Calculation method for simulating urban road network traffic efficiency through spatial partition

Abstract

The invention relates to the technical field of data processing, in particular to a calculation method for simulating urban road network traffic efficiency by spatial partitions, which comprises the following steps: acquiring population raster data and road network data; converting the population raster data into a two-dimensional matrix and standardizing the two-dimensional matrix; spatial clustering is carried out by adopting a comprehensive distance index fusing geographic distance and population difference, a partition set is generated, a shortest path between point pairs is searched in a road network, the ratio of the length of a spatial linear path to the length of the shortest path is calculated as a length efficiency index, and a comprehensive traffic efficiency index is output. According to the method, population distribution characteristics are fused through a space partitioning technology, diversified travel scenes are covered by Monte Carlo simulation, and the accuracy of urban road network traffic efficiency evaluation is remarkably improved by combining a path length and shape two-dimensional evaluation mechanism. The comprehensive traffic efficiency index directly represents the overall efficiency level of the road network, and a quantitative basis is provided for road network structure optimization.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method for calculating the traffic efficiency of urban road networks by spatial partitioning. Background Technology

[0002] Urban road network traffic efficiency measures how smoothly the road network supports the movement of people and goods. It is mainly affected by road hierarchy design, signal coordination strategies, and demand management measures. A hierarchical road system can guide the rational allocation of traffic flow, signal optimization can reduce the number of consecutive stops, and demand regulation, such as prioritizing public transportation, can reduce vehicle density, thereby improving network capacity, shortening travel time, reducing energy consumption, and making urban functions more closely connected, thus improving the overall efficiency of urban operation.

[0003] Because existing traffic efficiency assessment methods fail to integrate population spatial distribution information and lack efficient stochastic simulation techniques, travel path generation cannot accurately reflect actual traffic demand. This is because differences in population density directly affect the distribution of travel origins and destinations, and existing methods rely on fixed algorithms that ignore probability sampling, resulting in a lack of representativeness in path selection. For example, in urban road network planning scenarios, ignoring travel hotspots in densely populated areas leads to an underestimation of the traffic efficiency of congested paths during peak hours, failing to support effective traffic optimization strategies. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for calculating the traffic efficiency of urban road networks by simulating spatial partitioning. This invention solves the technical problem of inaccurate assessment of urban road network traffic efficiency caused by ignoring population spatial distribution information and lacking effective simulation methods.

[0005] To solve the above-mentioned technical problems, the specific technical content of the present invention is as follows: The present invention provides a method for calculating the traffic efficiency of urban road networks by simulating spatial partitioning, comprising: Step 1: Obtain population raster data and road network data for the urban area; Step 2: Based on the population raster data from Step 1, the population raster data is converted into a two-dimensional matrix including longitude, latitude, and population size. The matrix data is standardized, and a spatial clustering algorithm is executed using a comprehensive distance index that integrates geographical distance and population size differences to generate multiple partitions. Step 3: Based on the set of partitions generated in Step 2, perform trip allocation processing, calculate the population weight of each partition, and allocate the set total trips to each partition according to the weights. Step 4: Based on the allocation results in Step 3, perform the point of departure generation process, and randomly generate point of departure locations corresponding to the allocated number within each partition using the Monte Carlo method. Step 5: Based on the travel point locations generated in Step 4, perform travel point pair construction processing, merge all partition travel point locations, combine non-repeating travel point pairs, and generate spatial straight-line path vector data for each point pair; Step 6: Based on the spatial straight path vector data from Step 5 and the road network data from Step 1, perform path efficiency calculation processing, find the nearest projection node of each travel point on the road network, calculate the shortest path between projection nodes, connect the travel point with the corresponding projection node to form complete shortest path vector data, calculate the ratio of the spatial straight path length to the shortest path length as the path length efficiency index, and calculate the curve shape difference between the spatial straight path and the shortest path as the path shape efficiency index. Step 7: Based on the path length efficiency index and path shape efficiency index from Step 6, perform efficiency evaluation processing, construct a point-to-point traffic efficiency index, and repeat Steps 2 to 7. Each time Step 2 is executed, a new partition set is generated. The average value of the point-to-point traffic efficiency indices generated by multiple simulations is taken to obtain the comprehensive traffic efficiency index. The comprehensive traffic efficiency index is output as an indicator negatively correlated with traffic efficiency.

[0006] Furthermore, in the method for calculating the traffic efficiency of urban road networks by spatial partitioning as described in this invention, step 2 includes: Based on population raster data, the population raster data is converted into a two-dimensional matrix including longitude, latitude, and population size; Based on the transformed two-dimensional matrix, the longitude, latitude, and population values ​​are standardized to generate a standardized matrix. Based on the longitude, latitude, and population data in the generated standardized matrix, a comprehensive distance index is constructed that integrates geographical distance and population differences. Geographical distance is calculated using longitude and latitude differences, while population differences are calculated using population value differences. The constructed comprehensive distance index is then used to execute a spatial clustering algorithm to generate a set of partitions.

[0007] Furthermore, in the method for calculating the traffic efficiency of urban road networks by spatial partitioning as described in this invention, step 2 further includes: Based on the standardized matrix, the longitude difference, latitude difference, and population difference are calculated for two pixel samples. Based on the calculated longitude and latitude differences, the geographic spatial distance is calculated as the square root of the sum of the squares of the longitude and latitude differences. Based on the calculated population difference, the attribute spatial distance is calculated as the absolute value of the population difference. The sum of one-third of the geographic spatial distance and two-thirds of the attribute spatial distance is used as the comprehensive distance index between two pixels. Furthermore, in the method for calculating the traffic efficiency of urban road networks by spatial partitioning as described in this invention, step 3 includes: Based on the set of districts and their total population data, calculate the weight ratio of the total population of each district to the total population of the entire region; Based on the calculated weight ratio, the total number of trips is multiplied by the weight ratio to generate the allocation value of the number of trip points in each zone. Furthermore, in the method for calculating the traffic efficiency of urban road networks by spatial partitioning as described in this invention, step 6 includes: Based on the location of the travel point and road network data, for the travel point in the travel point pair, find the node in the road network that is closest to it as the projection node; Based on the found projection nodes, a path search algorithm is used to calculate the shortest connected path between the projection nodes; based on the shortest connected path, the departure point is connected to the projection node, and a complete vector path is generated by combining the paths between the nodes. Furthermore, in the method for calculating the traffic efficiency of urban road networks by spatial partitioning as described in this invention, step 6 further includes: Based on spatial straight-line path vector data and complete shortest path vector data, the path vector line elements are discretized and sampled to generate a discrete point sequence. Based on the generated discrete point sequence, the distance matrix of all point pairs between the discrete point sequences is calculated, and the maximum and minimum distance values ​​in the distance matrix are determined as the path shape efficiency index. Furthermore, in the method for calculating the traffic efficiency of urban road networks by spatial partitioning as described in this invention, step 7 includes: Based on the path length efficiency index, generate the converted value of the path length efficiency index; Based on the generated conversion value and the path shape efficiency index, the conversion value is multiplied by the path shape efficiency index to generate the traffic efficiency index of the trip point pair. Furthermore, in the method for calculating the traffic efficiency of urban road networks by spatial partitioning as described in this invention, step 7 further includes: Based on the comprehensive traffic efficiency index, the comprehensive traffic efficiency index is compared with a preset threshold; When the comprehensive traffic efficiency index exceeds the threshold, locate the distribution area of ​​the trip point pairs with the highest comprehensive traffic efficiency index. Based on the location distribution area, the topological characteristics of the road network are analyzed; Based on the analysis results of the road network topology characteristics, suggestions are generated for adding new connecting roads or adjusting the road grade. Furthermore, in the method for calculating the traffic efficiency of urban road networks by spatial partitioning as described in this invention, step 7 further includes: Initialize the random number generator and assign a different random number seed for each simulation; Set the number of repeated simulations to be in the range of 5 to 10; For each simulation, an assigned random seed value is used to control the point of departure generation process, generating point of departure location data; Based on the generated point-of-department location data, point-of-department pair construction processing is performed to generate spatial straight-line path data; Based on the generated spatial straight path data, perform path efficiency calculation processing to generate path length efficiency index and path shape efficiency index; Based on the generated path length efficiency index and path shape efficiency index, perform index construction to generate a point-to-point traffic efficiency index. Collect all simulated point-pair traffic efficiency indices to form an index set, and perform average calculation based on the index set.

[0008] Furthermore, in the method for calculating the traffic efficiency of urban road networks by spatial partitioning as described in this invention, step 7 further includes: Based on partition boundary data, point-of-department location data, path data where the path shape efficiency index is below a preset threshold, and the comprehensive traffic efficiency index; Overlay a zone boundary layer on the electronic map base map, and then overlay point location distribution markers based on the overlaid zone boundary layer; Based on path data where the path shape efficiency index is below a preset threshold, a path efficiency heatmap is generated, producing a visualization result that includes a partition boundary layer, trip point location distribution markers, and the path efficiency heatmap.

[0009] Beneficial effects of this invention; This invention precisely quantifies population distribution characteristics through spatial partitioning technology. Based on population raster data, it generates a set of geographically continuous partitions with similar density characteristics, enabling travel demand allocation to accurately match actual population aggregation patterns. The Monte Carlo method uses independent seed values ​​to randomly generate travel point locations within each partition, covering diverse travel scenarios in densely populated and sparsely populated areas, eliminating sampling bias from fixed algorithms. The path length efficiency index quantifies the deviation between the actual path length and the theoretically optimal path, while the path shape efficiency index captures morphological differences caused by path detours. The point-to-point traffic efficiency index, constructed collaboratively by these two indices, comprehensively reflects the defects in the road network topology. By averaging the results of multiple Monte Carlo simulations, a comprehensive traffic efficiency index is generated. Its negative correlation directly characterizes the overall efficiency level of the road network, providing an objective quantitative basis for road network structure optimization and effectively solving the problem of inaccurate evaluation caused by ignoring population spatial distribution information and simulation defects. Attached Figure Description

[0010] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the drawings without creative effort.

[0011] Figure 1 A flowchart illustrating the calculation method for spatial partitioning simulation of urban road network traffic efficiency provided in an embodiment of the present invention.

[0012] Figure 2 This is a schematic diagram of the spatial distribution of road network and population data according to the present invention.

[0013] Figure 3 This is a schematic diagram of the spatial partitioning of the population grid of the present invention.

[0014] Figure 4 This is a spatial distribution map of the travel point distribution, travel point connection lines, and shortest paths between travel points, as presented in this invention.

[0015] Figure 5 This is a schematic diagram of the traffic efficiency index and the comprehensive traffic efficiency index area of ​​the present invention. Detailed Implementation

[0016] To make the technical solution of the present invention clearer, the present invention will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. The present invention provided by various embodiments will be described in detail below with reference to the accompanying drawings. To better understand the purpose of the present invention, the present invention will be described in further detail below.

[0017] Please see Figures 1 to 5 The present invention provides a method for calculating the traffic efficiency of a spatially partitioned urban road network, comprising: Step 1: Obtain population raster data and road network data for the urban area; Step 2: Based on the population raster data from Step 1, the population raster data is converted into a two-dimensional matrix including longitude, latitude, and population size. The matrix data is standardized, and a spatial clustering algorithm is executed using a comprehensive distance index that integrates geographical distance and population size differences to generate multiple partitions. Step 3: Based on the set of partitions generated in Step 2, perform trip allocation processing, calculate the population weight of each partition, and allocate the set total trips to each partition according to the weights. Step 4: Based on the allocation results in Step 3, perform the point of departure generation process, and randomly generate point of departure locations corresponding to the allocated number within each partition using the Monte Carlo method. Step 5: Based on the travel point locations generated in Step 4, perform travel point pair construction processing, merge all partition travel point locations, combine non-repeating travel point pairs, and generate spatial straight-line path vector data for each point pair; Step 6: Based on the spatial straight path vector data from Step 5 and the road network data from Step 1, perform path efficiency calculation processing, find the nearest projection node of each travel point on the road network, calculate the shortest path between projection nodes, connect the travel point with the corresponding projection node to form complete shortest path vector data, calculate the ratio of the spatial straight path length to the shortest path length as the path length efficiency index, and calculate the curve shape difference between the spatial straight path and the shortest path as the path shape efficiency index. Step 7: Based on the path length efficiency index and path shape efficiency index from Step 6, perform efficiency evaluation processing, construct a point-to-point traffic efficiency index, and repeat Steps 2 to 7. Each time Step 2 is executed, a new partition set is generated. The average value of the point-to-point traffic efficiency indices generated by multiple simulations is taken to obtain the comprehensive traffic efficiency index. The comprehensive traffic efficiency index is output as an indicator negatively correlated with traffic efficiency.

[0018] Step 1 involves acquiring urban area population raster data and road network data as input sources for the technical solution. Population raster data includes spatialized population distribution information, while road network data consists of nodes and edges forming a topological structure. These two types of data together constitute the foundational geographic information carrier for subsequent spatial analysis. Population raster data characterizes the spatial distribution features of travel demand, while road network data provides the basic framework for path calculation.

[0019] Step 2 performs spatial partitioning based on the population raster data from Step 1. First, the population raster data is converted into a two-dimensional matrix structure including longitude, latitude, and population count. The longitude, latitude, and population count values ​​in the matrix are then standardized to eliminate dimensional differences. A spatial clustering algorithm is driven by a comprehensive distance index that integrates geographical distance and population count differences, generating a set of partitions with continuous geographical boundaries and similar internal population density characteristics. This partitioning result reflects the spatial heterogeneity of population clustering characteristics, providing a basis for spatial unit division for subsequent travel demand allocation.

[0020] Step 3 performs trip allocation processing based on the partition set generated in Step 2. It calculates the weight ratio of the total population of each partition to the total population of the entire region, and allocates the preset total trips to each partition according to the weight ratio. This process transforms the abstract trip demand into the specific number of locations within the spatial partitions, achieving spatial matching between traffic demand and population distribution, and providing a quantitative basis for Monte Carlo simulation.

[0021] Step 4 performs trip point generation based on the allocation results from Step 3. Within each partition, Monte Carlo random sampling is used to generate trip point coordinates corresponding to the allocated number. The randomization process is controlled by independent seed values, resulting in a differentiated spatial distribution of trip points in each simulation. The generated trip points represent the spatial probability distribution of potential origin and destination points within the partition, covering diverse travel scenarios in both densely populated and sparsely populated areas.

[0022] Step 5 performs trip point pair construction based on the trip point locations generated in Step 4. All regional trip points are merged and paired without repetition, generating spatial straight-line path vector data connecting the two points for each pair. This data represents the theoretically optimal spatial movement path and constitutes a benchmark reference system for evaluating road network efficiency.

[0023] Step 6 performs path efficiency calculation based on the spatial straight-line path vector data from Step 5 and the road network data from Step 1. The nearest projected node to the departure point is located on the road network, the shortest path between the projected nodes is calculated, and the departure points are connected to form a complete actual path. The ratio of the spatial straight-line path length to the actual path length is calculated to obtain the path length efficiency index. Simultaneously, the path shape efficiency index is calculated through the shape differences of the discrete point sequence. These two indices quantify path length redundancy and morphological detour degree, respectively, jointly reflecting the defects in the road network topology.

[0024] Step 7 performs efficiency evaluation based on the path efficiency index from Step 6. A point-to-point traffic efficiency index is constructed, integrating length efficiency and shape efficiency for dual-dimensional evaluation. The Monte Carlo process from Steps 2 to 7 is repeated. In each iteration, the partition set and travel point distribution are regenerated, and diverse travel scenarios are covered through multiple independent simulations. Finally, the average of the point-to-point traffic efficiency indices from multiple simulations is used to generate a comprehensive traffic efficiency index. This index value is negatively correlated with traffic efficiency, achieving an objective quantitative assessment of urban road network traffic efficiency.

[0025] Specifically, in the method for calculating the traffic efficiency of urban road networks by spatial partitioning as described in this invention, step 2 includes: Based on population raster data, the population raster data is converted into a two-dimensional matrix including longitude, latitude, and population size; Based on the transformed two-dimensional matrix, the longitude, latitude, and population values ​​are standardized to generate a standardized matrix. Based on the longitude, latitude, and population data in the generated standardized matrix, a comprehensive distance index is constructed that integrates geographical distance and population differences. Geographical distance is calculated using longitude and latitude differences, while population differences are calculated using population value differences. The constructed comprehensive distance index is then used to execute a spatial clustering algorithm to generate a set of partitions.

[0026] First, the input population raster data is processed, converting the raster data into a structured two-dimensional matrix. The rows of the two-dimensional matrix represent raster cells, and the columns store longitude, latitude, and population values, respectively, thus establishing a mapping between spatial location and population attributes. The conversion process preserves the spatial distribution characteristics of the original data, providing a unified data format for subsequent standardization operations.

[0027] Based on the transformed two-dimensional matrix, longitude, latitude, and population values ​​are standardized. Standardization eliminates the influence of different units on the data, ensuring that the longitude, latitude, and population values ​​are within a comparable range. The standardization method employs common data scaling techniques to generate a standardized matrix. This matrix serves as the input source for distance index calculations, enabling subsequent clustering algorithms to balance geographical and population attributes.

[0028] Based on the longitude, latitude, and population values ​​in the standardized matrix, a comprehensive distance index integrating geographical distance and population size differences is constructed. The geographical distance component is generated by calculating the square root of the sum of the squares of the longitude and latitude differences, quantifying spatial location differences; the population size difference component is generated by the absolute value of the difference in population size values, reflecting differences in population distribution. The comprehensive distance index integrates the geographical distance and population size difference components, assigning each a weight. A spatial clustering algorithm is applied using the comprehensive distance index, aggregating similar pixels based on the comprehensive distance index values ​​to generate a set of partitions with continuous geographical boundaries and consistent internal population density distribution. This set of partitions serves as the output, directly supporting the trip allocation processing in step 3.

[0029] Specifically, in the method for calculating the traffic efficiency of urban road networks by spatial partitioning as described in this invention, step 2 further includes: Based on the standardized matrix, the longitude difference, latitude difference, and population difference are calculated for two pixel samples. Based on the calculated longitude and latitude differences, the geographic spatial distance is calculated as the square root of the sum of the squares of the longitude and latitude differences. Based on the calculated population difference, the attribute spatial distance is calculated as the absolute value of the population difference. The sum of one-third of the geographic spatial distance and two-thirds of the attribute spatial distance is used as the comprehensive distance index between two pixels. First, the standardized matrix data is processed, and for any two pixel samples, the differences in longitude, latitude, and population size are calculated. The longitude difference reflects the location difference in the east-west direction, the latitude difference represents the location difference in the north-south direction, and the population size difference reflects the difference in population distribution. The difference calculation provides the basic data input for subsequent distance quantization, establishing a differentiated expression of spatial location and population attributes.

[0030] Based on the calculated longitude and latitude differences, geospatial distance is calculated. The geospatial distance is generated by taking the square root of the sum of the squares of the longitude and latitude differences; this calculation method accurately quantifies the spherical distance between two pixel samples. Geospatial distance characterizes the actual geographical proximity between samples and constitutes the spatial component of the comprehensive distance index.

[0031] Based on the calculated population difference, attribute spatial distance is calculated. The attribute spatial distance is taken as the absolute value of the population difference, directly quantifying the degree of difference in population distribution between two pixel samples. Simultaneously, geographic spatial distance and attribute spatial distance are integrated, and a comprehensive distance index is generated through weighted summation. The spatial component weight of the comprehensive distance index is set to one-third, and the attribute component weight is set to two-thirds. This index serves as the input metric for the spatial clustering algorithm, driving the aggregation process of similar pixels.

[0032] Specifically, in the method for calculating the traffic efficiency of urban road networks by spatial partitioning as described in this invention, step 3 includes: Based on the set of districts and their total population data, calculate the weight ratio of the total population of each district to the total population of the entire region; Based on the calculated weight ratio, the total number of trips is multiplied by the weight ratio to generate the allocation value of the number of trip points in each zone. First, the data on the set of partitions and their total populations are processed. The set of partitions is generated by the spatial clustering algorithm in step 2. The weight ratio of each partition's total population to the total population of the entire region is calculated. This weight ratio reflects the differences in population concentration among different partitions. The weight ratio calculation process uses a standard proportional operation method to generate a set of numerical weight coefficients.

[0033] Based on the calculated weight ratios, the preset total trip volume is multiplied by the weight ratio of each zone. The multiplication operation generates the corresponding zone's trip point allocation value, which represents the number of trip points that should be generated in each zone. The trip point allocation value is positively correlated with the zone's population size, achieving spatial matching between travel demand and population distribution.

[0034] The generated trip point allocation values ​​are output and directly input into the trip point generation process in step 4. These allocation values ​​quantify the travel demand intensity of each zone, providing a constraint on the number of points for Monte Carlo random sampling. This allocation mechanism ensures that densely populated areas receive more trip point sampling opportunities, improving the spatial representativeness of traffic efficiency assessment.

[0035] Specifically, in the method for calculating the traffic efficiency of urban road networks by spatial partitioning as described in this invention, step 6 includes: Based on the location of the travel point and road network data, for the travel point in the travel point pair, find the node in the road network that is closest to it as the projection node; Based on the found projection nodes, a path search algorithm is used to calculate the shortest connected path between the projection nodes; based on the shortest connected path, the departure point is connected to the projection node, and a complete vector path is generated by combining the paths between the nodes. The process performs path efficiency calculations, generating complete shortest path vector data based on origin point location data and road network data. First, for each origin point in a pair, the system searches for the spatially closest topological node in the road network node set as the projection node. This projection node serves as the mapping location of the origin point on the road network, achieving a matching between the origin point and the actual road network.

[0036] Based on the found projection nodes, a path search algorithm is used to calculate the shortest connected path between two projection nodes in the same travel point pair. The path search algorithm, based on the road network topology, traverses the connectivity relationships between nodes to determine the path with the minimum travel cost. The shortest connected path generation process outputs the path vector segments between nodes.

[0037] Based on the calculated shortest connecting path, the departure point is spatially connected to its corresponding projection node. Straight line segments connect the departure point and the projection node, and combined with the shortest connecting path segments between the projection nodes, vector line data including the complete path is generated. This vector line data fully represents the actual travel trajectory from the starting departure point through the road network to the ending departure point.

[0038] Specifically, in the method for calculating the traffic efficiency of urban road networks by spatial partitioning as described in this invention, step 6 further includes: Based on spatial straight-line path vector data and complete shortest path vector data, the path vector line elements are discretized and sampled to generate a discrete point sequence. Based on the generated discrete point sequence, the distance matrix between all point pairs in the discrete point sequence is calculated, and the maximum and minimum distance values ​​in the distance matrix are determined as indicators of path shape efficiency. The path shape difference quantification process is performed to generate a path shape efficiency index based on spatial straight-line path vector data and complete shortest path vector data. First, the features of the two path vector lines are discretized and sampled, extracting coordinate points along the path trajectory at fixed intervals. The sampling process generates a corresponding set of discrete point sequences, each sequence containing ordered spatial coordinate points, fully preserving the geometric features of the original path.

[0039] Based on the generated discrete point sequences, the spatial distance matrix of all point pairs between the two sequences is calculated. The distance matrix construction process traverses the discrete point combinations of the two paths and calculates the Euclidean distance between each pair of points. The distance matrix fully records the spatial deviation of corresponding points on the two paths, forming the basis for quantifying shape differences.

[0040] Based on the constructed distance matrix, the maximum and minimum distance values ​​for all corresponding point pairs in the matrix are determined. The selection process for the maximum and minimum distance values ​​is achieved through matrix traversal and comparison, and the output numerical results serve as a path shape efficiency index. This index reflects the degree of deviation between the actual path and the theoretical straight path curve shape; a larger deviation value indicates a more severe path detour.

[0041] Specifically, in the method for calculating the traffic efficiency of urban road networks by spatial partitioning as described in this invention, step 7 includes: Based on the path length efficiency index, generate the converted value of the path length efficiency index; Based on the generated conversion value and the path shape efficiency index, the conversion value is multiplied by the path shape efficiency index to generate the traffic efficiency index of the trip point pair. The process involves constructing a traffic efficiency index based on point pairs, generating a comprehensive evaluation index from path length efficiency and path shape efficiency indicators. First, the path length efficiency index data is processed; this index represents the ratio between the length of a spatial straight path and the actual path length. Then, a numerical transformation process is performed to generate a converted value for the path length efficiency index. This converted value reflects the negative characteristic of path length efficiency; that is, the lower the index value, the greater the loss of length efficiency.

[0042] Based on the generated conversion values ​​and path shape efficiency index data, numerical fusion processing is performed. The path shape efficiency index quantifies the degree of difference between the curve shape of the actual path and the theoretical path; a higher value indicates a more severe path detour. The conversion values ​​and the path shape efficiency index are multiplied to generate a product, which serves as the traffic efficiency index for the current travel point pair.

[0043] Specifically, in the method for calculating the traffic efficiency of urban road networks by spatial partitioning as described in this invention, step 7 further includes: Based on the comprehensive traffic efficiency index, the comprehensive traffic efficiency index is compared with a preset threshold; When the comprehensive traffic efficiency index exceeds the threshold, locate the distribution area of ​​the trip point pairs with the highest comprehensive traffic efficiency index. Based on the location distribution area, the topological characteristics of the road network are analyzed; Based on the analysis results of the road network topology characteristics, suggestions are generated for adding new connecting roads or adjusting the road grade. The process involves performing road network optimization analysis and generating road network improvement suggestions based on the comprehensive traffic efficiency index. First, the comprehensive traffic efficiency index data is processed; this index is generated by averaging multiple Monte Carlo simulations. The comprehensive traffic efficiency index is then numerically compared with a preset threshold, which reflects the acceptable level of road network efficiency. The comparison results trigger a conditional judgment mechanism for subsequent optimization operations.

[0044] When the overall traffic efficiency index exceeds a preset threshold, spatial regional positioning processing is performed. The spatial distribution areas of the travel point pairs contributing the most to the traffic efficiency index are identified, and the range of efficiency loss hotspots is determined through geospatial analysis. The regional positioning results output spatial coordinate boundaries, marking areas with concentrated road network efficiency deficiencies.

[0045] Based on the spatial distribution area of ​​the location, a road network topology feature analysis is performed. Topological parameters such as node connectivity, road hierarchy distribution, and loop closure status are extracted within the region. The feature analysis process identifies road network bottlenecks, including road segments with missing connectivity or unbalanced hierarchical configurations, and generates a structured diagnostic report.

[0046] Based on the analysis of road network topology characteristics, proposed road network optimization schemes are generated. For connectivity issues, it is recommended to add connecting road segments; for imbalanced road hierarchy configurations, it is recommended to adjust road hierarchy parameters. The proposed schemes include specific spatial locations and modification types, providing a quantitative basis for urban road network optimization decisions.

[0047] Specifically, in the method for calculating the traffic efficiency of urban road networks by spatial partitioning as described in this invention, step 7 further includes: Initialize the random number generator and assign a different random number seed for each simulation; Set the number of repeated simulations to be in the range of 5 to 10; For each simulation, an assigned random seed value is used to control the point of departure generation process, generating point of departure location data; Based on the generated point-of-department location data, point-of-department pair construction processing is performed to generate spatial straight-line path data; Based on the generated spatial straight path data, perform path efficiency calculation processing to generate path length efficiency index and path shape efficiency index; Based on the generated path length efficiency index and path shape efficiency index, perform index construction to generate a point-to-point traffic efficiency index. Collect all simulated point-pair traffic efficiency indices to form an index set, and perform average calculation based on the index set.

[0048] The Monte Carlo process generates diverse travel scenario data through multiple independent simulations, eliminating single-sample bias and providing a statistical basis for the comprehensive traffic efficiency index. The initialization of the random number generator creates a random number sequence generation mechanism, assigning a different random number seed value to each simulation. The random number seed value controls the initial state of subsequent random operations, ensuring that the random process of each simulation is independent and repeatable.

[0049] The number of simulation iterations is defined as between 5 and 10. This number balances computational efficiency with statistical representativeness, covers changes in travel demand under different population distributions, and provides a framework constraint for multiple simulations.

[0050] For each simulation, an assigned random seed value is used to control the point-of-entry (POI) generation process. The seed value drives the Monte Carlo random sampling process to generate POI location data within each partition. The POI location data reflects the spatial distribution of POIs based on the population weight allocation within each partition, and outputs a set of coordinate points.

[0051] Based on the generated point-of-entry location data, point-of-entry pair construction is performed. All point-of-entry points in all zones are merged and paired without repetition to generate spatial straight-line path data. This spatial straight-line path data represents the theoretically optimal movement path between point pairs, and the output is a vector line feature.

[0052] Based on the generated spatial straight-line path data, path efficiency calculation is performed. The shortest path between projected nodes is calculated by combining road network data, and path length efficiency and path shape efficiency indices are output. The path length efficiency index quantifies the actual path length redundancy, while the path shape efficiency index captures the degree of path morphology detour.

[0053] Based on the generated path length efficiency index and path shape efficiency index, an index construction operation is performed. The transformed value is multiplied by the index to generate the point-to-point traffic efficiency index. The point-to-point traffic efficiency index comprehensively reflects the network efficiency loss for a single trip, and the numerical result is output.

[0054] All simulated point-pair traffic efficiency indices are collected to form an index set, and an average value is calculated based on this index set. The index set integrates results from multiple simulations, and the average value is used to output a comprehensive traffic efficiency index. This comprehensive traffic efficiency index serves as the final evaluation indicator, characterizing the overall efficiency level of the urban road network.

[0055] Specifically, in the method for calculating the traffic efficiency of urban road networks by spatial partitioning as described in this invention, step 7 further includes: Based on partition boundary data, point-of-department location data, path data where the path shape efficiency index is below a preset threshold, and the comprehensive traffic efficiency index; Overlay a zone boundary layer on the electronic map base map, and then overlay point location distribution markers based on the overlaid zone boundary layer; Based on path data where the path shape efficiency index is below a preset threshold, a path efficiency heatmap is generated, producing a visualization result that includes a partition boundary layer, trip point location distribution markers, and the path efficiency heatmap.

[0056] Spatial visualization processing is performed to generate a visual representation of urban road network traffic efficiency based on multi-source data. First, the input dataset is processed, including zone boundary data, origin point location data, path shape efficiency indices below a preset threshold, and a comprehensive traffic efficiency index. Zone boundary data defines the spatial analysis unit scope, origin point location data represents the distribution of travel demand, inefficient path data identifies inefficient road segments, and the comprehensive traffic efficiency index reflects the overall road network performance. The integration of multi-source data provides fundamental geographic information and quantitative efficiency indicators for visualization.

[0057] A zone boundary layer is overlaid on the electronic map base map. This zone boundary layer is generated based on zone boundary data, and the layer overlay operation employs geographic information system spatial registration technology to achieve precise matching between the boundary geometry and the map coordinate system. The overlay result forms the spatial analysis baseline framework, with the zone boundary layer using polygonal features to identify the spatial distribution range of population cluster units.

[0058] Based on an overlaid partition boundary layer, trip point location distribution markers are superimposed. These markers are generated from trip point location data, and the marker rendering employs a dot-based symbolization method, using color or shape to distinguish the trip point density characteristics of different partitions. The marker overlay operation inherits the spatial reference system of the partition boundary layer, enabling the visual positioning of travel demand hotspot areas.

[0059] A path efficiency heatmap is generated based on path data where the path shape efficiency index is below a preset threshold. The heatmap generation process uses a kernel density estimation algorithm to convert inefficient path data into a continuous color gradient surface. The heatmap layer uses warm colors to represent high-detour areas and cool colors to represent low-detour areas. The color gradient is mapped to the path shape efficiency index value, intuitively reflecting the spatial differentiation characteristics of road network topological defects.

[0060] The system generates visualizations including zone boundary layers, point-of-department (POD) location markers, and route efficiency heatmaps. The result synthesis integrates these three visualization layers, with the layer stacking order following spatial hierarchy: the base map displays zone boundaries, the middle layer shows POD markers, and the top layer overlays the heatmap. The final output is a single map document or an interactive view, providing a comprehensive representation of the spatial pattern of urban road network efficiency.

[0061] This invention addresses the technical problem of inaccurate urban road network traffic efficiency assessment caused by neglecting population spatial distribution information and lacking effective simulation methods. It achieves this through the following core technical logic: First, it acquires urban area population raster data and road network data as input sources. The population raster data includes spatialized population distribution information, and the road network data provides the road network topology; together, they form the basis of the analysis. Spatial partitioning is then performed to generate a set of geographically continuous partitions with similar population density characteristics based on the population raster data. Specifically, this involves converting the population raster data into a two-dimensional matrix, standardizing it to eliminate dimensional differences, constructing a comprehensive distance index that integrates geographical distance and population size differences, and driving a spatial clustering algorithm to output the partition set. The partition set accurately matches the spatial distribution characteristics of the population, enabling travel demand allocation to reflect actual population aggregation patterns and eliminating demand bias caused by fixed partitions.

[0062] Secondly, the Monte Carlo method addresses the shortcomings of stochastic simulation. The trip allocation process calculates the population weight of each partition based on a set of partitions, allocates the total number of trips to each partition according to the weight, and generates trip point allocation values. The trip point generation process uses the Monte Carlo method to randomly generate trip point locations within each partition corresponding to the allocated number. The random process is controlled by independent seed values, resulting in a differentiated spatial distribution of trip points in each simulation, covering diverse travel scenarios in densely populated and sparsely populated areas. This method generates representative travel paths through multiple independent samplings, overcoming the shortcomings of existing methods that rely on fixed algorithms and ignore probability sampling, thus eliminating sampling bias.

[0063] The path efficiency calculation and processing integrates a two-dimensional evaluation mechanism. Based on origin point location data, non-repeating origin point pairs are constructed to generate spatial straight-line path vector data representing the theoretically optimal path. The nearest projected node in the road network is found, the shortest path between nodes is calculated, and origin points are connected to form complete actual path vector data. The ratio of the spatial straight-line path length to the actual path length is calculated as a path length efficiency index to quantify length redundancy; the difference in curve shape between the spatial straight-line path and the shortest path is calculated as a path shape efficiency index to capture the degree of path detour. These two indicators work together to construct a point-pair traffic efficiency index, comprehensively reflecting the defects in the road network topology.

[0064] Efficiency assessment improves reliability through multiple simulations and averaging. Spatial partitioning is repeatedly performed until efficiency assessment, with each iteration regenerating the partition set and trip point distribution. A Monte Carlo process initializes the random number generator, setting the number of simulations to 5-10, with each simulation using an independent seed value to control the data generation chain. Point-pair traffic efficiency indices from multiple simulations are collected to form an index set, and the average is taken to output the comprehensive traffic efficiency index. This comprehensive traffic efficiency index, negatively correlated with traffic efficiency, integrates population distribution weights and path efficiency dimensions to eliminate the impact of random fluctuations.

[0065] The technical solution forms a closed-loop processing chain: the population spatial partition accurately quantifies the demand distribution, the Monte Carlo simulation covers diverse travel scenarios, the dual-path efficiency indicators are collaboratively evaluated, and multiple averaging operations improve statistical reliability. Finally, it solves the problem of inaccurate evaluation caused by ignoring population distribution and simulation defects, providing an objective basis for urban road network optimization.

[0066] The specific implementation of the present invention is as follows: The following is the part of population spatial partition and travel quantity allocation: Collect the population grid data of the urban area, and process the data into two-dimensional matrix data. This data includes three columns, namely: X - longitude (after projection), Y - latitude (after projection), and P - (population). The number of rows of this data is the number of pixels in the population grid data. Then, perform z-score standardization on this data. The standardization calculation formula is as follows: ; ; ; Among them, represents the i-th (0 < i ≤ n) value in any column of this data, and n represents the total number of rows of this data, that is, the number of pixels in the population grid data.

[0067] Construct the geographical spatial distance G and the attribute spatial distance F, and based on this, construct the comprehensive distance calculation index D to achieve the comprehensive measurement of the similarity between different pixel samples. The calculation formulas of these three indexes are as follows: ; ; ; Among them, represents the geographical spatial distance between pixel i and pixel j, represents the geographical spatial distance between pixel i and pixel j, represents the geographical spatial distance between pixel i and pixel j, and represent the longitude (after projection) of pixel i and pixel j, and represent the latitude (after projection) of pixel i and pixel j, and represent the total population within the range of pixel i and pixel j.

[0068] Indicator D can simultaneously reflect the distance between pixel samples in geographic spatial distribution and the differences in population values. Therefore, we use indicator D as the distance calculation indicator, the above XYP data as the dataset, and specify the number of categories k. We apply it to the existing K-means clustering algorithm to realize population spatial partitioning based on raster data and obtain k spatially continuous population partitions.

[0069] Let M be the total number of trips within the city area. Then, calculate the total population of different zones. Using the population proportion of each zone as a weight, allocate the set total number of trips within the city area to the different zones. The calculation process is as follows: ; ; in, This represents the population weight of partition k. This represents the population size corresponding to cell j within partition k. This represents the number of trips within partition k.

[0070] The following is the travel route simulation part based on the spatial Monte Carlo algorithm: Using the Monte Carlo algorithm, the locations of the rows are randomly generated within each partition. The number of locations to be generated is equal to the number of occurrences in each partition. .

[0071] Merge the travel point locations from all partitions to obtain a travel point dataset of total size m. Then, perform pairwise pairwise combinations of the point data to obtain unique travel point pairs, with n pairs in total. .

[0072] Based on the coordinates of the two points in each pair, generate a spatial line connecting the two points and save the spatial line path as vector data. .

[0073] Each pair of travel points is overlaid onto the road network. First, for any pair of travel points (o, p), the points on the road network closest to points o and p respectively are found, denoted as or and dr. Then, Dijkstra's algorithm is used to find the shortest path between or and dr in the road network. Finally, or is connected to point o in this path, and dr is connected to point d in this path. The expanded shortest path of the road network is then saved as vector data. .

[0074] The following is the calculation part of the traffic efficiency index based on path length and path shape: Based on the spatial connection path vector data between each pair of travel points, the length of the spatial connection path between travel point pairs (o, p) is calculated. Based on the shortest path vector data of the road network between each pair of travel points, calculate the total length of the shortest path for the travel point pair (o, p). .

[0075] The traffic efficiency metric based on path length is calculated using the following formula: ; The range of values ​​for this indicator is as follows: The closer a value is to 1, the higher the traffic efficiency of the road network.

[0076] Based on the spatial connection path vector line feature data and the road network shortest path vector line feature data for each point pair, a traffic efficiency measure index based on path shape is calculated, specifically using Frechet distance to represent shape similarity.

[0077] ; Here, Frechet represents the Frechet distance calculation function between two line features. This shape metric has no fixed range of values; a larger value indicates a lower similarity between the shortest path and the spatial connection, and thus a lower traffic efficiency of the road network.

[0078] The following is the construction part of the traffic efficiency index that integrates length and shape efficiency indicators: Traffic efficiency metrics based on path length for each trip point pair (o, p) Traffic efficiency measures based on path shape The travel point-to-point traffic efficiency index is constructed, and the calculation formula is as follows: ; in, This represents the traffic efficiency index between trip point pairs (o, p). The smaller the value, the higher the similarity between the shortest path and the spatial connection. The smaller the value, the higher the shape similarity between the shortest path and the spatial connection. Therefore, The smaller the value, the higher the traffic efficiency between the travel point pairs (o, p).

[0079] For the point-pair traffic efficiency index obtained from n point pairs, calculate the average of all point-pair efficiency indices to obtain the traffic efficiency index T corresponding to a single Monte Carlo simulation.

[0080] The following is the part showing the average and overall traffic efficiency index generated from multiple Monte Carlo simulations: By changing the random number generation seed, the Monte Carlo method is run S times to simulate and generate traffic departure points for different zones. Based on the generated departure points, the above steps of spatial connection generation, shortest path search, traffic efficiency metric calculation based on path length, and traffic efficiency metric calculation based on path shape are repeated to calculate the traffic efficiency index corresponding to each Monte Carlo simulation.

[0081] The average traffic efficiency index generated by S Monte Carlo simulations is used to obtain the comprehensive traffic efficiency index of the urban road network. The calculation formula is as follows: ; The smaller the value of the comprehensive traffic efficiency index, the higher the traffic efficiency of the traffic network; the larger the value, the lower the efficiency of the traffic network.

[0082] The road network and population raster data at a 1km scale in the main urban area of ​​Lanzhou were selected as experimental subjects to calculate the traffic efficiency index of the Lanzhou road network. In the experiment, the spatial distribution of the road network and population data is as follows: Figure 2 As shown, the number of population spatial partitions is set to k=5, and the spatial partitions of the population raster are as follows: Figure 3 As shown in the figure. Based on this, the number of Monte Carlo simulations, S, is set to be 5, and the total number of departure points in the region is set to 30. The spatial distribution map of departure point distribution, departure point connections, and shortest paths between departure points in a single Monte Carlo simulation is shown in the figure. Figure 4 As shown. The traffic efficiency index and overall traffic efficiency index obtained from the last 5 Monte Carlo simulations are as follows. Figure 5 As shown.

Claims

1. A method for calculating the traffic efficiency of an urban road network by spatial partitioning, characterized in that, include: Step 1: Obtain population raster data and road network data for the urban area; Step 2: Based on the population raster data from Step 1, the population raster data is converted into a two-dimensional matrix including longitude, latitude, and population size. The matrix data is standardized, and a spatial clustering algorithm is executed using a comprehensive distance index that integrates geographical distance and population size differences to generate multiple partitions. Step 3: Based on the set of partitions generated in Step 2, perform trip allocation processing, calculate the population weight of each partition, and allocate the set total trips to each partition according to the weights. Step 4: Based on the allocation results in Step 3, perform the point of departure generation process, and randomly generate point of departure locations corresponding to the allocated number within each partition using the Monte Carlo method. Step 5: Based on the travel point locations generated in Step 4, perform travel point pair construction processing, merge all partition travel point locations, combine non-repeating travel point pairs, and generate spatial straight-line path vector data for each point pair; Step 6: Based on the spatial straight path vector data from Step 5 and the road network data from Step 1, perform path efficiency calculation processing, find the nearest projection node of each travel point on the road network, calculate the shortest path between projection nodes, connect the travel point with the corresponding projection node to form complete shortest path vector data, calculate the ratio of the spatial straight path length to the shortest path length as the path length efficiency index, and calculate the curve shape difference between the spatial straight path and the shortest path as the path shape efficiency index. Step 7: Based on the path length efficiency index and path shape efficiency index from Step 6, perform efficiency evaluation processing, construct a point-to-point traffic efficiency index, and repeat Steps 2 to 7. Each time Step 2 is executed, a new partition set is generated. The average value of the point-to-point traffic efficiency indices generated by multiple simulations is taken to obtain the comprehensive traffic efficiency index. The comprehensive traffic efficiency index is output as an indicator negatively correlated with traffic efficiency.

2. The method for calculating the traffic efficiency of urban road networks by spatial partitioning according to claim 1, characterized in that, Step 2 includes: Based on population raster data, the population raster data is converted into a two-dimensional matrix including longitude, latitude, and population size; Based on the transformed two-dimensional matrix, the longitude, latitude, and population values ​​are standardized to generate a standardized matrix. Based on the longitude, latitude, and population data in the generated standardized matrix, a comprehensive distance index is constructed that integrates geographical distance and population differences. Geographical distance is calculated using longitude and latitude differences, while population differences are calculated using population value differences. The constructed comprehensive distance index is then used to execute a spatial clustering algorithm to generate a set of partitions.

3. The method for calculating the traffic efficiency of urban road networks by spatial partitioning according to claim 2, characterized in that, Step 2 also includes: Based on the standardized matrix, the longitude difference, latitude difference, and population difference are calculated for two pixel samples. Based on the calculated longitude and latitude differences, the geographic spatial distance is calculated as the square root of the sum of the squares of the longitude and latitude differences. Based on the calculated population difference, the attribute spatial distance is calculated as the absolute value of the population difference. The sum of one-third of the geographic spatial distance and two-thirds of the attribute spatial distance is used as the comprehensive distance index between two pixels.

4. The method for calculating the traffic efficiency of urban road networks by spatial partitioning according to claim 3, characterized in that, Step 3 includes: Based on the set of districts and their total population data, calculate the weight ratio of the total population of each district to the total population of the entire region; Based on the calculated weight ratio, the total number of trips is multiplied by the weight ratio to generate the allocation value of the number of trip points in each zone.

5. The method for calculating the traffic efficiency of urban road networks by spatial partitioning according to claim 4, characterized in that, Step 6 includes: Based on the location of the travel point and road network data, for the travel point in the travel point pair, find the node in the road network that is closest to it as the projection node; Based on the found projection nodes, a path search algorithm is used to calculate the shortest connected path between the projection nodes; based on the shortest connected path, the departure point is connected to the projection node, and a complete vector path is generated by combining the paths between the nodes.

6. The method for calculating the traffic efficiency of urban road networks by spatial partitioning according to claim 5, characterized in that, Step 6 also includes: Based on spatial straight-line path vector data and complete shortest path vector data, the path vector line elements are discretized and sampled to generate a discrete point sequence. Based on the generated discrete point sequence, the distance matrix of all point pairs between the discrete point sequences is calculated, and the maximum and minimum distance values ​​in the distance matrix are determined as the path shape efficiency index.

7. The method for calculating the traffic efficiency of urban road networks by spatial partitioning according to claim 6, characterized in that, Step 7 includes: Based on the path length efficiency index, generate the converted value of the path length efficiency index; Based on the generated conversion value and the path shape efficiency index, the conversion value is multiplied by the path shape efficiency index to generate the traffic efficiency index of the trip point pair.

8. The method for calculating the traffic efficiency of urban road networks by spatial partitioning according to claim 7, characterized in that, Step 7 also includes: Based on the comprehensive traffic efficiency index, the comprehensive traffic efficiency index is compared with a preset threshold; When the comprehensive traffic efficiency index exceeds the threshold, locate the distribution area of ​​the trip point pairs with the highest comprehensive traffic efficiency index. Based on the location distribution area, the topological characteristics of the road network are analyzed; Based on the analysis results of the road network topology characteristics, suggestions are generated for adding new connecting roads or adjusting the road grade.

9. The method for calculating the traffic efficiency of a spatially partitioned urban road network according to claim 8, characterized in that, Step 7 also includes: Initialize the random number generator and assign a different random number seed for each simulation; Set the number of repeated simulations to be in the range of 5 to 10; For each simulation, an assigned random seed value is used to control the point of departure generation process, generating point of departure location data; Based on the generated point-of-department location data, point-of-department pair construction processing is performed to generate spatial straight-line path data; Based on the generated spatial straight path data, perform path efficiency calculation processing to generate path length efficiency index and path shape efficiency index; Based on the generated path length efficiency index and path shape efficiency index, perform index construction to generate a point-to-point traffic efficiency index. Collect all simulated point-pair traffic efficiency indices to form an index set, and perform average calculation based on the index set.

10. The method for calculating the traffic efficiency of a spatially partitioned urban road network according to claim 9, characterized in that, Step 7 also includes: Based on partition boundary data, point-of-department location data, path data where the path shape efficiency index is below a preset threshold, and the comprehensive traffic efficiency index; Overlay a zone boundary layer on the electronic map base map, and then overlay point location distribution markers based on the overlaid zone boundary layer; Based on path data where the path shape efficiency index is below a preset threshold, a path efficiency heatmap is generated, producing a visualization result that includes a partition boundary layer, trip point location distribution markers, and the path efficiency heatmap.

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