the set of travel nodes in the transportation network and the set of transport arcs between nodes, respectively; C is the set of transfer arcs; F is the set of starting points and ending points; and S is the set of transportation modes; the transfer arc set C includes transfers at the same station and transfers at different stations; the transportation mode set S is {H, R, A}, representing road, rail, and aviation, respectively.

3. The method for calculating accessibility of a comprehensive transportation multimodal network according to claim 2, characterized in that: The travel time of a road section is the ratio of the road section length to the average speed. The road section types include expressways, national roads, provincial roads and expressways; the speeds are preset as the first speed, second speed, third speed and fourth speed respectively.

4. The method for calculating accessibility of a comprehensive transportation multimodal network according to claim 1, characterized in that: S3 also includes searching for the path with the least number of intercity transfers between the starting and ending cities according to the rules, including: The total number of transfers does not exceed the first threshold; If there is no corresponding railway hub or aviation hub in the current city, a hub in a nearby city will be selected; For routes within the city where the starting point is located and the route within the city where the end point is located, the default method is to use a car to complete the connection; The interval between the transfer service and the arrival service of the previous travel stage shall not be less than the sum of the advance waiting time and the car transfer travel time.

5. The method for calculating accessibility of a comprehensive transportation multimodal network according to claim 1, characterized in that: S3 also includes: using the urban road network as the low-level network, and using the Dijkstra algorithm to search for the shortest paths between the starting point and the end point and the optional hub station. The hub station can be selected as any hub station in the city where the starting point and the end point are located; using the intercity network as the high-level network, and using the improved A* algorithm to search for the path with the least number of transfers; splicing the shortest paths of each travel plan in all levels to obtain the global optimal path.

6. The method for calculating accessibility of a comprehensive transportation multimodal network according to claim 1, characterized in that: S4 includes the following steps: S41, respectively calculating the travel time of the inner-city route of the starting city and the travel time of the inner-city route of the destination city; S42. Calculate the intercity travel time between the starting and ending cities. Based on the route with the least number of transfers, calculate the travel time corresponding to different bus combinations to determine the shortest travel time. S43. Calculate the total travel time using the following formula: Where T ij is the total travel time from region i to region j; The running time of selecting transport route k from node p to node q; It means that if both node p and node q are on transport route k, it is 1, otherwise it is 0; is the total transfer time from transport route k to transport route l at node p; If node p can realize the transfer between line k and line l, it is 1, otherwise it is 0; T α To prepare passengers for arriving at hubs earlier than they are accustomed to when travelling between cities; N is the number of nodes in the network; n pq is the number of transport routes between node p and node q, n p is the number of transport routes at node p.

7. The method for calculating accessibility of a comprehensive transportation multimodal network according to claim 6, characterized in that: T α Including the first time preset by railways and the second time preset by aviation.

8. The method for calculating accessibility of a comprehensive transportation multimodal network according to claim 1, characterized in that: S5 includes the following steps: S51. Use socioeconomic attribute data of each city, including GDP data and permanent population data; S52. Calculate the reachability using the following formula: Where A i represents the shortest travel time accessibility value of county i; W is the number of destination counties; T ij is the total travel time from county i to county j; M j is the scale index of county j, G j is the annual GDP of the city in district / county j, P j is the permanent population of district or county j; S53. Through regression fitting of the city's permanent population and accessibility, a combined distribution is obtained, and the area above the fitting line is judged as an accessibility depression.

9. A computing device, characterized in that: The method comprises a processor and a memory, wherein the memory stores executable codes, and when the executable codes are executed by the processor, the processor executes the method according to any one of claims 1 to 8.

10. A non-transitory machine-readable storage medium, characterized in that Executable codes are stored thereon, and when the executable codes are executed by a processor of an electronic device, the processor is caused to execute the method according to any one of claims 1 to 8.

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