Urban space structure and traffic jam relation measurement method based on continuous coherence

Abstract

The embodiment of the invention provides an urban space structure and traffic jam relation measurement method based on continuous coherence, and belongs to the technical field of traffic control, and the method specifically comprises the steps: 1, matching target data of a target region with a road network, and recognizing urban space structure features and traffic jam features; 2, substituting the urban spatial structure characteristics and the traffic jam characteristics into a multi-scale geographic weighted regression model for regression analysis, and generating an analysis result; and step 3, according to an analysis result and a continuous coherence method, measuring a relationship between the urban space structure and traffic congestion. According to the scheme of the invention, the coupling property and topological property that multiple urban spatial structure features interact in a real environment are considered, and the relation between the multiple urban spatial structure features and traffic congestion is calculated by using the multi-scale geographical weighted regression. A continuous coherence method is applied to measure the relation between the urban space structure characteristics and the traffic jam characteristics, and higher reality and interpretability are achieved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of traffic control technology, and in particular to a method for measuring the relationship between urban spatial structure and traffic congestion based on continuous coherence. Background Art

[0002] At present, with the continuous development of urban space, people's travel needs are also increasing, resulting in increasingly serious urban traffic congestion. Reasonable understanding of the relationship between urban spatial structure and traffic congestion is the key to solving the problem of traffic congestion.

[0003] There are also two main evaluation methods for evaluating the relationship between urban spatial structure and traffic congestion, namely, relationship reliability evaluation and relationship spatial distribution evaluation. Relationship reliability evaluation mainly uses methods such as model fit test and significance test to evaluate the reliability of urban spatial structure in explaining traffic congestion. This evaluation method is limited to the current research area and can only test a single feature, so it has limitations in the generalization and mobility of the analysis results. Relationship spatial distribution evaluation mainly displays the non-stationary characteristics of spatial relationships by visualizing the coefficients of influencing factors and interprets them in combination with geographical meanings. This method has certain advantages in displaying spatial relationships, but this method mainly focuses on the visualization analysis of a single feature and ignores the complexity of the interaction between multiple elements in the traffic system. In addition, existing methods lack the ability to quantitatively analyze these interactions and cannot fully capture the spatial distribution characteristics of traffic flow.

[0004] It can be seen that there is an urgent need for a measurement method of the relationship between urban spatial structure and traffic congestion based on continuous coherence that is highly interpretable and adaptable. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a method for measuring the relationship between urban spatial structure and traffic congestion based on continuous coherence, which at least partially solves the problems of poor interpretability and adaptability in the prior art.

[0006] The embodiment of the present invention provides a method for measuring the relationship between urban spatial structure and traffic congestion based on continuous coherence, including: Step 1: Match the target data of the target area with the road network and identify the urban spatial structure characteristics and traffic congestion characteristics; Step 2: Substitute the urban spatial structure characteristics and traffic congestion characteristics into the multi-scale geographically weighted regression model for regression analysis to generate analysis results; Step 3: Measure the relationship between urban spatial structure and traffic congestion based on the analysis results and the continuous homology method.

[0007] According to a specific implementation of the embodiment of the present invention, step 1 specifically includes: Step 1.1, perform topological check and vectorization on the road network, process the double-lane road into a single-lane road, determine the buffer radius accordingly, generate the buffer of the road network as the research unit, clean the POI data in the target data and match it with the research unit, and, based on the road network geometry, topological information and vehicle speed constraints, use the preset map matching algorithm to match the vehicle trajectory in the target data with the research unit; Step 1.2, extract the physical attributes and social attributes of urban structural characteristics according to the target data matched with the research unit to form the urban spatial structural characteristics; Step 1.3, calculate the average congestion index of each road in the study unit to form the traffic congestion characteristics.

[0008] According to a specific implementation of the embodiment of the present invention, step 2 specifically includes: The urban spatial structure characteristics are taken as the independent variable X, and the average congestion index is taken as the dependent variable Y. The multi-scale geographically weighted regression model is used to conduct a multi-scale geographically weighted regression analysis on the urban spatial structure characteristics and traffic congestion characteristics to generate analysis results.

[0009] According to a specific implementation of an embodiment of the present invention, the expression of the multi-scale geographically weighted regression model is: ; in, It is The dependent variable for each observation point is It is The first observation point independent variables, It is The spatial coordinates of the observation positions, is in position The intercept term at is in position Independent variable The regression coefficients of depend on the spatial coordinates, is the error term; ; in, It is The spatial bandwidth parameter of the independent variable, It's location and location The spatial weight between The spatial bandwidth parameter of the independent variable It is calculated that It is The first position independent variable values, It is The value of the dependent variable at each location.

[0010] According to a specific implementation of the embodiment of the present invention, step 3 specifically includes: Step 3.1, extract the regression coefficient of the independent variable in the analysis results as the high-dimensional coordinates of each road, and construct a VR complex for this set of point cloud data; Step 3.2, draw the topological feature duration graphs of different dimensions according to the VR complex, and calculate the duration of all two-dimensional features accordingly; Step 3.3, sort the two-dimensional features in descending order according to the duration, select a preset number of two-dimensional features from top to bottom, extract the road group corresponding to each two-dimensional feature, and output the spatial distribution result corresponding to each road group.

[0011] The urban spatial structure and traffic congestion relationship measurement scheme based on continuous homology in the embodiment of the present invention includes: step 1, matching the target data of the target area with the road network, and identifying the urban spatial structure characteristics and traffic congestion characteristics; step 2, substituting the urban spatial structure characteristics and traffic congestion characteristics into a multi-scale geographically weighted regression model for regression analysis to generate analysis results; step 3, measuring the relationship between urban spatial structure and traffic congestion based on the analysis results and the continuous homology method.

[0012] The beneficial effects of the embodiments of the present invention are as follows: through the scheme of the present invention, considering that multiple urban spatial structure characteristics in a real environment will interact with each other to jointly cause traffic congestion and the coupling and topological properties of the urban spatial structure, the relationship between multiple urban spatial structure characteristics and traffic congestion is calculated using multi-scale geographically weighted regression, and the method of continuous homology is innovatively applied to measure the relationship between urban spatial structure characteristics and traffic congestion characteristics. A method for measuring the relationship between urban spatial structure and traffic congestion based on continuous homology is developed, which has stronger reality and interpretability. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1 A flow chart of a method for measuring the relationship between urban spatial structure and traffic congestion based on continuous coherence provided by an embodiment of the present invention; Figure 2 A schematic diagram of a specific implementation process of a method for measuring the relationship between urban spatial structure and traffic congestion based on continuous coherence provided by an embodiment of the present invention; Figure 3 A schematic diagram of a partial taxi trajectory and a research area provided by an embodiment of the present invention; Figure 4 A schematic diagram of a two-dimensional characteristic duration barcode with the longest duration provided by an embodiment of the present invention; Figure 5 A schematic diagram of the spatial distribution results of a road group provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0016] The following describes the embodiments of the present invention through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0017] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present invention, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this device and / or practice this method.

[0018] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention. The drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0019] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0020] With the continuous development of urban space, people's travel needs are also increasing, resulting in increasingly serious urban traffic congestion. Reasonable understanding of the relationship between urban spatial structure and traffic congestion is the key to solving the problem of traffic congestion.

[0021] Traditional research believes that there are two main types of relationships between urban spatial structure and traffic congestion, namely spatial homogeneity and spatial heterogeneity. The former refers to the fact that the relationship between urban spatial structure characteristics and traffic congestion at different locations in the study area is the same, mainly including correlation analysis, multivariate linear regression analysis, and land use-traffic models. The characteristics of these models are that their regression coefficient estimates represent the global average, and they are usually analyzed from a macro perspective. The latter refers to the fact that the relationship between urban spatial structure and traffic congestion at different locations in the study area varies with location. Typical models for studying spatial heterogeneity include local weighted regression, spatial variable parameter regression, and geographically weighted regression models. They take into account the locality and heterogeneity of spatial data and can more accurately portray the complex relationships within the urban space.

[0022] There are also two main evaluation methods for evaluating the relationship between urban spatial structure and traffic congestion, namely, relationship reliability evaluation and relationship spatial distribution evaluation. Relationship reliability evaluation mainly uses methods such as model fit test and significance test to evaluate the reliability of urban spatial structure in explaining traffic congestion. This evaluation method is limited to the current research area and can only test a single feature, so it has limitations in the generalization and mobility of the analysis results. Relationship spatial distribution evaluation mainly displays the non-stationary characteristics of spatial relationships by visualizing the coefficients of influencing factors and interprets them in combination with geographical meanings. This method has certain advantages in displaying spatial relationships, but this method mainly focuses on the visualization analysis of a single feature and ignores the complexity of the interaction between multiple elements in the traffic system. In addition, existing methods lack the ability to quantitatively analyze these interactions and cannot fully capture the spatial distribution characteristics of traffic flow.

[0023] It can be seen that the shortcomings of the prior art are: (1) Traditional relationship evaluation research mainly focuses on relationship reliability assessment and qualitative evaluation of relationship spatial distribution. It has limitations in the generalization and transferability of analysis results, or only focuses on the visualization analysis of a single feature, ignoring the complexity of the interaction between multiple elements in the transportation system.

[0024] (2) In traditional research on the relationship between urban spatial structure and traffic congestion, the impact of spatial structure on traffic congestion is evaluated only from a single dimension, namely physical distance, without taking into account other attributes of the urban spatial structure, such as the topology.

[0025] An embodiment of the present invention provides a method for measuring the relationship between urban spatial structure and traffic congestion based on continuous coherence, and the method can be applied to traffic control processes in urban management scenarios.

[0026] See also Figure 1 , is a flow chart of a method for measuring the relationship between urban spatial structure and traffic congestion based on continuous coherence provided by an embodiment of the present invention. Figure 1 and Figure 2 As shown, the method mainly comprises the following steps: Step 1: Match the target data of the target area with the road network and identify the urban spatial structure characteristics and traffic congestion characteristics; Furthermore, the step 1 specifically includes: Step 1.1, perform topological check and vectorization on the road network, process the double-lane road into a single-lane road, determine the buffer radius accordingly, generate the buffer of the road network as the research unit, clean the POI data in the target data and match it with the research unit, and, based on the road network geometry, topological information and vehicle speed constraints, use the preset map matching algorithm to match the vehicle trajectory in the target data with the research unit; Step 1.2, extract the physical attributes and social attributes of urban structural characteristics according to the target data matched with the research unit to form the urban spatial structural characteristics; Step 1.3, calculate the average congestion index of each road in the study unit to form the traffic congestion characteristics.

[0027] In the specific implementation, the data used is cleaned and matched with the road network, and the characteristics of urban spatial structure and traffic congestion are identified. Specifically, it includes: 1.1 Data processing and road network matching First, the road network is topologically checked and vectorized, and the double-line roads are processed as single-line roads. On this basis, the buffer radius is determined to generate the buffer zone of the road network as the research unit. Secondly, the classification errors in the POI data caused by individual word meaning analysis during the data crawling process are solved, and the POI types that have almost no impact on the research, such as "maintenance department" and "agent" data, are deleted. Then, the coordinate correction and projection conversion processing are performed on the remaining data, and the POI data are divided into six categories: public services, commercial services, offices, and accommodation services. Next, the data with an average price of less than 500 yuan per square meter and other data anomalies in the collected second-hand residential data are removed. Finally, the trajectory data outside the research area, with time anomalies and repeated records are deleted, and then the points where the front and rear coordinates of the same vehicle drift or the state is abnormal are eliminated.

[0028] Taking into account the road network geometry, topology information and vehicle speed constraints, a map matching algorithm for low sampling rate trajectory points - ST-Matching is used to match vehicle trajectories with urban road networks.

[0029] 1.2 Calculation of urban spatial structure characteristic indicators The physical attributes of urban structural characteristics are the external manifestations of urban spatial structure. Urban scale, spatial pattern, land use structure and urban density are selected as the external characteristics of urban structure, and built-up area, central vitality, land use mix and floor area ratio are used as indicators of these characteristics. The social attributes of urban structural characteristics are the internal connections of urban spatial structure. The distribution of economic development level and population space distribution are selected as the external characteristics of urban structure, and the two indicators of housing price distribution and population density distribution are used to characterize the internal connections of cities.

[0030] 1.3 Calculation of traffic congestion characteristic indicators The traffic congestion characteristics of the study unit are characterized by the average congestion index of the road. Given a directed road, the traffic congestion index in time period t is: ; In the formula For vehicles exist The actual average speed at the time, for The 85% quantile after ascending order within a working day. Calculate the average of all unique trajectory points on the directional road every hour as the average driving speed , to ensure the stability of road speed. The calculation requires at least 5 vehicles to pass through the road section every hour. The calculation requires at least 8 hours per day The average congestion index is calculated as the average of the congestion indexes of the road section at all times. The higher the value, the more serious the congestion.

[0031] Step 2: Substitute the urban spatial structure characteristics and traffic congestion characteristics into the multi-scale geographically weighted regression model for regression analysis to generate analysis results; Based on the above embodiment, step 2 specifically includes: The urban spatial structure characteristics are taken as the independent variable X, and the average congestion index is taken as the dependent variable Y. The multi-scale geographically weighted regression model is used to conduct a multi-scale geographically weighted regression analysis on the urban spatial structure characteristics and traffic congestion characteristics to generate analysis results.

[0032] Furthermore, the expression of the multi-scale geographically weighted regression model is: ; in, It is The dependent variable for each observation point is It is The first observation point independent variables, It is The spatial coordinates of the observation positions, is in position The intercept term at is in position Independent variable The regression coefficients of depend on the spatial coordinates, is the error term; ; in, It is The spatial bandwidth parameter of the independent variable, It's location and location The spatial weight between The spatial bandwidth parameter of the independent variable It is calculated that It is The first position independent variable values, It is The value of the dependent variable at each location.

[0033] In the specific implementation, the urban spatial structure characteristics and traffic congestion characteristics are substituted into the multi-scale geographically weighted regression model for regression analysis, where the urban spatial structure characteristics are the independent variable X and the average congestion index is the dependent variable Y.

[0034] The multiscale geographically weighted regression model is an extended version of the geographically weighted regression model, which can more finely capture the characteristics of different explanatory variables that may play a role at different spatial scales. The formula of the multiscale geographically weighted regression model is: ; In the formula, It is The dependent variable for each observation point is It is The first observation point independent variables, It is The spatial coordinates of the observation positions, is in position The intercept term at is in position Place, independent variable The regression coefficients of depend on the spatial coordinates, is the error term.

[0035] Different from the traditional geographically weighted regression model, each regression coefficient in the multi-scale geographically weighted regression model Corresponding to different bandwidths , that is, the regression coefficient of each independent variable can have different smoothing scales in space: ; In the formula, It is The spatial bandwidth parameter of the independent variable, It's location and location The spatial weight between The spatial bandwidth parameter of the independent variable It is calculated that It is The first position independent variable values, It is The value of the dependent variable at each location.

[0036] Step 3: Measure the relationship between urban spatial structure and traffic congestion based on the analysis results and the continuous homology method.

[0037] Based on the above embodiment, step 3 specifically includes: Step 3.1, extract the regression coefficient of the independent variable in the analysis results as the high-dimensional coordinates of each road, and construct a VR complex for this set of point cloud data; Step 3.2, draw the topological feature duration graphs of different dimensions according to the VR complex, and calculate the duration of all two-dimensional features accordingly; Step 3.3, sort the two-dimensional features in descending order according to the duration, select a preset number of two-dimensional features from top to bottom, extract the road group corresponding to each two-dimensional feature, and output the spatial distribution result corresponding to each road group.

[0038] In specific implementation, the process of detecting the relationship between urban spatial structure and traffic congestion is as follows: 3.1 Building a topology map Extract the regression coefficient of the independent variable in the multi-scale geographical weighted regression analysis results in step 2 as the high-dimensional coordinates of each road, that is, treat each research unit as a six-dimensional point. Construct a VR complex for this set of point cloud data, where the nodes represent the relationship between spatial structural characteristics and traffic congestion, and the edges represent the relationship distance between them. Given a distance threshold parameter, generate a set of increasing distance parameters with inclusion relationships , constructs a series of simplicial complexes with inclusion relations. As the distance parameter increases, the original simplicial complex will not be affected, but will be included in the new simplicial complex formed by the larger distance parameter. Assuming that the distance parameter The simplicial complex generated by , the generated simplicial complex sequence is shown as follows: ; In the formula, is the point cloud data set, is the increasing distance parameter, For the distance parameter The simplicial complex generated by Indicates that the previous simplicial complex is contained in the next simplicial complex.

[0039] 3.2 Calculating feature duration In the process of constructing the filter nested complex, as the distance parameter increases, the topological features will experience the process of "birth" and "death". can define its life cycle. First appearance Under this distance parameter, If the topological feature disappears at this distance parameter, it is said that the topological feature is "born" at the former and "dies" at the latter, and the duration is Also called the life cycle of the feature, i.e. duration. Calculate the duration of all features.

[0040] 3.3 Extracting long-lasting features corresponding to road groups In the process of continuous homology, features with longer duration mean that there is a stable topological structure between the elements that make up this feature, which requires special attention; while topological features with shorter duration are usually regarded as noise and can be ignored. The process of continuous homology can extract the topological feature information of the data without changing the original data structure, that is, it can extract the topological features of the data without reducing the dimension, and reveal its life cycle by describing the birth and death of the topological features.

[0041] Sort the two-dimensional features in descending order of duration, select three features from top to bottom, and extract their corresponding roads. The duration of the continuous homophonic two-dimensional features of these three groups of roads is the longest among all the two-dimensional features, so their internal structure is stable.

[0042] The method for measuring the relationship between urban spatial structure and traffic congestion based on continuous coherence provided in this embodiment takes into account that multiple urban spatial structure characteristics in a real environment will interact with each other to jointly cause traffic congestion and the coupling and topological properties of the urban spatial structure, calculates the relationship between multiple urban spatial structure characteristics and traffic congestion using multi-scale geographically weighted regression, and innovatively applies the continuous coherence method to measure the relationship between urban spatial structure characteristics and traffic congestion characteristics, thereby developing a method for measuring the relationship between urban spatial structure and traffic congestion based on continuous coherence, which is more realistic and interpretable.

[0043] The method of the present invention will be further described below through a specific embodiment, using the 2019 road data, building outline data, second-hand housing price data, POI data, population density spatial distribution data and taxi trajectory data from September 23, 2019 to September 29, 2019 in District B of City A to illustrate the specific implementation process of the present invention: (1) In the embodiment, District B of City A is selected as the research area, and the data used are taxi trajectory data, road data, building outline data, second-hand housing price data, POI data and population density spatial distribution data. The time of road data, building outline data, second-hand housing price data, POI data and population density spatial distribution data is 2019, and the time of taxi trajectory data is from 6:00 to 23:59 every day from September 23, 2019 to September 29, 2019, with a sampling interval of 15 seconds. Some trajectory data are related to the research area as shown in Figure 2. Figure 3 shown.

[0044] (2) The road network in Area B is processed into a single line. On this basis, a buffer zone with a radius of 120 meters is generated as the research unit. The misclassified data in the POI data is modified and invalid data is eliminated. The data with an average price of less than 500 yuan per square meter and other data anomalies in the collected second-hand residential data are removed. The remaining POI data and second-hand residential data are processed by coordinate correction and projection transformation. The POI data is deleted. Trajectory data outside the study area, with time anomalies and repeated records are deleted. Points where the front and rear coordinates of the same vehicle drift or have abnormal states are eliminated. ST-Matching is used to match vehicle trajectories with urban road networks.

[0045] (3) Calculate the urban spatial structure characteristics and traffic congestion characteristics of Area B. The spatial structure characteristics include urban scale characteristics, spatial pattern characteristics, land use structure characteristics, urban density characteristics, urban economic level distribution characteristics, and urban population spatial distribution characteristics. The corresponding indicators are the area of ​​the research unit, the central vitality value of the research unit, the land use mix, the floor area ratio, the average price of second-hand housing, and the average population density; the corresponding indicator for traffic congestion is the average congestion index of the research unit.

[0046] Directly calculate the area of ​​each research unit to obtain the scale characteristics. Perform kernel density analysis on the six types of POI data, normalize them, and then perform weighted superposition to calculate the mean of each research unit to obtain the spatial pattern characteristics. Count the number of six types of POIs in each research unit, and use the Shannon entropy calculation method to express the land use mixture. The calculation formula is as follows: ; In the formula, represents the value of Shannon entropy, Refers to the different elements within the unit of study, Refers to the types of elements within the research unit. Refers to the research unit Elements account for all study units The proportion of the total amount of factors.

[0047] The calculation method of volume ratio is as follows: ; In the formula, Refers to the volume ratio, Refers to the total number of buildings in the research unit, Refers to a building within a research unit. The area of ​​the building. Refers to the number of floors of the building.

[0048] The ordinary kriging difference method is used to make a surface prediction of second-hand housing prices, and the mean of the interpolation results in each research unit is calculated as the economic level distribution of the research unit. The mean population density in each research unit is calculated as the population spatial distribution of the research unit.

[0049] Given a directed road, the traffic congestion index at time t is: ; In the formula For vehicles exist The actual average speed at the time, for The 85% percentile in ascending order within a working day. The average congestion index is calculated as the average of the congestion indexes of the road section at all time periods.

[0050] (4) The urban spatial structure characteristics are taken as the independent variable X and the average congestion index is taken as the dependent variable Y. The multi-scale geographically weighted regression model is used to conduct a multi-scale geographically weighted regression analysis on the urban spatial structure characteristics and traffic congestion characteristics.

[0051] (5) The regression coefficients of the independent variables in the multi-scale geographically weighted regression analysis results are extracted as the high-dimensional coordinates of each road. A VR complex is constructed for this set of point cloud data, in which the nodes represent the relationship between spatial structural characteristics and traffic congestion, and the edges represent the relationship distance between them.

[0052] (6) Draw the duration graph of topological features of different dimensions, calculate the duration of all two-dimensional features, and arrange them in descending order of duration. The duration barcode of the two-dimensional feature with the longest duration is as follows: Figure 4 shown.

[0053] (7) Select three features from top to bottom and extract their corresponding road groups. The spatial distribution results of the three road groups are as follows: Figure 5 The characteristics of urban spatial structure and traffic congestion calculated in (3) are visualized on the spatial distribution of the three road groups. It is found that the traffic congestion index of the three road groups is relatively high, the average area and housing price is high, and the average center vitality, land utilization rate, volume ratio and population density are relatively small.

[0054] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof.

[0055] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for measuring the relationship between urban spatial structure and traffic congestion based on continuous coherence, characterized in that: include: Step 1: Match the target data of the target area with the road network and identify the urban spatial structure characteristics and traffic congestion characteristics; Step 2: Substitute the urban spatial structure characteristics and traffic congestion characteristics into the multi-scale geographically weighted regression model for regression analysis to generate analysis results; Step 3: Measure the relationship between urban spatial structure and traffic congestion based on the analysis results and the continuous homology method.

2. The method according to claim 1, characterized in that The step 1 specifically includes: Step 1.1, perform topological check and vectorization on the road network, process the double-lane road into a single-lane road, determine the buffer radius accordingly, generate the buffer of the road network as the research unit, clean the POI data in the target data and match it with the research unit, and, based on the road network geometry, topological information and vehicle speed constraints, use the preset map matching algorithm to match the vehicle trajectory in the target data with the research unit; Step 1.2, extract the physical attributes and social attributes of urban structural characteristics according to the target data matched with the research unit to form the urban spatial structural characteristics; Step 1.3, calculate the average congestion index of each road in the study unit to form the traffic congestion characteristics.

3. The method according to claim 2, characterized in that The step 2 specifically includes: The urban spatial structure characteristics are taken as the independent variable X, and the average congestion index is taken as the dependent variable Y. The multi-scale geographically weighted regression model is used to conduct a multi-scale geographically weighted regression analysis on the urban spatial structure characteristics and traffic congestion characteristics to generate analysis results.

4. The method according to claim 3, characterized in that The expression of the multi-scale geographically weighted regression model is: ； in, It is The dependent variable for each observation point is It is The first observation point independent variables, It is The spatial coordinates of the observation positions, is in position The intercept term at is in position Independent variable The regression coefficients of depend on the spatial coordinates, is the error term; ； in, It is The spatial bandwidth parameter of the independent variable, It's location and location The spatial weight between The spatial bandwidth parameter of the independent variable It is calculated that It is The first position independent variable values, It is The value of the dependent variable at each location.

5. The method according to claim 4, characterized in that The step 3 specifically includes: Step 3.1, extract the regression coefficient of the independent variable in the analysis results as the high-dimensional coordinates of each road, and construct a VR complex for this set of point cloud data; Step 3.2, draw the topological feature duration graphs of different dimensions according to the VR complex, and calculate the duration of all two-dimensional features accordingly; Step 3.3, sort the two-dimensional features in descending order according to the duration, select a preset number of two-dimensional features from top to bottom, extract the road group corresponding to each two-dimensional feature, and output the spatial distribution result corresponding to each road group.

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