Urban traffic circle service level analysis and evaluation method and system

Abstract

The invention provides an urban traffic circle service level analysis and evaluation method and system, and relates to the technical field of intelligent traffic and regional planning, and the method comprises the steps: carrying out the calculation of the shortest highway travel time of each county administrative center in a target region in a peak period and a flat peak period, so as to obtain a highway traffic shortest travel OD matrix, and based on the railway traffic time table data, calculating and generating the shortest railway travel time between every two county administrative centers in the target area to obtain a railway traffic shortest travel OD matrix. Based on the road and railway shortest travel matrix, a traffic travel isochronal circle with the administrative center of each county as the center is formed through spatial interpolation, the traffic isochronal circle area and the population coverage rate are calculated in combination with the geographic space area and permanent population data of each county, and quantitative evaluation analysis of the traffic circle service level is carried out according to the coverage rate. According to the invention, a reliable technical platform is provided for continuously carrying out traffic network dynamic evaluation and planning simulation prediction.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation and regional planning technology, and in particular to a method and system for analyzing and evaluating the service level of urban transportation circles. Background Technology

[0002] Currently, research and practice on the service level of transportation networks mostly focus on macro-level qualitative descriptions or rely on static theoretical analysis of a single mode of transportation. There is a lack of an analysis and evaluation system that integrates dynamic data of multi-modal transportation networks, has unified quantitative standards, and can be refined down to the county level.

[0003] Existing technologies often struggle to comprehensively consider the time-varying characteristics of highway congestion and the impact of scheduled railway services, and they also fail to effectively correlate spatiotemporal accessibility results with socioeconomic indicators such as population and area. This leads to discrepancies between analytical results and actual travel experiences, making it difficult to accurately identify shortcomings and provide quantitative evidence for infrastructure improvement. Therefore, there is an urgent need to develop a scientific, systematic, and operable analytical method and technological system to support refined and intelligent transportation planning decisions. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a method and system for analyzing and evaluating the service level of urban transportation circles, providing a scientific decision support tool for regional transportation planning and facility optimization.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] Firstly, a method for analyzing and evaluating the service level of urban transportation networks, the method comprising:

[0007] Step 1: Obtain basic geographic information, highway network data, and railway timetable data for each county within the target area;

[0008] Step 2: Based on highway traffic network data, calculate and generate the shortest road travel time for each county administrative center in the target area during peak and off-peak hours to obtain the shortest road traffic OD matrix;

[0009] Step 3: Based on railway timetable data, calculate and generate the shortest railway travel time between each pair of county administrative centers within the target area to obtain the shortest travel OD matrix for railway traffic.

[0010] Step 4: Based on the shortest travel OD matrix of highway traffic and the shortest travel OD matrix of railway traffic, the minimum value fusion processing is performed on the corresponding travel time between each pair of counties to generate the comprehensive shortest travel time between each county, so as to establish a comprehensive transportation network dataset.

[0011] Step 5: Based on the comprehensive shortest travel time in the comprehensive transportation network dataset, generate isochronous travel circles centered on the administrative centers of each county using a spatial interpolation algorithm;

[0012] Step 6: Based on the 1-hour isotime circle boundary in the transportation travel isotime circle, and combined with the area and resident population data of each county, calculate the area coverage rate and population coverage rate of the 1-hour transportation circle centered on each county, and conduct quantitative evaluation and comparative analysis of the service level of the transportation circle based on the coverage rate.

[0013] Furthermore, acquire basic geographic information, highway network data, and railway timetable data for each county within the target area, including:

[0014] Step 1.1: Acquire and integrate data from multiple data sources to form the basic geographic information dataset, the highway traffic network dataset, and the railway traffic timetable dataset;

[0015] Step 1.2: The basic geographic information dataset shall include at least the geographic coordinates of the administrative centers and the administrative division boundaries of each county within the target area; the highway traffic network dataset shall include at least the traffic speed information and topological connection relationship of roads of different levels at different time periods; and the railway traffic timetable dataset shall include at least the train numbers and running times between railway stations covering each county within the target area.

[0016] Furthermore, based on highway traffic network data, the shortest road travel times for each county administrative center within the target area during peak and off-peak hours are calculated and generated to obtain the shortest road traffic origin-destination (OD) matrix, including:

[0017] Step 2.1: Transform the road network topology model with time-time weights by using the topological connectivity relationships in the highway traffic network dataset and the road traffic speed information at different times;

[0018] Step 2.2: Based on the road network topology model, using the geographical coordinates of each county administrative center as the starting and ending points of travel, apply the shortest path algorithm to calculate the shortest road travel time between any two county administrative centers within the target area during peak and off-peak hours. The shortest road travel time is the total travel time for point-to-point travel, and its calculation process additionally includes the travel time by urban and rural roads from the starting county administrative center to the highway entrance, and from the highway exit to the ending county administrative center.

[0019] Step 2.3 involves storing the calculation results in a structured format to generate a shortest-trip OD matrix for highway traffic that includes both peak-hour and off-peak-hour data.

[0020] Furthermore, based on railway timetable data, the shortest train travel times between each county administrative center within the target area are calculated, resulting in a shortest-travel OD matrix for railway transportation, including:

[0021] Step 3.1: Using the railway traffic timetable dataset and based on the coverage relationship between the geographical coordinates of the administrative center and the railway station, map each county to the corresponding railway station node; construct a railway timetable network graph with stations as nodes using train number and running time information, and calculate the shortest railway travel time between any two mapped station nodes.

[0022] Step 3.2: Combine the shortest railway travel time with the above calculations, and additionally calculate the road connection time from the starting county administrative center to the corresponding departure railway station, and from the arrival railway station to the destination county administrative center, to generate the pairwise shortest railway travel time between any two county administrative centers in the target area; store the calculation results in a structured way as a railway traffic shortest travel OD matrix.

[0023] Furthermore, based on the shortest trip OD matrix for highway traffic and the shortest trip OD matrix for rail traffic, the minimum value fusion processing is performed on the corresponding travel times between every two counties to generate the comprehensive shortest travel times between each county, thus establishing a comprehensive transportation network dataset, including:

[0024] Step 4.1: Based on the shortest trip OD matrix for highway traffic generated in Step 2 and the shortest trip OD matrix for railway traffic generated in Step 3; for each pair of counties in the target area, extract the corresponding highway travel time and railway travel time from the two OD matrices.

[0025] Step 4.2: Compare the extracted road travel time and rail travel time one by one, and take the smaller value as the shortest reachable time between the two places in the county based on the multimodal transportation network.

[0026] Step 4.3: Summarize the shortest reach times for all county pairs, construct and output a comprehensive transportation network dataset to characterize the overall connectivity efficiency of the region.

[0027] Furthermore, based on the comprehensive shortest travel time in the integrated transportation network dataset, isochronous travel circles centered on the administrative centers of each county are generated using a spatial interpolation algorithm, including:

[0028] Step 5.1: By combining the shortest travel time between each county in the comprehensive transportation network dataset, the data is associated with the corresponding geographical coordinates of the administrative center to form a discrete point-like spatiotemporal dataset.

[0029] Step 5.2: Based on the comprehensive shortest travel time in the point spatiotemporal dataset as the interpolation basis, perform diffusion calculations in continuous geographic space using a spatial interpolation algorithm;

[0030] Step 5.3: Based on the diffusion calculation results, draw closed contour lines on the electronic map of the target area, with each county administrative center as the starting point, representing different travel time thresholds. These closed contour lines constitute the isochronous travel circles.

[0031] Furthermore, based on the boundaries of the 1-hour isotime circle within the transportation isotime circle, and combined with the area and resident population data of each county, the area coverage rate and population coverage rate of the 1-hour transportation circle centered on each county are calculated respectively. Based on the coverage rate, a quantitative evaluation and comparative analysis of the transportation circle service level are conducted, including:

[0032] Step 6.1: Extract the spatial boundary corresponding to the 1-hour travel time threshold centered on each county administrative center from the isochronous travel circle generated in Step 5.

[0033] Step 6.2: Based on the spatial boundary of the 1-hour isotime circle, perform spatial overlay analysis and statistics with the administrative division boundary and resident population distribution data in the basic geographic information dataset; calculate the geographic area covered by the 1-hour isotime circle of each county and the total number of resident population within it, and calculate the area coverage rate and population coverage rate of the 1-hour transportation circle corresponding to the county.

[0034] Step 6.3: Based on area coverage and population coverage, conduct horizontal comparisons and classify all counties to quantitatively assess and demonstrate the spatial differentiation characteristics of the service levels of different county transportation networks within the target area, providing a basis for transportation planning decisions.

[0035] Secondly, the urban transportation circle service level analysis and evaluation system includes:

[0036] The acquisition module is used to acquire basic geographic information, highway network data, and railway timetable data for each county within the target area.

[0037] The calculation module is used to calculate and generate the pairwise shortest road travel time of each county administrative center in the target area during peak and off-peak hours based on highway traffic network data, so as to obtain the shortest travel OD matrix of highway traffic.

[0038] The modeling module is used to calculate and generate the shortest railway travel time between each county administrative center in the target area based on railway traffic timetable data, and obtain the shortest railway travel OD matrix.

[0039] The fusion module is used to perform minimum value fusion processing on the corresponding travel times between every two counties based on the shortest travel OD matrix of highway traffic and the shortest travel OD matrix of railway traffic, so as to generate the comprehensive shortest travel time between each county and establish a comprehensive transportation network dataset.

[0040] The algorithm module is used to generate isochronous travel circles centered on the administrative centers of each county based on the comprehensive shortest travel time in the comprehensive transportation network dataset using a spatial interpolation algorithm.

[0041] The processing module is used to calculate the area coverage rate and population coverage rate of the 1-hour transportation circle centered on each county, based on the boundary of the 1-hour isotime circle in the transportation travel isotime circle, combined with the area and resident population data of each county, and to conduct quantitative evaluation and comparative analysis of the service level of the transportation circle based on the coverage rate.

[0042] Thirdly, a computing device includes:

[0043] One or more processors;

[0044] A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0045] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0046] The above-described solution of the present invention has at least the following beneficial effects:

[0047] This invention achieves deep fusion and standardized processing of multi-source heterogeneous data. By systematically integrating basic geographic information, time-segmented dynamic highway network data, and railway timetable data, and constructing a unified spatiotemporal computing framework, it solves the problems of single and static data in traditional evaluations, providing a real and comprehensive data foundation for analysis. It innovatively introduces an analytical model that integrates time-varying weights and multi-mode networks. By establishing a time-segmented weighted highway network model and performing minimum value fusion with the railway timetable network, it accurately simulates the decision-making process of choosing the optimal route and mode of transportation in real-world travel, making the definition of transportation circles more closely aligned with actual travel experience and accessibility. It constructs a complete analytical chain from discrete points to continuous space, and from time calculation to socio-economic evaluation. This method transforms point-to-point travel time data into intuitive isochronous circle maps through spatial interpolation, and further combines it with area and population data to generate core coverage indicators, achieving visualization, quantification, and comparability of transportation service effectiveness. It provides modular and systematic technical implementation paths and decision support tools. This approach not only enhances the automation and intelligence of the analysis process, but also significantly improves the scientific rigor and accuracy of planning. It possesses excellent scalability and application prospects. The design of this method and system is not limited to specific regions and is compatible with data from future emerging transportation modes, providing a reliable technical platform for continuous dynamic assessment and planning simulation prediction of transportation networks. Attached Figure Description

[0048] Figure 1 This is a flowchart illustrating the urban transportation circle service level analysis and evaluation method provided in an embodiment of the present invention.

[0049] Figure 2 This is a schematic diagram of an urban transportation circle service level analysis and evaluation system provided in an embodiment of the present invention. Detailed Implementation

[0050] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0051] like Figure 1 As shown, embodiments of the present invention propose a method for analyzing and evaluating the service level of urban transportation circles, the method comprising the following steps:

[0052] Step 1: Obtain basic geographic information, highway network data, and railway timetable data for each county within the target area;

[0053] Step 2: Based on highway traffic network data, calculate and generate the shortest road travel time for each county administrative center in the target area during peak and off-peak hours to obtain the shortest road traffic OD matrix;

[0054] Step 3: Based on railway timetable data, calculate and generate the shortest railway travel time between each pair of county administrative centers within the target area to obtain the shortest travel OD matrix for railway traffic.

[0055] Step 4: Based on the shortest travel OD matrix of highway traffic and the shortest travel OD matrix of railway traffic, the minimum value fusion processing is performed on the corresponding travel time between each pair of counties to generate the comprehensive shortest travel time between each county, so as to establish a comprehensive transportation network dataset.

[0056] Step 5: Based on the comprehensive shortest travel time in the comprehensive transportation network dataset, generate isochronous travel circles centered on the administrative centers of each county using a spatial interpolation algorithm;

[0057] Step 6: Based on the 1-hour isotime circle boundary in the transportation travel isotime circle, and combined with the area and resident population data of each county, calculate the area coverage rate and population coverage rate of the 1-hour transportation circle centered on each county, and conduct quantitative evaluation and comparative analysis of the service level of the transportation circle based on the coverage rate.

[0058] In this embodiment of the invention, because the invention employs multi-source data fusion, time-segmented road network modeling, and minimum value fusion techniques, it overcomes the technical problems of existing technologies, such as single data sources, neglect of dynamic traffic congestion, and failure to effectively integrate multi-modal travel options. This achieves the following technical effects: by generating a comprehensive transportation network dataset, regional accessibility can be realistically simulated; by constructing intuitive traffic isochronous circles through spatial interpolation, the spatial pattern of service levels is visualized; and finally, by calculating area and population coverage, a quantifiable and comparable evaluation index system is formed, thus providing accurate and scientific decision-making basis for transportation planning and facility optimization.

[0059] In a preferred embodiment of the present invention, step 1 above may include:

[0060] Step 1.1 involves acquiring and integrating data from multiple data sources to form the basic geographic information dataset, the highway traffic network dataset, and the railway timetable dataset. Specifically, this includes accessing and calling the service interfaces and databases of multiple authoritative data sources via a cloud processor. These data sources include, but are not limited to, the statutory basic geographic information database of the natural resources authorities, the highway network basic database of the transportation departments, real-time and historical traffic data platforms provided by internet map service providers, and train timetable data published by the official railway customer service platform. The system automatically or semi-automatically collects raw data from the above sources through pre-configured data interface protocols.

[0061] Step 1.2: The basic geographic information dataset should at least include the geographic coordinates of the administrative centers and administrative boundary information of each county within the target area; the highway traffic network dataset should at least include the traffic speed information and topological connections of roads of different levels at different time periods; the railway timetable dataset should at least include train numbers and travel times between railway stations covering each county within the target area. Specifically, this includes cleaning, transforming, and standardizing the collected raw data. For the basic geographic information, the latitude and longitude coordinates of the administrative centers of each county and the coordinate sequences of the polygonal boundaries of each county's administrative division are extracted and verified to ensure they conform to a unified spatial reference coordinate system. For the highway traffic network data, the topological structure of the road network, i.e., the connection relationships between nodes and road segments, is extracted according to road level classification, and the average traffic speed data corresponding to each road segment is associated with different time periods, especially typical peak and off-peak periods. For the railway timetable data, train number information for all railway stations involving counties and cities within the target area is extracted, including the origin and destination stations, the sequence of stops, and the travel time between stations for each train. Finally, the three types of data, after being cleaned and standardized, are structured into datasets that can be used in subsequent steps. The basic geographic information dataset is organized in the form of a spatial data table, with each record corresponding to a county and containing its unique identifier, administrative center coordinates, and boundary geometry data. The highway traffic network dataset is organized in the form of a graph data model or relational table, clearly expressing the road network topology and associating road segment traffic speed with time period and road grade attributes. The railway traffic timetable dataset is organized in the form of a timetable network relational table, recording train connections and travel times between stations. All datasets are stored in a cloud database or data lake for subsequent modeling and analysis modules to access as needed.

[0062] In this embodiment of the invention, by employing the technical means of systematically extracting, cleaning, standardizing, and integrating multi-source heterogeneous data, the technical problems of scattered and independent basic geographic, highway network, and railway schedule data, inconsistent formats, inconsistent spatiotemporal benchmarks, and lack of structured correlation in traditional methods are overcome. This leads to the construction of a unified, accurate, and spatiotemporally correlated basic data system, laying a reliable data foundation for subsequent traffic network modeling and service level quantitative analysis.

[0063] In a preferred embodiment of the present invention, step 2 above may include:

[0064] Step 2.1 involves transforming the topological connectivity relationships and road traffic speed information at different times from the highway traffic network dataset into a time-weighted road network topology model. Specifically, the system reads the integrated highway traffic network dataset and constructs a basic road network graph structure based on the road topological connectivity relationships. In this structure, road intersections or specific locations are considered nodes, and road segments connecting nodes are considered edges. The system further transforms the traffic speed information of roads of different levels at different times into corresponding road segment travel cost weights for different time scenarios. Specifically, the road segment length is divided by the average traffic speed during that time period to obtain the travel cost in units of time. Finally, a dual-weighted road network topology model with peak-hour weight sets and off-peak-hour weight sets is formed.

[0065] Step 2.2: Based on the road network topology model, using the geographical coordinates of each county administrative center as the starting and ending points, apply the shortest path algorithm to calculate the shortest road travel time between any two county administrative centers within the target area during peak and off-peak hours. The shortest road travel time is the total travel time for point-to-point travel, and its calculation process additionally includes the travel time by urban and rural roads from the starting county administrative center to the highway entrance, and from the highway exit to the ending county administrative center. Specifically, Step 2.2 calculates the shortest road travel time based on the road network topology model with time-weighted calculations. The core execution step of calculating the shortest road travel time between county-level administrative centers during peak and off-peak hours is based on the dual-weighted road network topology model constructed in step 2.1. The entire process revolves around precise matching of administrative centers and road network nodes, time-segmented algorithm calls, full-process travel time calculation, result verification, and preliminary structuring. Furthermore, the travel time on urban and rural roads connecting to expressways is included in the overall time calculation to ensure that the calculation results accurately reflect real-world road travel scenarios. The specific implementation process is divided into the following five sub-steps, each closely linked and progressively advancing, as detailed below:

[0066] Precise matching and location calibration of the geographical coordinates of the county administrative center to road network model nodes:

[0067] The system first reads the latitude and longitude geographic coordinates of the administrative centers of each county in the basic geographic information dataset, as well as the geographic coordinates of all road nodes in the time-weighted road network topology model constructed in step 2.1, and performs a one-to-one precise matching from the administrative center coordinates to the road network nodes. The specific operation is as follows:

[0068] The spatial nearest neighbor matching rule is adopted. Taking the coordinates of a single county administrative center as the center, a reasonable spatial search radius is set. The road node closest to the coordinates is retrieved in the road network topology model. The search radius is adapted and adjusted in combination with the road network density of the target area to ensure that the matched node is a road node that can be directly connected to the administrative center around the administrative center, and to avoid matching remote road network nodes due to an excessively large search radius.

[0069] The matching results are calibrated for location. If the geographic coordinates of the administrative center are located on a road node in the road network model, the node is directly designated as the starting / ending point of highway travel. If the administrative center is located in a non-road area outside the road network, the nearest matched road node is used as the benchmark to supplement the connecting route information from the administrative center to that node, and the travel time of the connecting route will be included in the subsequent calculation of the total travel time.

[0070] After completing the node matching of all county-level administrative centers, each matching node is uniquely identified and associated. The node number is bound to the corresponding county name, administrative code, geographical coordinates and other information. At the same time, a time period attribute compatibility mark is marked on the matching node to ensure that the node can be adapted to the weight set call of both peak and off-peak periods, and to avoid the problem of node and county information being disconnected in subsequent calculations.

[0071] All matching results are validated to remove invalid matches caused by missing road network data. For invalid county administrative centers, the search radius is expanded and node matching is completed by manual review to ensure that all county administrative centers in the target area can be matched to valid road nodes in the road network model, laying the foundation for subsequent path calculation.

[0072] Based on the number of counties in the target area and the complexity of the road network topology model, the shortest path algorithm is selected and its operating parameters are configured to ensure that the algorithm is suitable for calculating pairwise paths between counties, while also considering computational efficiency and result accuracy. The specific operations are as follows:

[0073] Algorithm Selection and Adaptation: For the point-to-point shortest path calculation requirement between county-level administrative centers, the system provides selection channels for two classic shortest path algorithms: Dijkstra's algorithm and Floyd's algorithm. If the target area has a small number of counties, the Floyd's algorithm is selected to directly perform pairwise path calculations with all sources and sinks. If the target area has a large number of counties, the Dijkstra's algorithm is selected, starting with the matching node of a single county as the single source, and sequentially performing path search on the matching nodes of all other counties. Both algorithms support the accumulation of travel costs and shortest path filtering for weighted edges in the road network model, and the algorithm's operating logic is fully compatible with the weight assignment rules of the road network topology model.

[0074] Core operating parameter configuration:

[0075] The algorithm sets a path search range limit, strictly confining the algorithm's path search scope to the road network topology model of the target area, excluding invalid road network segments outside the area, and avoiding distortion of calculation results due to cross-regional searches. Path validity verification rules are configured so that when traversing paths, the algorithm automatically excludes invalid road segments marked as under construction, closed, or prohibited from passage in the road network model, only traversing normally passable road segments, ensuring that the calculated paths are actually passable highway paths. An algorithm iteration termination condition is set so that when the algorithm finds a matching node in the target county, the traversal and cost accumulation of that path are immediately terminated, and the total cost of the current shortest path is recorded, avoiding invalid algorithm iterations and improving overall computational efficiency. Cost accumulation rules are configured so that the algorithm strictly accumulates costs according to the time-period weights (travel costs in units of time) of road segments in the road network model, retaining information such as the level and number of road segments during the accumulation process, facilitating subsequent path tracing and result verification.

[0076] Using the calibrated and labeled road network nodes as the origin and destination, the system calls the peak-hour weight set and off-peak-hour weight set respectively to perform a full pairwise shortest path traversal and trip cost calculation between all county-level administrative centers in the target area, thereby realizing the calculation of the theoretical shortest highway travel time in different time periods. The specific operation is as follows:

[0077] Peak-hour route calculation: The system first calls the peak-hour weight set in the road network topology model to load the travel cost weights of the peak hours for all road segments in the road network model, completing the initialization of the calculation environment. Then, taking the matching node of each county in the target area as a single source starting point, it sequentially performs path search on the matching nodes of all other counties in the area. Through the selected shortest path algorithm, it traverses all feasible road paths from the starting point to the destination, and accumulates the peak-hour travel costs of all road segments on each feasible path. The path with the smallest accumulated cost value is selected, and this minimum value is the theoretical shortest road path travel cost for that county pair during peak hours. After completing the calculation for all destination nodes from one starting node, it switches to the next starting node and repeats the above operation until the peak-hour route calculation for all county pairs in the target area is completed.

[0078] Off-peak route calculation: After the peak period calculation is completed, the system clears the weight data of the current calculation environment, calls the off-peak period weight set in the road network topology model, loads the off-peak period travel cost weights for all road segments, and re-initializes the calculation environment; then, it adopts the same traversal calculation method as the peak period, with the matching nodes of all counties as the starting and ending points, to carry out the off-peak period shortest path search and cost accumulation for all county pairs, and selects the theoretical shortest highway route travel cost for each county pair under the off-peak period;

[0079] Marking unreachable county pairs: During the time-segmented traversal calculation, if there are no accessible road routes between a pair of county pairs, the system will mark the pair of county pairs as unreachable during a specific time period, that is, marked as unreachable by road during peak hours or unreachable by road during off-peak hours, providing a clear unreachable identifier for the subsequent generation of the OD matrix.

[0080] During the time-segmented calculation process, the shortest path information for each county pair is recorded in real time, including the core road segments corresponding to the path, the travel cost of each road segment, the total cost accumulation process, etc., forming a calculation process log, which is stored in the cloud database for easy subsequent result verification and problem tracing.

[0081] Based on the theoretical shortest highway route cost mentioned above, the system, for routes including highway sections, completes the travel time for the two urban and rural road segments: from the starting administrative center to the highway entrance, and from the highway exit to the ending administrative center, forming a complete point-to-point total highway travel time. Simultaneously, all calculation results are validated for reasonableness, and outlier data is removed. The specific operations are as follows:

[0082] Identification and completion of travel time for connecting road segments: The system identifies the road segment type for the shortest path of each county pair. If the path includes a highway segment, it automatically extracts the corresponding highway entrance and exit nodes from the road network topology model. Simultaneously, it retrieves and extracts the urban and rural road connecting road segments from the starting county administrative center matching node to the highway entrance node, and from the highway exit node to the ending county administrative center matching node, and retrieves the travel costs of these two connecting road segments for the corresponding time period. Then, it adds the travel time of the two connecting road segments to the theoretical shortest travel cost of the main path (including highway segments) to obtain the complete point-to-point shortest road travel time for the county pair in the corresponding time period, i.e., the total travel time. If the path does not include a highway segment, the theoretical shortest path travel cost is directly used as the total road travel time without additional completion.

[0083] Set a threshold range for regional travel time. Based on the geographical scope and road network level distribution of the target area, calculate the reasonable range of highway travel time for similar county pairs (such as adjacent counties or cross-regional counties). Mark the calculation results that exceed the range as abnormal data. Conduct source tracing and verification of abnormal data to investigate whether the abnormality is due to node matching deviation, road network data error, improper algorithm parameter configuration, etc. After making targeted corrections for different causes, recalculate the path and full time of the county pair. For results that are still abnormal after verification, make a final judgment in combination with manual verification to ensure that all calculation results are consistent with the actual time pattern of highway travel in the target area and avoid the distortion of the OD matrix due to data errors.

[0084] After calculating and verifying the shortest road travel time during peak and off-peak hours for all county pairs, the calculation results are standardized, organized, and stored in a structured manner. This provides directly accessible and traceable basic data for the subsequent generation of the shortest road traffic OD matrix. The specific operations are as follows:

[0085] All county pairs are categorized according to the combination of origin and destination counties. Each county pair is then bound to the corresponding peak-hour and off-peak-hour full-journey road travel times. For county pairs marked as inaccessible, a pre-defined inaccessibility flag is used to fill in the blanks, ensuring a consistent result format across all county pairs. Each calculation result is supplemented with related information, including the administrative codes and geographic coordinates of the origin / destination counties, the matched road network node numbers, the core road segment information of the shortest path, and the algorithm type used in the calculation, achieving full correlation between the results and the original data and the calculation process. The organized shortest road travel time data by time period is used to construct a structured basic dataset with county pair-time period-travel time as the core fields. This dataset is stored in a designated directory in the cloud database. This dataset is linked with the road network topology model from step 2.1, the basic geographic information dataset from step 1, and the highway traffic network dataset, supporting rapid retrieval and data traceability in subsequent steps. Simultaneously, modification and update interfaces are set up for the dataset to facilitate synchronous updates of travel time results when adding new road network data or correcting error data, ensuring the timeliness and accuracy of the data.

[0086] Step 2.3 involves structuring and storing the calculation results to generate a shortest-trip OD matrix for highway traffic that includes both peak-hour and off-peak data. Specifically, the system organizes the shortest travel times during peak and off-peak hours between all county pairs calculated in Step 2.2. These results are structured into a two-dimensional relational table, namely, an origin-destination matrix. In this matrix, rows and columns represent each county, and each cell stores the two sets of shortest travel time values ​​between the corresponding two counties during peak and off-peak hours. This complete two-dimensional relational table generates the shortest-trip OD matrix for highway traffic and is persistently stored in a database or file to ensure that subsequent steps can accurately read and retrieve the time-segmented travel time data.

[0087] In this embodiment of the invention, by combining road topology with time-segmented travel speeds to construct a dynamic weighted road network model, and using the shortest path algorithm for batch calculations based on this model, the technical problem of traditional highway travel time assessment using static theoretical speeds, which cannot reflect the time-varying characteristics of real traffic flow, especially the impact of peak congestion, is overcome. This achieves the technical effect of accurately simulating actual highway travel time at different times and outputting a complete and structured travel time matrix, providing a real and reliable benchmark for subsequent multimodal transportation integration and service level analysis.

[0088] In a preferred embodiment of the present invention, step 3 above may include:

[0089] Step 3.1: Using the railway timetable dataset and based on the coverage relationship between the geographical coordinates of administrative centers and railway stations, each county is mapped to its corresponding railway station node. A railway timetable network graph with stations as nodes is constructed using train numbers and travel time information. The shortest railway travel time between any two mapped station nodes is calculated. Specifically, this includes: reading the integrated railway timetable dataset to obtain all relevant train information, their stops, and travel times. Simultaneously, based on the geographical coordinates of the administrative centers of each county in the basic geographic information dataset, the system uses spatial calculations to determine railway passenger stations within a certain distance or travel time threshold around the administrative center of each county. This maps each county to one or more of the most relevant railway stations, serving as representative nodes for railway travel in that county. Subsequently, the system uses the timetable data, treating all stations as nodes, and constructs directed edges between stations with direct trains or connections via transfers. The travel time of the train or the total transfer time is used as the weight of the edge, thus constructing a railway timetable network graph with stations as nodes and travel time as the weight. Based on this network graph, the system applies a shortest path algorithm suitable for timetable networks to calculate the fastest travel time between any two representative station nodes mapped from the county.

[0090] Step 3.2: Combining the aforementioned shortest railway travel time, and additionally calculating the road connection time from the originating county administrative center to the corresponding departure railway station, and from the arrival railway station to the destination county administrative center, the shortest railway travel time between any two county administrative centers within the target area is generated. The calculation results are then structured and stored as a railway traffic shortest travel OD matrix. Specifically, this includes: retrieving the one-to-one mapping table of county administrative centers and railway stations established in Step 3.1. This table contains core information such as the unique county identifier, administrative center coordinates, mapped station name / number, station coordinates, and mapping type (direct station / connecting station). Based on this, a full verification of the mapping relationship is performed to ensure the accuracy of the basic data for reverse association. The specific operations are as follows:

[0091] Traverse all counties within the target area and check whether each county has a corresponding railway station. If there is an unmatched county, mark it as having no railway connection station and record it in the anomaly list. Subsequently, the railway travel time between this county and all other counties will be marked as unreachable. Verify the information of the matched stations to confirm whether the station is in operation and whether it is included in the railway passenger network model constructed in step 3.1. If the station is out of service or not included in the railway network model, rematch the nearest valid operating station for the county and update the mapping table. Check whether the coordinates of the county administrative center and the station in the mapping table are consistent with the spatial reference coordinate system of the target area (such as the CGCS2000 coordinate system). If there is a coordinate reference deviation, perform coordinate transformation and calibration to avoid spatial deviation in the subsequent connection path calculation.

[0092] For each pair of counties within the target area (denoted as starting county A - ending county B), the system completes the matching of mapped stations and the targeted retrieval of the shortest railway travel time between the corresponding stations. The specific operation is as follows:

[0093] From the verified mapping table, extract the mapping station corresponding to the starting county A (denoted as station X) and the mapping station corresponding to the ending county B (denoted as station Y), and record the unique identifiers of stations X and Y; retrieve the shortest travel time dataset between railway stations calculated in step 3.1. This dataset stores the shortest railway travel time for all reachable station pairs using the departure station-arrival station as the index; the system retrieves the corresponding shortest railway travel time in the dataset with station X as the departure station and station Y as the arrival station. If a valid value is found (i.e., there is a reachable train between the two stations), the time is recorded; if no value is found (i.e., there is no direct / stopping train between the two stations), it is marked as unreachable by rail between the stations, and subsequent connection time aggregation is not performed; bind the starting station-arrival station identifier and the corresponding railway travel time / unreachable marker to each pair of county pairs to form a basic association list of county pairs-station pairs-railway travel time, which facilitates subsequent connection time aggregation operations.

[0094] For county pairs where valid railway travel times between stations are found, the system additionally calculates the connecting times of the two road segments and adds them to the railway travel time to generate the shortest railway travel time from the origin county administrative center, the origin station, the arrival station, to the destination county administrative center. The specific operation is as follows:

[0095] The rules for calculating connecting routes and times are determined: The connecting time calculation reuses the highway traffic network dataset (road network topology model + off-peak travel speed) constructed in step 2. Since urban and rural road travel connected by railways is usually not included in peak-hour congestion considerations, peak-hour congestion can be switched if there are special analysis needs. The connecting route time is calculated with the shortest time as the core principle.

[0096] Calculation of the connection time from the starting county administrative center to the starting station (station X): Match the geographical coordinates of the starting county administrative center A to the nearest road node in the road network model, and similarly match the entrance / exit coordinates of station X to the nearest road node in the road network model. Use the off-peak period weight set and calculate the shortest road travel time between the two nodes using the Dijkstra algorithm. This time is the starting connection time (denoted as T1). If the straight-line distance between the administrative center and station X is ≤1 km and there is a direct connection via an urban road, T1 can be directly assigned a preset fixed short travel time (e.g., 5 minutes) to simplify the calculation.

[0097] Calculation of connection time from arrival station (station Y) to the destination county administrative center: Using the same method as the starting point connection, calculate the shortest road travel time from the exit node of station Y to the matching node of the destination county B administrative center, i.e., the destination connection time (denoted as T2); sum up the starting point connection time (T1), the shortest rail journey time between stations (Trail), and the destination connection time (T2) to obtain the shortest rail travel time for the entire journey between the starting point county A and the destination county B administrative center (T total = T1 + Trail + T2); if the connection time for a certain segment cannot be calculated due to missing road network data, mark the connection time for that segment as unknown, and mark the entire rail travel time as an unreachable connection route, and include it in the anomaly list.

[0098] Following the principles of full traversal, step-by-step calculation, and categorized labeling, the shortest rail travel time for all county pairs within the target area is calculated to ensure no omissions. The specific steps are as follows:

[0099] Based on all counties within the target area, generate all combinations of origin and destination counties (including AB and BA bidirectional combinations) to avoid missing county pairs. If the railway between the origin and destination stations of a county pair is inaccessible, mark the shortest railway travel time for that county pair as "railway route inaccessible". If the origin / destination county has no mapping station, mark it as "no railway connection station, inaccessible". If the connection time calculation is abnormal (e.g., connection route inaccessible), mark it as "connection route inaccessible". If there are no abnormalities in the above steps, record the shortest railway travel time for the entire journey after superposition. Classify all county pairs marked as inaccessible by inaccessibility type and record the county identifier, reason for inaccessibility, mapping station information, etc., to facilitate subsequent manual review and data correction.

[0100] Based on the calculation results of all county pairs, a standardized railway shortest trip OD matrix is ​​constructed to ensure that the matrix format is consistent with the dimensions and structure of the highway OD matrix generated in step 2. The specific operation is as follows:

[0101] Matrix dimension and index determination: The administrative codes of all counties in the target area are used as the row index (starting county) and column index (ending county) of the matrix. The number of rows and columns of the matrix are equal to the total number of counties in the target area, and the county order of the rows and columns is completely consistent with the highway OD matrix to ensure the compatibility of subsequent multi-modal traffic data fusion.

[0102] Matrix data filling:

[0103] Based on the correspondence between row index (originating county) and column index (destination county), the shortest rail travel time for each pair of counties is filled into the corresponding cells of the matrix. For all county pairs marked as unreachable, a uniform unreachable identifier (such as -1 or unreachable) is filled into the corresponding cells, and the identifier format is consistent with the unreachable identifier in the highway OD matrix. The matrix data is converted into a two-dimensional relational table format, with core fields including originating county code, originating county name, destination county code, destination county name, shortest rail travel time (minutes), and reachability identifier, ensuring that the data can be directly read and processed in subsequent steps.

[0104] After completing the OD matrix construction, structured storage is performed and a full-link data traceability mechanism is established to ensure data traceability and updability. The specific operations are as follows:

[0105] The railway OD matrix is ​​stored in a designated directory of the cloud database. The storage format supports both two-dimensional arrays (for easy algorithm calls) and relational data tables (for easy manual queries). A data version number is set, recording the matrix generation time, the railway timetable version used for calculation, and the rules for calculating highway connection time. An association index is established between the OD matrix and the original data source, including: the mapping station information of the corresponding county pairs associated with the matrix cell data, the shortest railway travel time calculation log between stations, and the path information for calculating highway connection time. The matrix as a whole is associated with the railway traffic timetable dataset in step 1 and the railway passenger network model in step 3.1, realizing full-link traceability of OD matrix values, calculation process, and original data. Automatic / manual update trigger rules are configured for the OD matrix. When railway timetable data is updated, county-station mapping relationships are adjusted, or highway connection network data is updated, the system can automatically trigger matrix recalculation or support manual update operations to ensure the timeliness of matrix data.

[0106] In this embodiment of the invention, by employing a county-station mapping method based on spatial correlation and a railway network modeling technique based on timetables, the technical problems of the lack of direct correspondence between administrative division units and the physical locations of railway stations, as well as the discretization of time calculations caused by the constraint of fixed train schedules on railway travel, are overcome. This achieves the technical effect of accurately converting discrete train schedules into continuous inter-regional railway accessibility time data and constructing a complete and structured regional railway travel OD matrix, providing an accurate data foundation for objectively evaluating the role of railways in the integrated transportation network.

[0107] In a preferred embodiment of the present invention, step 4 above may include:

[0108] Step 4.1: Based on the shortest-trip OD matrix for highway traffic generated in Step 2 and the shortest-trip OD matrix for rail traffic generated in Step 3, for each pair of counties within the target area, extract the corresponding highway and rail travel times from the two OD matrices. Specifically, this includes retrieving and loading the shortest-trip OD matrix for highway traffic generated in Step 2 and the shortest-trip OD matrix for rail traffic generated in Step 3 from storage. Both matrices exist in a structured two-dimensional table format, where rows and columns correspond to the counties within the target area, and the values ​​in the table represent the shortest highway and rail travel times between specific county pairs. The system then iterates through all possible county pair combinations within the target area in a predetermined order. For each pair of counties, based on the row and column indices of the two counties in the matrices, the system precisely extracts two sets of time data from the corresponding cells of the highway and rail OD matrices: the shortest travel time for the county pair via the highway network and the shortest travel time via the rail network.

[0109] Step 4.2 involves comparing the extracted road and rail travel times pairwise and selecting the smaller value as the shortest reachable time between the county pairs based on the multimodal transportation network. Specifically, after extracting the road and rail travel times for any county pair from the two matrices, the system immediately compares these two time values. The comparison logic is to determine which value is smaller, i.e., which mode of transportation provides a shorter travel time between the county pairs. The system performs a minimum value operation, selecting this smaller value as the theoretical shortest reachable time between the county pairs after comprehensively considering both road and rail transportation modes. This value represents the best time efficiency that travelers can obtain by choosing the optimal mode of transportation between the county pairs.

[0110] Step 4.3 summarizes the shortest reachability times for all county pairs, constructing and outputting a comprehensive transportation network dataset characterizing the overall connectivity efficiency of the region. This includes repeatedly performing the data extraction from step 4.1 and the minimum value comparison and assignment operations from step 4.2 on all county pairs within the target area. Subsequently, the system aggregates all county pairs and their corresponding comprehensive shortest reachability times. These results are reorganized into a new, structured two-dimensional relational dataset. In this new dataset, rows and columns still correspond to individual counties, and each cell stores the comprehensive shortest reachability time between the county corresponding to that row and the county corresponding to that column. This dataset comprehensively depicts the optimal travel time between any two counties within the target area based on the existing road and rail networks, thus constituting a comprehensive transportation network dataset. This dataset is ultimately output by the system and persistently stored, serving as the core input for subsequent spatial analysis and service level assessment.

[0111] In this embodiment of the invention, by employing the technical means of parallel reading of the OD matrix of highways and railways and performing pairwise time value extraction and minimum value fusion processing, the technical problems of traditional single-mode evaluation being unable to reflect the optimal choice behavior in real travel and the lack of a unified comparable benchmark for multi-source heterogeneous travel time data are overcome. Thus, a comprehensive transportation network dataset that can comprehensively reflect the optimal timeliness of highways and railways and serve as a unified measurement benchmark for the overall connectivity efficiency of the region is constructed, laying a core data foundation for subsequent isochronous circle generation and service evaluation.

[0112] In a preferred embodiment of the present invention, step 5 above may include:

[0113] Step 5.1 involves associating the shortest travel time between each pair of counties in the integrated transportation network dataset with the corresponding administrative center geographic coordinates to form a discrete point-based spatiotemporal dataset. Specifically, this includes: reading the generated and stored integrated transportation network dataset, which records the shortest travel time between each pair of counties within the target area. The system then extracts the precise latitude and longitude coordinates of the administrative center for each county from the basic geographic information dataset. Next, the system associates the shortest travel time between each pair of counties with the administrative center geographic coordinates of the county serving as the destination in that pair. Specifically, for each county serving as the starting point for analysis, the system creates a dataset containing the administrative center geographic coordinates of all other counties within the target area, as well as the shortest travel time from the starting county to these counties. Thus, for each starting county, a discrete point-based spatiotemporal dataset is formed, using geographic coordinates as the carrier and including the time required to travel from that starting point to that point.

[0114] Step 5.2 uses the shortest travel time from the point-based spatiotemporal dataset as the interpolation basis, and performs diffusion calculations in continuous geographic space using a spatial interpolation algorithm. Specifically, this includes using a discrete point-based spatiotemporal dataset for a specific starting county as the input data source. The system selects the geographic coordinates of each point as the spatial location input and the corresponding shortest travel time as the numerical input to be interpolated. Then, the system calls a built-in spatial interpolation algorithm, such as inverse distance weighted interpolation or Kriging interpolation. This algorithm, based on the time values ​​of known points in space, uses specific mathematical rules to calculate the estimated travel time required to depart from the starting county for each unsampled location in the entire continuous geographic space. This process essentially involves spatial numerical diffusion and smoothing calculations, ultimately generating a continuous time distribution surface covering the entire target area, commonly referred to as the time cost surface.

[0115] Step 5.3: Based on the diffusion calculation results, closed contour lines representing different travel time thresholds are drawn on the electronic map of the target area, starting from the administrative center of each county. These closed contour lines constitute the isochronous travel circles. Specifically, this includes: visually drawing the continuous time distribution surface generated in Step 5.2 on the digital base map or electronic map background of the target area. The system sets a series of specific travel time thresholds, such as ten minutes, thirty minutes, and sixty minutes. For each set time threshold, the system finds all spatial locations on the time distribution surface whose time value equals that threshold and connects these points to form closed ring contour lines. The area enclosed by each closed contour line represents the geographical range that can be reached from the administrative center of that starting county, provided that the travel time does not exceed that time threshold, through the integrated transportation network. These closed ring areas formed by different time thresholds together constitute a complete isochronous travel circle map centered on the administrative center of the county, and are overlaid and displayed by the system on the electronic map platform.

[0116] In this embodiment of the invention, by using a technique that associates discrete inter-county time data with geographic coordinates to form a point dataset, and then uses a spatial interpolation algorithm to perform time diffusion calculations on continuous geographic space, the technical problems of traditional methods, such as the difficulty in intuitively displaying spatial continuity of point-to-point travel time data and the inability to effectively express the reachability of any point, are overcome. This achieves the technical effect of transforming abstract travel time data into a continuous and intuitive spatial isochronous map, making the spatial distribution pattern and radiation range of traffic service levels clearly visible, and providing direct graphical insight for regional traffic pattern analysis.

[0117] In a preferred embodiment of the present invention, step 6 above may include:

[0118] Step 6.1: From the isochronous travel circles generated in Step 5, extract the spatial boundary corresponding to the 1-hour travel time threshold centered on each county's administrative center. Specifically, the system first calls and loads all the stored isochronous travel circle data generated in Step 5. This data contains a series of closed spatial polygons defined by different travel time thresholds, starting from each county's administrative center. For each county, the system performs query and extraction operations in its isochronous circle data, specifically identifying and extracting the spatial boundary enclosed by the closed isoline representing the 1-hour travel time threshold. This boundary is a polygonal geometric object that precisely defines the maximum geographical range reachable within one hour from the county's administrative center via the integrated transportation network. The system associates this 1-hour spatial boundary polygonal object with the corresponding county and temporarily stores it, preparing for the next step of analysis.

[0119] Step 6.2: Based on the spatial boundary of the 1-hour isotime circle, perform spatial overlay analysis and statistics with the administrative division boundaries and resident population distribution data in the basic geographic information dataset; calculate the geographical area covered by the 1-hour isotime circle of each county and the total number of resident population within that area, and calculate the area coverage rate and population coverage rate of the corresponding 1-hour transportation circle for each county. Specifically, the system starts the spatial analysis engine and processes the 1-hour spatial boundary polygon of each county in sequence. For the county currently being analyzed, the system performs spatial overlay analysis: overlaying the 1-hour boundary polygon of the county with the detailed administrative division polygon layer stored in the basic geographic information dataset; simultaneously, overlaying it with the population distribution layer containing resident population statistics for each street, township, or finer-grained unit. Through overlay analysis, the system first calculates the total geographical area covered by the 1-hour boundary polygon. Then, through spatial correlation and statistics, the system identifies all basic population statistics units that are completely or partially covered by this 1-hour boundary polygon, and summarizes and calculates the estimated total number of resident populations within the polygon's area based on area weight or other allocation algorithms. Finally, the system uses pre-acquired total area and total population data of the target area to calculate the county's one-hour transportation circle area coverage rate and population coverage rate, respectively. This is achieved by dividing the covered area by the total area and the covered population by the total population. The formulas for the city's one-hour transportation circle area and population coverage level indicators are as follows:

[0120] , ;

[0121] in, This represents the geographical area that can be covered by a 1-hour travel time circle centered on county i; This represents the population that can be covered by a 1-hour travel time circle centered on county i; and These represent the province's total area and population, respectively. This represents the area within a 1-hour travel radius of county i. This represents the area and population coverage of a 1-hour transportation radius of county i, where i refers to a specific county.

[0122] Step 6.3, based on area coverage and population coverage, conducts horizontal comparisons and classifications of all counties to quantitatively assess and display the spatial differentiation characteristics of transportation circle service levels among different counties within the target area, providing a basis for transportation planning decisions. Specifically, after calculating the area coverage and population coverage of the one-hour transportation circle for all counties, the system aggregates these two indicators into a complete evaluation dataset. Based on this dataset, the system conducts horizontal multi-dimensional comparative analysis of all counties, such as sorting by coverage values ​​from high to low, or clustering based on value ranges. The system can further apply preset grading standards to assign a grade label to the transportation circle service level of each county. Finally, the system clearly displays the quantitative differences and spatial differentiation characteristics of transportation circle service levels among different counties by generating thematic maps, statistical charts, and comprehensive analysis reports. This complete set of analysis results can clearly identify which areas have high transportation convenience and which areas have service shortcomings, thus providing a direct and quantitative basis for determining key areas for transportation infrastructure investment and optimizing network layout planning decisions. The formula for highway service level is as follows:

[0123] ;

[0124] in, To improve the level of highway travel services from county i to j, This indicates the peak-hour highway travel service level from county i to county j. This indicates the level of highway travel service during off-peak hours from county i to county j.

[0125] i represents the county number of the departure point, and j represents the county number of the destination point.

[0126] In this embodiment of the invention, by employing the core technical means of extracting the one-hour spatial boundary from the traffic isochronous circle and performing spatial overlay analysis and statistical calculation with administrative division and population distribution data, the technical problems of traditional traffic circle assessment, which only focuses on the spatial radiation range and cannot quantify its service population size, and which are difficult to make fair cross-regional comparisons under unified indicators, are overcome. This achieves the technical effect of transforming traffic accessibility into two core quantitative indicators: area coverage rate and population coverage rate, and using these as a basis for horizontal comparison and hierarchical classification of different regions. Ultimately, this provides an objective and accurate decision-making basis for identifying service shortcomings and optimizing resource allocation.

[0127] like Figure 2 As shown, embodiments of the present invention also provide an urban transportation circle service level analysis and evaluation system, including:

[0128] The acquisition module is used to acquire basic geographic information, highway network data, and railway timetable data for each county within the target area.

[0129] The calculation module is used to calculate and generate the pairwise shortest road travel time of each county administrative center in the target area during peak and off-peak hours based on highway traffic network data, so as to obtain the shortest travel OD matrix of highway traffic.

[0130] The modeling module is used to calculate and generate the shortest railway travel time between each county administrative center in the target area based on railway traffic timetable data, and obtain the shortest railway travel OD matrix.

[0131] The fusion module is used to perform minimum value fusion processing on the corresponding travel times between every two counties based on the shortest travel OD matrix of highway traffic and the shortest travel OD matrix of railway traffic, so as to generate the comprehensive shortest travel time between each county and establish a comprehensive transportation network dataset.

[0132] The algorithm module is used to generate isochronous travel circles centered on the administrative centers of each county based on the comprehensive shortest travel time in the comprehensive transportation network dataset using a spatial interpolation algorithm.

[0133] The processing module is used to calculate the area coverage rate and population coverage rate of the 1-hour transportation circle centered on each county, based on the boundary of the 1-hour isotime circle in the transportation travel isotime circle, combined with the area and resident population data of each county, and to conduct quantitative evaluation and comparative analysis of the service level of the transportation circle based on the coverage rate.

[0134] In the specific embodiments of the present invention described above, the process of constructing the traffic circle analysis model for counties, cities, and districts includes:

[0135] (a) Definition of transportation circles for counties, cities, and districts: These are defined as the spatial and temporal boundaries accessible from the administrative centers of counties, cities, and districts via highways, railways, and other means. Considering that air travel has no competitive advantage over ground transportation within an 800-kilometer radius and the proportion of passengers using air travel is very low, air travel is not considered in the transportation circles of counties, cities, and districts.

[0136] (II) Traffic Accessibility Model for Counties, Cities, and Districts: Accessibility is reflected by the shortest travel time between two points using multiple modes of transportation. For ease of model calculation, transfers between highways and railways are not considered here. The shortest travel time from county / city / district i to j is defined as... Then we have:

[0137] ;

[0138] in, This represents the shortest travel time from county, city, or district i to j, where i represents the departure point and j represents the destination. This represents the shortest travel time from i to j by rail. This represents the shortest travel time from i to j via highway. To take the minimum of the two, we choose the fastest mode of transportation.

[0139] (III) Highway Service Level (Congestion) Model: Considering the impact of highway traffic congestion, the highway service level (congestion) is represented by the ratio of peak travel time between two points, such as 8:00 AM, to off-peak travel time (such as midnight). The highway service level is defined as follows: Level i to level j of highway service from county, city, and district are... Then we have:

[0140] ;

[0141] in, To improve the level of highway travel services from county i to j, This indicates the peak-hour highway travel service level from county i to county j. This indicates the level of highway travel service during off-peak hours from county i to county j.

[0142] i represents the county number of the departure point, and j represents the county number of the destination point.

[0143] (iv) Indicators of Area and Population Coverage within a City's 1-Hour Transportation Circle: Taking the city as the center, the geographical area (combining geographic area data) and population (combining district / county resident population data) covered by the 1-hour transportation circle radiating outwards are used to reflect the city's 1-hour transportation circle area and population coverage level. Definition This represents the geographical area that can be covered by a 1-hour travel radius centered on (i) of a county, city, or district. This represents the population that can be covered by a 1-hour transportation radius centered on (i) of a county, city, or district. and These represent the province's total area and population, respectively. and Let i represent the area and population coverage of the 1-hour transportation radius of counties, cities, and districts respectively.

[0144] , ;

[0145] in, This represents the geographical area that can be covered by a 1-hour travel time circle centered on county i; This represents the population that can be covered by a 1-hour travel time circle centered on county i; and These represent the province's total area and population, respectively. This represents the area within a 1-hour travel radius of county i. This represents the area and population coverage of a 1-hour transportation radius of county i, where i refers to a specific county.

[0146] In the specific embodiments of the present invention described above, the implementation path of the traffic circle analysis model for counties, cities, and districts is as follows:

[0147] (I) Data Collection and Processing for the Model: Highway traffic network data, relevant railway timetables, and local government location data for each prefecture-level city or county / city / district during peak and off-peak periods were collected and organized. Traffic network data were sourced from Zhejiang Provincial Institute of Surveying and Mapping Science and Technology, the 12306 website, Baidu Maps, Gaode Maps, etc.

[0148] (II) Construction of the shortest travel OD matrix table: 1. Construct the shortest travel OD matrix table for highway traffic during peak and off-peak hours. Based on the collected highway network information (including highway alignment maps, mileage information, and highway entrance / exit coordinates), and assigning average travel speeds to road segments at different times and for different road grades (average travel speeds for different times and road grades can be obtained from historical data from Baidu or Gaode Maps, and shortest path algorithms such as Dijkstra's are used to calculate the shortest highway travel times between any two local government locations in 90 districts and counties of Zhejiang Province during peak and off-peak hours). 2. Highway travel service level analysis. Based on the highway travel time data during peak and off-peak hours among the 90 districts and counties, the highway travel service level among the 90 districts and counties is obtained. 3. Constructing a railway shortest travel OD matrix table. Based on the basic data of railway shortest travel timetables between stations, and adjusted according to the actual coverage area of ​​each station, the shortest railway travel times between any two counties and cities in Zhejiang Province and provincial capitals in the Yangtze River Delta are generated. 4. Based on the highway and railway shortest travel time matrix data, a comprehensive transportation network dataset of shortest travel time matrix is ​​established.

[0149] (III) Construction of the travel time isochronous circles of counties, cities and districts: Based on the measurement results of the shortest travel time of counties, cities and districts, spatial difference processing is carried out with the help of spatial analysis platforms such as ArcGIS to construct a 1-hour (60-minute) travel time isochronous circle centered on each county, city and district, which intuitively reflects the spatiotemporal accessibility of the central city and its morphological characteristics.

[0150] (iv) Analysis of the coverage level of the 1-hour transportation circle of counties, cities and districts: Based on the 1-hour (60-minute) transportation time circle of counties, cities and districts, and combined with the area and permanent population of counties, cities and districts, the area coverage and population coverage of the 1-hour transportation circle of counties, cities and districts are calculated, and compared with the area and total population of the province, the area and population coverage rate of the 1-hour transportation circle of counties, cities and districts are obtained.

[0151] (vi) Dynamically evaluate and analyze the city’s future transportation planning based on the evaluation index results: Based on the analysis results of the coverage level of the 1-hour transportation circle of counties, cities and districts, we can carry out a horizontal comparison of the development level of transportation circles between different counties, cities and districts, identify the gaps and shortcomings in the radiation of transportation circles in different directions and regions of the city, and provide quantitative support for the subsequent planning of local transportation corridor projects.

[0152] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for analyzing and evaluating the service level of urban transportation circles, characterized in that, The method includes: Step 1: Obtain basic geographic information, highway network data, and railway timetable data for each county within the target area; Step 2: Based on highway traffic network data, calculate and generate the shortest road travel time for each county administrative center in the target area during peak and off-peak hours to obtain the shortest road traffic OD matrix; Step 3: Based on railway timetable data, calculate and generate the shortest railway travel time between each pair of county administrative centers within the target area to obtain the shortest travel OD matrix for railway traffic. Step 4: Based on the shortest travel OD matrix of highway traffic and the shortest travel OD matrix of railway traffic, the minimum value fusion processing is performed on the corresponding travel time between each pair of counties to generate the comprehensive shortest travel time between each county, so as to establish a comprehensive transportation network dataset. Step 5: Based on the comprehensive shortest travel time in the comprehensive transportation network dataset, generate isochronous travel circles centered on the administrative centers of each county using a spatial interpolation algorithm; Step 6: Based on the 1-hour isotime circle boundary in the transportation travel isotime circle, and combined with the area and resident population data of each county, calculate the area coverage rate and population coverage rate of the 1-hour transportation circle centered on each county, and conduct quantitative evaluation and comparative analysis of the service level of the transportation circle based on the coverage rate.

2. The method for analyzing and evaluating the service level of urban transportation circles according to claim 1, characterized in that, Obtain basic geographic information, highway network data, and railway timetable data for each county within the target area, including: Step 1.1: Acquire and integrate data from multiple data sources to form the basic geographic information dataset, the highway traffic network dataset, and the railway traffic timetable dataset; Step 1.2: The basic geographic information dataset shall include at least the geographic coordinates of the administrative centers and the administrative division boundaries of each county within the target area; the highway traffic network dataset shall include at least the traffic speed information and topological connection relationship of roads of different levels at different time periods; and the railway traffic timetable dataset shall include at least the train numbers and running times between railway stations covering each county within the target area.

3. The method for analyzing and evaluating the service level of urban transportation circles according to claim 2, characterized in that, Based on highway traffic network data, the shortest road travel times for each county administrative center within the target area are calculated during peak and off-peak hours to obtain the shortest road travel origin-destination (OD) matrix, including: Step 2.1: Transform the road network topology model with time-time weights by using the topological connectivity relationships in the highway traffic network dataset and the road traffic speed information at different times; Step 2.2: Based on the road network topology model, using the geographical coordinates of each county administrative center as the starting and ending points of travel, apply the shortest path algorithm to calculate the shortest road travel time between any two county administrative centers within the target area during peak and off-peak hours. The shortest road travel time is the total travel time for point-to-point travel, and its calculation process additionally includes the travel time by urban and rural roads from the starting county administrative center to the highway entrance, and from the highway exit to the ending county administrative center. Step 2.3: Store the calculation results in a structured format to generate a road traffic shortest trip OD matrix that includes both peak and off-peak hour data.

4. The method for analyzing and evaluating the service level of urban transportation circles according to claim 3, characterized in that, Based on railway timetable data, the shortest train travel times between each county administrative center within the target area are calculated, resulting in a shortest-travel-origin matrix for railway transportation, including: Step 3.1: Using the railway traffic timetable dataset and based on the coverage relationship between the geographical coordinates of the administrative center and the railway station, map each county to the corresponding railway station node; construct a railway timetable network graph with stations as nodes using train number and running time information, and calculate the shortest railway travel time between any two mapped station nodes. Step 3.2: Combine the shortest railway travel time with the above calculations, and additionally calculate the road connection time from the starting county administrative center to the corresponding departure railway station, and from the arrival railway station to the destination county administrative center, to generate the pairwise shortest railway travel time between any two county administrative centers in the target area; store the calculation results in a structured way as a railway traffic shortest travel OD matrix.

5. The method for analyzing and evaluating the service level of urban transportation circles according to claim 4, characterized in that, Based on the shortest trip origin-destination (OD) matrices for highway and rail traffic, the minimum travel times between any two counties are fused to generate a comprehensive shortest travel time between all counties, thus establishing a comprehensive transportation network dataset, including: Step 4.1: Based on the shortest trip OD matrix for highway traffic generated in Step 2 and the shortest trip OD matrix for railway traffic generated in Step 3; for each pair of counties in the target area, extract the corresponding highway travel time and railway travel time from the two OD matrices. Step 4.2: Compare the extracted road travel time and rail travel time one by one, and take the smaller value as the shortest reachable time between the two places in the county based on the multimodal transportation network. Step 4.3: Summarize the shortest reach times for all county pairs, construct and output a comprehensive transportation network dataset to characterize the overall connectivity efficiency of the region.

6. The method for analyzing and evaluating the service level of urban transportation circles according to claim 5, characterized in that, Based on the comprehensive shortest travel time in the integrated transportation network dataset, isochronous travel circles centered on the administrative centers of each county are generated using a spatial interpolation algorithm, including: Step 5.1: By combining the shortest travel time between each county in the comprehensive transportation network dataset, the data is associated with the corresponding geographical coordinates of the administrative center to form a discrete point-like spatiotemporal dataset. Step 5.2: Based on the comprehensive shortest travel time in the point spatiotemporal dataset as the interpolation basis, perform diffusion calculations in continuous geographic space using a spatial interpolation algorithm; Step 5.3: Based on the diffusion calculation results, draw closed contour lines on the electronic map of the target area, with each county administrative center as the starting point, representing different travel time thresholds. These closed contour lines constitute the isochronous travel circles.

7. The method for analyzing and evaluating the service level of urban transportation circles according to claim 6, characterized in that, Based on the boundaries of the 1-hour isotime circle in the transportation travel isotime circle, and combined with the area and resident population data of each county, the area coverage rate and population coverage rate of the 1-hour transportation circle centered on each county are calculated respectively. Then, a quantitative evaluation and comparative analysis of the service level of the transportation circle is conducted based on the coverage rate, including: Step 6.1: Extract the spatial boundary corresponding to the 1-hour travel time threshold centered on each county administrative center from the isochronous travel circle generated in Step 5. Step 6.2: Based on the spatial boundary of the 1-hour isotime circle, perform spatial overlay analysis and statistics with the administrative division boundary and resident population distribution data in the basic geographic information dataset; calculate the geographic area covered by the 1-hour isotime circle of each county and the total number of resident population within it, and calculate the area coverage rate and population coverage rate of the 1-hour transportation circle corresponding to the county. Step 6.3: Based on area coverage and population coverage, conduct horizontal comparisons and classify all counties to quantitatively assess and demonstrate the spatial differentiation characteristics of the service levels of different county transportation networks within the target area, providing a basis for transportation planning decisions.

8. A service level analysis and evaluation system for urban transportation circles, wherein the system implements the method as described in any one of claims 1 to 7, characterized in that, include: The acquisition module is used to acquire basic geographic information, highway network data, and railway timetable data for each county within the target area. The calculation module is used to calculate and generate the pairwise shortest road travel time of each county administrative center in the target area during peak and off-peak hours based on highway traffic network data, so as to obtain the shortest travel OD matrix of highway traffic. The modeling module is used to calculate and generate the shortest railway travel time between each county administrative center in the target area based on railway traffic timetable data, and obtain the shortest railway travel OD matrix. The fusion module is used to perform minimum value fusion processing on the corresponding travel times between every two counties based on the shortest travel OD matrix of highway traffic and the shortest travel OD matrix of railway traffic, so as to generate the comprehensive shortest travel time between each county and establish a comprehensive transportation network dataset. The algorithm module is used to generate isochronous travel circles centered on the administrative centers of each county based on the comprehensive shortest travel time in the comprehensive transportation network dataset using a spatial interpolation algorithm. The processing module is used to calculate the area coverage rate and population coverage rate of the 1-hour transportation circle centered on each county, based on the boundary of the 1-hour isotime circle in the transportation travel isotime circle, combined with the area and resident population data of each county, and to conduct quantitative evaluation and comparative analysis of the service level of the transportation circle based on the coverage rate.

9. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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