Comprehensive traffic multi-mode network accessibility calculation method, calculation device and storage medium

Abstract

The invention relates to a multi-mode network accessibility calculation method for comprehensive traffic, a calculation device and a storage medium, and the method comprises the steps: constructing a network structure of a comprehensive traffic network which comprises a road network, a railway network and an aviation network; recording longitude and latitude coordinates of a starting point and an ending point; a heuristic function of an A * algorithm is improved; the hierarchical search algorithm comprehensively considers travel paths in the traffic network, including intra-city travel paths of cities where a starting point and a terminal point are located, and inter-city travel paths connecting the cities where the starting point and the terminal point are located; calculating total travel time; and calculating reachability and diagnosing reachability depression. According to the method, the path search algorithm is improved with the aim of improving the efficiency, and the path search algorithm suitable for the multi-mode traffic network is constructed from the two aspects of optimizing the search path and improving the search strategy. And by introducing a hierarchical search strategy, unnecessary repeated search is effectively reduced, and the search efficiency of the algorithm is improved. Meanwhile, the algorithm fusion considers the influence of the transfer times and the train connection relationship on the travel path selection, and the actual travel behavior of the traveler is simulated more accurately.

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-mode network accessibility calculation. Background Art

[0002] In recent years, my country's transportation industry has developed rapidly, the infrastructure network has basically been formed, and the comprehensive transportation system has been continuously improved. However, in this process, there are still realistic problems of unbalanced and insufficient development, and the layout of the comprehensive transportation network is difficult to effectively meet the actual transportation needs.

[0003] It is necessary to build a rapid transportation network with expressways, high-speed railways, civil aviation, etc. as the main body, enhance interconnection and strengthen the organic connection between various modes of transportation to improve travel efficiency. To achieve this goal, it is necessary to implement quantitative accessibility evaluation and depression diagnosis for the comprehensive transportation network.

[0004] Traffic accessibility not only reflects the spatial interaction between cities, but also reveals the difficulty of one region to reach other regions. It is an important indicator to measure the level of regional traffic development and service quality. At present, there is an imbalance and insufficiency between comprehensive transportation construction and local development level. By evaluating the matching degree between accessibility and regional needs, we can diagnose the depressions in comprehensive transportation development and services, and promote regional coordinated development by improving these depressions. 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 multi-mode network, comprising the following steps:

[0006] S1. Construct 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. Improved heuristic function of 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 node intercity network characteristic indicators, 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 adjacent 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; 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.

[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 2 hub stations in each province, search for the path with the least number of transfers between hub stations under each pair of parameter pairs, calculate the travel time of each path, and select the p and q pairs with the least average travel time and number of transfers to search the global path.

[0016] S3. Hierarchical search for travel paths in the comprehensive transportation network, including intra-city travel paths within the city where the starting point is located and the city where the end point is located, and inter-city travel paths between the cities where the starting point is located and the end point is located.

[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 airline schedule information; highway network data includes the nodes of the road network, the length of roads between nodes and the road grade; railway and airline schedule information includes the starting hub station, the terminal hub station, the departure time and the arrival time.

[0021] S12. Record the starting point, the 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 transportation arcs between nodes, C is the set of transfer arcs, F is the set of starting points and end 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 highway, railway and aviation, respectively.

[0024] Furthermore, the section travel time is the ratio of the section length to the average speed, and the 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 path 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] The default method for connecting the starting point and the end point within the city is to use a car.

[0029] The interval time between the transfer service and the arriving 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 selectable 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] Further, S4 includes the following steps:

[0032] S41, respectively calculating the travel time of the inner-city route in the city where the starting point is located and the travel time of the inner-city route in the city where the end point is located.

[0033] S42, calculating the intercity route travel time between the starting and ending cities, and based on the route with the least number of transfers, calculating the travel time corresponding to different combinations of trains 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 the 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] Further, S5 includes the following steps:

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

[0040] S52. Calculate the reachability. The formula is as follows:

[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 district 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 straight line is judged as an accessibility depression.

[0044] The present invention also provides a computing device, including 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 as described above.

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

[0046] The present invention comprehensively considers the multi-modal travel mode and the single-mode travel mode, which is more scientific and comprehensive. The paths of different multi-modal travel and single-mode travel are searched, the travel time corresponding to the paths of different modes is calculated, and the travel path of the optimal travel mode is obtained.

[0047] The method of the present invention improves the path search algorithm, which is more efficient and reasonable. The path search algorithm of the multi-modal transportation network is constructed from two aspects: optimizing the search path and improving the search strategy. By introducing a hierarchical search strategy, unnecessary repeated searches are reduced and the search efficiency of the algorithm is improved. Considering the structural characteristics of the inter-city transportation network, the influence of the number of transfers and the train connection relationship on the travel path selection is integrated, and the travel behavior of travelers is better simulated.

[0048] The present invention takes into account the total travel time of the transfer behavior of the entire travel chain, which is consistent with the actual travel time. The entire travel chain is "travel within the departure city + intercity travel + travel within the destination city", and the total travel time includes the sum of the traveler's usual waiting time in advance, 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 drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 creative work.

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

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

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

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

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

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

[0056] Figure 7 is a partial inter-city travel timetable in an embodiment of the present invention;

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

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

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

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

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

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] Embodiment 1:

[0064] like Figure 1 As shown, the method for calculating accessibility of a comprehensive transportation multi-modal network in this embodiment includes the following steps: S1, constructing a comprehensive transportation network structure; S2, improving the A* algorithm by improving the heuristic function based on the characteristics of the intercity network; S3, searching for travel paths based on the comprehensive transportation network layer; S4, calculating the total travel time for intra-city travel and inter-city travel; S5, calculating accessibility and diagnosing accessibility depressions. This embodiment takes urban agglomeration A and urban agglomeration B as examples for explanation, 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, such as Figure 3 As shown; get the starting and ending points (starting and ending points) and the longitude and latitude coordinates of the hub station, such as Figure 4 As shown in the figure, the road network topology is constructed, with road nodes as nodes and section travel time as weight. Section travel time is the ratio of section length to 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, S is the set of transportation modes, and the transfer arc set C includes two types of arcs: same-station transfer and different-station transfer; 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}, which represents 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 route 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), which meets 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 p and q parameters. In this embodiment, the Latin hypercube sampling method is used 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 hub stations under each pair of parameter pairs is searched, and 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. Hierarchical search of travel paths based on the comprehensive transportation network: For the comprehensive multimodal network of step S1, the urban road network is the low-level network, and 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 comprehensive multimodal network of step S1, the intercity network is the high-level network, and the improved A* algorithm of step S2 is used to search for the path with the least transfers; The shortest paths of each travel plan in all levels, that is, the three-stage shortest path of "intra-city + inter-city + intra-city", are spliced ​​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, and 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 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 of the city and inter-city travel: calculate the travel time within the city, which is the travel time of the shortest path searched by S3; calculate the inter-city travel time, such as Figure 7 As shown in , considering travel efficiency and convenience, when searching for the shortest intercity travel route, the travel time corresponding to different combinations of trains is calculated based on the path with the least number of intercity transfers 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 that passengers usually 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 the 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 are used to arrive at the hub in advance when traveling between cities. In this embodiment, the high-speed rail is preset to 30 minutes, and the aviation is preset to 60 minutes. In other embodiments, other first time and second time can also 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 Fig. 9 As shown, including GDP data and permanent population data; (2) based on the total travel time calculated in step S4 and the socio-economic attribute data, calculate accessibility, such as Fig.10 As shown, 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 is the total travel time from county i to county j, calculated based on step S4; M j As the scale index of the target city district j, the city GDP (G j ) and the number of permanent residents (P j ) for comprehensive characterization.

[0083] Based on whether accessibility and local demand are balanced, 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 in the city and accessibility, the combined distribution is obtained to determine the areas where accessibility and travel demand are relatively unbalanced in the current urban agglomeration, that is, the accessibility depression, such as Fig.11 , 12 As shown in the figure. The larger the weighted average travel time accessibility value, the worse the accessibility. Therefore, the area below the fitted straight line indicates that the accessibility can meet the travel needs of residents, while the area above the straight line is the opposite and is judged as an accessibility depression.

[0084] Embodiment 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 a customized circuit, such as an application-specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

[0087] The memory may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage. Among them, ROM can 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 the stored instructions and data even after 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 a permanent storage device. In some 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 a dynamic random access memory. The system memory may store some or all instructions and data required by the processor at run time. 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 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 laser disc (CD), a read-only digital versatile disc (e.g., DVD-ROM, double-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. The computer-readable storage medium does 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 execute the above method.

[0089] Embodiment 3:

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

[0091] A non-temporary 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 a combination of both.

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

Claims

1. A method for calculating accessibility of a comprehensive transportation multi-modal 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. Improved heuristic function of A* algorithm; including: S21, searching for a route with the least number of transfers between hubs in the railway network and the aviation network and recording the number of transfers; S22. Calculate the node intercity network characteristic indicators, 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 as small cases in the study area, search for the path with the least number of transfers between hub stations under each pair of parameter pairs, calculate the travel time of each path, and select the p and q pairs with the least average travel time and number of transfers to search the global path; S3, hierarchical search for travel paths in the integrated transportation network, including intra-city travel paths within the city where the starting point is located and the city where the end point is located, and inter-city travel paths between the cities where the starting point is located and the end point is located; S4. Calculate the total travel time; S5. Calculate accessibility and diagnose accessibility depressions.

2. The method for calculating accessibility of a comprehensive transportation multi-modal network according to claim 1, characterized in that: S1 includes the following steps: S11. Use highway network data, railway and air schedule information; highway network data includes nodes of the road network, length of roads between nodes and road grade; railway and air schedule information includes starting hub station, terminal hub station, departure time and arrival time; S12, record the starting point, the end point and the 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 transportation arcs between nodes, C is the set of transfer arcs, F is the set of starting points and end 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 highway, railway and aviation, respectively.

3. The method for calculating accessibility of a comprehensive transportation multi-modal network according to claim 2, characterized in that: The travel time of a section is the ratio of the section length to the average speed. The 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.

4. The method for calculating accessibility of a comprehensive transportation multi-modal 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, choose a hub in a nearby city; For routes within the city where the starting point is located and routes within the city where the end point is located, cars are used by default to complete the connection; The interval time between the transfer service and the arriving 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 multi-modal network according to claim 1, characterized in that: 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 selectable 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.

6. The method for calculating accessibility of a comprehensive transportation multi-modal network according to claim 1, characterized in that: S4 includes the following steps: S41, respectively calculating the travel time of the inner-city route in the city where the starting point is located and the travel time of the inner-city route in the city where the end point is located; S42, calculating the intercity route travel time between the starting and ending cities, and based on the route with the least number of transfers, calculating the travel time corresponding to different combinations of trains 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 the 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 to arrive at the hub earlier 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 multi-modal 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 multi-modal network according to claim 1, characterized in that: S5 includes the following steps: S51. Use socio-economic attribute data of each city, including GDP data and permanent population data; S52. Calculate the reachability. The formula is as follows: 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 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 straight 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: An executable code is stored thereon, and when the executable code is executed by a processor of an electronic device, the processor is caused to execute any one of the methods described in claims 1-8.

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