A method and system for analyzing residents' travel behavior based on road network centrality

Abstract

The present invention discloses a method and system for analyzing residents' travel behavior based on road network centrality. Shared bicycle trajectory data reveals residents' riding behavior characteristics, such as frequency, time and distance, and provides a basis for behavior analysis. Urban road network data ensures the accuracy of road network centrality calculation and is the key to building a road network model. Urban built environment data helps to analyze its impact on riding behavior. Taking streets as analysis units, it can capture riding behavior characteristics more finely. The road network model displays the structure of the road network and provides visualization tools for subsequent analysis. Proximity centrality, betweenness centrality and direct access centrality provide multi-dimensional perspectives, deeply analyze riding behavior, use network analysis tools for calculation, ensure the accuracy and efficiency of the results, and build a linear regression model to analyze the relationship between riding behavior and factors such as riding supply, road network centrality and urban built environment, providing a scientific basis for optimization strategies.

Description

Technical Field

[0001] The present invention relates to the technical field of resident travel behavior analysis, and in particular to a resident travel behavior analysis method and system based on road network centrality. Background Art

[0002] With the acceleration of urbanization and the continuous development of intelligent transportation systems, the analysis of residents' travel behavior has become an important topic in the fields of urban planning, traffic management and public service optimization. Traditional studies on residents' travel behavior mostly rely on questionnaire surveys, traffic flow monitoring and other means. Although these methods can provide certain data support, they have problems such as high data collection costs, poor timeliness and limited coverage.

[0003] In recent years, with the rise of big data technology, especially the popularization of shared travel modes (such as shared bicycles, shared cars, etc.), a new data source has been provided for the analysis of residents' travel behavior. As an important supplement to the urban public transportation system, shared bicycles record a large amount of residents' travel behavior information, including travel starting point, end point, time, distance, etc., providing a rich data basis for in-depth analysis of residents' travel patterns. However, it is not enough to rely solely on shared bicycle trajectory data for travel behavior analysis, which can easily lead to inaccurate analysis results of residents' travel behavior. Summary of the invention

[0004] In view of this, the present invention proposes a method and system for analyzing residents' travel behavior based on road network centrality, which can improve the accuracy and timeliness of residents' travel behavior analysis.

[0005] The technical solution of the present invention is achieved in this way:

[0006] A method for analyzing residents' travel behavior based on road network centrality, specifically including:

[0007] Data acquisition steps: Acquire shared bicycle trajectory data, urban road network data, and urban built environment data in the study area. The shared bicycle trajectory data is used to reflect the riding behavior characteristics of residents;

[0008] Network construction steps: Taking streets as analysis units, a road network model is constructed based on urban road network data, and nodes and edges in the road network model are determined;

[0009] Centrality calculation step: using a network analysis tool to calculate the proximity centrality, betweenness centrality and directness centrality of each street unit in the road network model;

[0010] Behavior analysis: Based on the trajectory data of shared bicycles, the riding behavior characteristics of residents in the study area are analyzed, including riding frequency, riding time and riding distance;

[0011] Modeling and analysis: Taking cycling behavior characteristics as dependent variables and cycling supply, road network centrality and urban built environment data as independent variables, a linear regression model under different search radii was constructed, and the analysis results of residents' travel behavior were obtained based on the constructed linear regression model.

[0012] As a further optional solution of the resident travel behavior analysis method based on road network centrality, the neighborhood centrality of each street unit in the road network model is calculated by the following specific formula:

[0013] ;

[0014] Among them, the Representation Node i The proximity centrality of n represents the total number of nodes in the road network model, Representation Node i To Node j The shortest distance.

[0015] As a further optional solution of the resident travel behavior analysis method based on road network centrality, the betweenness centrality of each street unit in the road network model is calculated by the following specific formula:

[0016] ;

[0017] Among them, the Representation Node k The betweenness centrality of Representation Node i To Node j The total number of shortest paths, Indicates the nodes passed through in these shortest paths k The number of

[0018] As a further optional solution of the resident travel behavior analysis method based on road network centrality, the direct centrality of each street unit in the road network model is calculated, and the specific formula is:

[0019] ;

[0020] Among them, the Representation Node i The direct centrality of Representation Node i With Node j The Euclidean distance between Representation Node i With Node j The shortest network distance between them.

[0021] As a further optional solution to the resident travel behavior analysis method based on road network centrality, the riding behavior characteristics of residents in the study area are analyzed based on the trajectory data of shared bicycles. The riding behavior characteristics include riding frequency, riding time and riding distance, specifically including:

[0022] Count the frequency of cycling in different time periods and analyze the time distribution of cycling activities;

[0023] Draw a spatial distribution map of cycling frequency to show cycling activity in different areas;

[0024] Count the number of orders with different riding times and analyze the distribution of riding time;

[0025] Draw a scatter plot or linear regression graph to show the relationship between riding time and distance;

[0026] Count the number of orders with different riding distances and analyze the distribution of riding distances;

[0027] Based on urban planning and residents’ travel needs, analyze the travel purposes corresponding to different cycling distances.

[0028] As a further optional solution of the resident travel behavior analysis method based on road network centrality, the cycling behavior characteristics are used as dependent variables, and the cycling supply, road network centrality and urban built environment data are used as independent variables to construct linear regression models under different search radii. The analysis results of resident travel behavior are obtained according to the constructed linear regression model, which specifically includes:

[0029] Select different search radius according to the research purpose and regional characteristics;

[0030] Under each search radius, a linear regression method was used to build a model and conduct regression analysis on the independent variables and the dependent variables;

[0031] Analyze the degree and direction of the influence of independent variables on dependent variables in the model;

[0032] According to the degree and direction of the influence of the independent variables on the dependent variables, the changing patterns of residents' travel behavior under different search radii are analyzed.

[0033] As a further optional solution of the resident travel behavior analysis method based on road network centrality, the method also includes proposing an optimization strategy for urban street cycling based on the analysis results of the resident travel behavior, specifically including:

[0034] Adjust the number and distribution of shared bikes based on the frequency of riding and hot spots in the analysis results of residents' travel behavior;

[0035] According to the road network centrality in the analysis results of residents' travel behavior, the traffic design of key paths and nodes is optimized.

[0036] A resident travel behavior analysis system based on road network centrality, including:

[0037] A data acquisition unit is used to acquire shared bicycle trajectory data, urban road network data, and urban built environment data in the study area, wherein the shared bicycle trajectory data is used to reflect the riding behavior characteristics of residents;

[0038] A network construction unit, which is used to construct a road network model based on urban road network data by taking streets as analysis units, and to determine nodes and edges in the road network model;

[0039] A centrality calculation unit, used to calculate the proximity centrality, betweenness centrality and directness centrality of each street unit in the road network model using a network analysis tool;

[0040] A behavior analysis unit, used to analyze the riding behavior characteristics of residents in the study area according to the trajectory data of the shared bicycles, wherein the riding behavior characteristics include riding frequency, riding time and riding distance;

[0041] The modeling and analysis unit is used to construct a linear regression model under different search radii with cycling behavior characteristics as dependent variables and cycling supply, road network centrality and urban built environment data as independent variables, and obtain the analysis results of residents' travel behavior based on the constructed linear regression model.

[0042] A computing device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for analyzing residents' travel behavior based on road network centrality are implemented.

[0043] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned method for analyzing resident travel behavior based on road network centrality.

[0044] The beneficial effects of the present invention are as follows: shared bicycle trajectory data directly reflects the riding behavior characteristics of residents, including key information such as riding frequency, riding time and riding distance, providing a solid foundation for subsequent behavior analysis; urban road network data is used to construct a road network model, which is the basis for calculating the centrality of the road network. Accurate road network data can ensure the accuracy and reliability of centrality calculation. Urban built environment data provides information on key factors such as land use, building density, and greening rate, which helps to analyze the impact of these factors on residents' cycling behavior. Taking streets as analysis units makes the research more refined and can more accurately capture the cycling behavior characteristics at the street level. By constructing a road network model, the structure and characteristics of the road network can be clearly displayed, providing a visualization tool for subsequent centrality calculation and behavior analysis. Proximity centrality, betweenness centrality, and direct centrality reflect the importance and influence of streets in the network from different perspectives, providing a multi-dimensional perspective for in-depth analysis of residents' cycling behavior. Using network analysis tools for calculation can ensure the accuracy and scientificity of the results and improve calculation efficiency. By constructing a linear regression model, the correlation between cycling behavior characteristics and factors such as cycling supply, road network centrality, and urban built environment can be deeply analyzed, providing a scientific basis for formulating optimization strategies. By constructing linear regression models under different search radii, the relationship between residents' travel behavior and various influencing factors can be more accurately reflected. Based on the constructed linear regression model, future residents' travel behavior can be predicted, providing forward-looking guidance for urban transportation planning and policy formulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 A schematic diagram of a flow chart of a method for analyzing residents' travel behavior based on road network centrality according to the present invention;

[0047] Figure 2 The present invention is a schematic diagram of the composition of a resident travel behavior analysis system based on road network centrality. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] refer to Figure 1 to Figure 2 , a method for analyzing residents' travel behavior based on road network centrality, specifically including:

[0050] Data acquisition steps: Acquire shared bicycle trajectory data, urban road network data, and urban built environment data in the study area. The shared bicycle trajectory data is used to reflect the riding behavior characteristics of residents;

[0051] Network construction steps: Taking streets as analysis units, a road network model is constructed based on urban road network data, and nodes and edges in the road network model are determined;

[0052] Centrality calculation step: using a network analysis tool to calculate the proximity centrality, betweenness centrality and directness centrality of each street unit in the road network model;

[0053] Behavior analysis: Based on the trajectory data of shared bicycles, the riding behavior characteristics of residents in the study area are analyzed, including riding frequency, riding time and riding distance;

[0054] Modeling and analysis: Taking cycling behavior characteristics as dependent variables and cycling supply, road network centrality and urban built environment data as independent variables, a linear regression model under different search radii was constructed, and the analysis results of residents' travel behavior were obtained based on the constructed linear regression model.

[0055] In this embodiment, the shared bicycle trajectory data directly reflects the residents' riding behavior characteristics, including key information such as riding frequency, riding time and riding distance, providing a solid foundation for subsequent behavioral analysis. The urban road network data is used to construct a road network model, which is the basis for calculating the road network centrality. Accurate road network data can ensure the accuracy and reliability of centrality calculation. Urban built environment data provides information on key factors such as land use, building density, and greening rate, which helps to analyze the impact of these factors on residents' cycling behavior. Taking streets as analysis units makes the research more refined and can more accurately capture the cycling behavior characteristics at the street level. By constructing a road network model, the structure and characteristics of the road network can be clearly displayed, providing a visualization tool for subsequent centrality calculation and behavior analysis. Proximity centrality, betweenness centrality, and direct centrality reflect the importance and influence of streets in the network from different perspectives, providing a multi-dimensional perspective for in-depth analysis of residents' cycling behavior. Using network analysis tools for calculation can ensure the accuracy and scientificity of the results and improve calculation efficiency. By constructing a linear regression model, the correlation between cycling behavior characteristics and factors such as cycling supply, road network centrality, and urban built environment can be deeply analyzed, providing a scientific basis for formulating optimization strategies. By constructing linear regression models under different search radii, the relationship between residents' travel behavior and various influencing factors can be more accurately reflected. Based on the constructed linear regression model, future residents' travel behavior can be predicted, providing forward-looking guidance for urban transportation planning and policy formulation.

[0056] Preferably, the calculation of the proximity centrality of each street unit in the road network model is carried out using the following specific formula:

[0057] ;

[0058] Among them, the Representation Node i The proximity centrality of n represents the total number of nodes in the road network model, Representation Node i To Node j The shortest distance.

[0059] In this embodiment, the proximity centrality of each node is calculated by the above formula. This method can accurately quantify the position of the node in the network, thereby helping us identify which nodes are more central or more peripheral; by calculating the proximity centrality of each node, the structural characteristics of the road network can be revealed. For example, nodes with high proximity centrality may be located in the central area of ​​the network, while nodes with low proximity centrality may be located at the edge or peripheral area of ​​the network; it can provide strong support for traffic planning and decision-making. For example, when planning new traffic routes or facilities, nodes with high proximity centrality can be given priority because these nodes are more important in the network and are more susceptible to traffic flow.

[0060] Preferably, the betweenness centrality of each street unit in the road network model is calculated using the following specific formula:

[0061] ;

[0062] Among them, the represents the betweenness centrality of node k, Representation Node i To Node j The total number of shortest paths, represents the number of nodes k passed through in these shortest paths.

[0063] In this embodiment, by calculating the betweenness centrality of each street unit, the structural characteristics of the road network can be revealed. Street units with high betweenness centrality are often key nodes in the network, and they play an important connection role in the network. The calculation results of the betweenness centrality can provide a scientific basis for traffic planning. For example, when planning new traffic routes, those street units with higher betweenness centrality can be given priority to ensure that the new routes can effectively connect different parts of the network. Understanding the betweenness centrality of each street unit helps to optimize the allocation of urban resources. For example, more public transportation resources (such as buses, subway stations, etc.) can be allocated to those street units with higher betweenness centrality to improve the efficiency of the entire transportation system.

[0064] Preferably, the specific formula for calculating the direct centrality of each street unit in the road network model is:

[0065] ;

[0066] Among them, the Representation Node i The direct centrality of Representation Node i With Node j The Euclidean distance between Representation Node i With Nodej The shortest network distance between them.

[0067] In this embodiment, by calculating the direct centrality of each street unit, this method can accurately quantify the direct accessibility of a node in the network, that is, the shortest path length from a node to other nodes; by calculating the direct centrality, the structural characteristics of the road network can be revealed. Nodes with high direct centrality are usually key nodes in the network, and they play an important connection role in the network. In addition, the distribution of direct centrality can also reflect the density and connectivity of the network; the calculation results of direct centrality can provide a scientific basis for path selection. When planning new traffic routes or selecting the optimal path, street units with higher direct centrality can be given priority to ensure the efficiency and reliability of the path; by understanding the direct centrality of each street unit, network resources can be allocated more reasonably and the efficiency of the entire network can be improved. For example, in case of traffic congestion, vehicles can be guided through street units with higher direct centrality to relieve traffic pressure.

[0068] Preferably, the riding behavior characteristics of residents in the study area are analyzed based on the trajectory data of the shared bicycles, and the riding behavior characteristics include riding frequency, riding time and riding distance, specifically including:

[0069] Count the frequency of cycling in different time periods and analyze the time distribution of cycling activities;

[0070] Draw a spatial distribution map of cycling frequency to show cycling activity in different areas;

[0071] Count the number of orders with different riding times and analyze the distribution of riding time;

[0072] Draw a scatter plot or linear regression graph to show the relationship between riding time and distance;

[0073] Count the number of orders with different riding distances and analyze the distribution of riding distances;

[0074] Based on urban planning and residents’ travel needs, analyze the travel purposes corresponding to different cycling distances.

[0075] In this embodiment, by counting the riding frequency in different time periods, the time distribution pattern of residents' riding activities can be clearly displayed, which helps to understand the travel needs and riding habits of residents in different time periods, and provides an important reference for urban traffic planning and shared bicycle scheduling; drawing a spatial distribution map of riding frequency can intuitively show the riding activity in different areas, which helps to identify riding hot spots and unpopular areas, and provide a basis for the selection and optimization of shared bicycle deployment points; counting the number of orders with different riding durations can deeply understand the distribution of residents' riding time, which helps to evaluate the utilization rate and riding efficiency of shared bicycles, and provide data support for the adjustment and optimization of shared bicycle service hours; Drawing a scatter plot or linear regression graph can show the relationship between riding time and distance, which helps to understand residents' riding speed and riding habits, and provide improvement suggestions for the design and use of shared bicycles; counting the number of orders for different riding distances can reveal the distribution of residents' riding distances, which helps to understand residents' travel needs and travel range, and provide a basis for urban transportation planning and adjustments to the scope of shared bicycle services; combining urban planning and residents' travel needs, analyzing the travel purposes corresponding to different riding distances can provide an in-depth understanding of residents' travel patterns and travel needs, which helps to provide targeted suggestions for urban transportation planning and optimization of shared bicycle services, and improve the convenience and efficiency of urban transportation.

[0076] Preferably, the cycling behavior characteristics are used as dependent variables, and the cycling supply, road network centrality and urban built environment data are used as independent variables to construct linear regression models under different search radii, and the analysis results of residents' travel behavior are obtained according to the constructed linear regression model, which specifically includes:

[0077] Select different search radius according to the research purpose and regional characteristics;

[0078] Under each search radius, a linear regression method was used to build a model and conduct regression analysis on the independent variables and the dependent variables;

[0079] Analyze the degree and direction of the influence of independent variables on dependent variables in the model;

[0080] According to the degree and direction of the influence of the independent variables on the dependent variables, the changing patterns of residents' travel behavior under different search radii are analyzed.

[0081] In this embodiment, by constructing a linear regression model, the technical solution can systematically reveal the quantitative relationship between multiple independent variables such as cycling supply, road network centrality and urban built environment and cycling behavior characteristics, which helps us to have a deeper understanding of how these factors work together to affect residents' cycling behavior; different search radii are selected according to different research purposes and regional characteristics. This flexibility ensures that the model can more accurately reflect the travel behavior characteristics of residents in different geographical regions and traffic environments, thereby improving the pertinence and practicality of the analysis; the linear regression model can quantify the degree and direction of the influence of each independent variable on the dependent variable, which enables us to clearly understand which factors have a significant impact on cycling behavior and how these factors affect cycling behavior; by comparing the results of the linear regression model under different search radii, we can find the changing patterns of residents' travel behavior at different spatial scales, which helps us to have a more comprehensive understanding of residents' travel patterns and needs in different spatial ranges.

[0082] Preferably, the method further includes proposing an optimization strategy for urban street cycling based on the analysis results of residents' travel behavior, specifically including:

[0083] Adjust the number and distribution of shared bikes based on the frequency of riding and hot spots in the analysis results of residents' travel behavior;

[0084] According to the road network centrality in the analysis results of residents' travel behavior, the traffic design of key paths and nodes is optimized.

[0085] In this embodiment, according to the riding frequency and hot spots in the analysis results of residents' travel behavior, the number and distribution of shared bicycles can be accurately adjusted, which helps to ensure that there are enough shared bicycles for residents to use in areas with high riding demand, while avoiding waste of resources in areas with low riding demand. By optimizing the deployment strategy of shared bicycles, the utilization rate of shared bicycles and residents' riding satisfaction can be improved; the road network centrality in the analysis results of residents' travel behavior reveals the importance of key paths and nodes in the riding network. By optimizing the traffic design of these key paths and nodes, such as adding bicycle lanes, improving intersection design, and improving road smoothness, the riding experience and safety can be significantly improved. At the same time, optimizing the design of key paths and nodes can also help alleviate traffic congestion and improve riding efficiency.

[0086] A resident travel behavior analysis system based on road network centrality, including:

[0087] A data acquisition unit is used to acquire shared bicycle trajectory data, urban road network data, and urban built environment data in the study area, wherein the shared bicycle trajectory data is used to reflect the riding behavior characteristics of residents;

[0088] A network construction unit, which is used to construct a road network model based on urban road network data by taking streets as analysis units, and to determine nodes and edges in the road network model;

[0089] A centrality calculation unit, used to calculate the proximity centrality, betweenness centrality and directness centrality of each street unit in the road network model using a network analysis tool;

[0090] A behavior analysis unit, used to analyze the riding behavior characteristics of residents in the study area according to the trajectory data of the shared bicycles, wherein the riding behavior characteristics include riding frequency, riding time and riding distance;

[0091] The modeling and analysis unit is used to construct a linear regression model under different search radii with cycling behavior characteristics as dependent variables and cycling supply, road network centrality and urban built environment data as independent variables, and obtain the analysis results of residents' travel behavior based on the constructed linear regression model.

[0092] A computing device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for analyzing residents' travel behavior based on road network centrality are implemented.

[0093] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned method for analyzing resident travel behavior based on road network centrality.

[0094] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for analyzing residents' travel behavior based on road network centrality, characterized in that: Specifically include: Data acquisition steps: Acquire shared bicycle trajectory data, urban road network data, and urban built environment data in the study area. The shared bicycle trajectory data is used to reflect the riding behavior characteristics of residents; Network construction steps: Taking streets as analysis units, a road network model is constructed based on urban road network data, and nodes and edges in the road network model are determined; Centrality calculation step: using a network analysis tool to calculate the proximity centrality, betweenness centrality and directness centrality of each street unit in the road network model; Behavior analysis: Based on the trajectory data of shared bicycles, the riding behavior characteristics of residents in the study area are analyzed, including riding frequency, riding time and riding distance; Modeling and analysis: Taking cycling behavior characteristics as dependent variables and cycling supply, road network centrality and urban built environment data as independent variables, a linear regression model with different search radii was constructed, and the analysis results of residents' travel behavior were obtained based on the constructed linear regression model; The specific formula for calculating the proximity centrality of each street unit in the road network model is: ； Among them, the Representation Node i The proximity centrality of n represents the total number of nodes in the road network model, Representation Node i To Node j The shortest distance; The specific formula for calculating the betweenness centrality of each street unit in the road network model is: ； Among them, the Representation Node k The betweenness centrality of Representation Node i To Node j The total number of shortest paths, Indicates the nodes passed through in these shortest paths k the number of The specific formula for calculating the direct centrality of each street unit in the road network model is: ； Among them, the Representation Node i The direct centrality of Representation Node i With Node j The Euclidean distance between Representation Node i With Node j The shortest network distance between The method also includes proposing an optimization strategy for urban street cycling based on the analysis results of residents' travel behavior, specifically including: Adjust the number and distribution of shared bikes based on the frequency of riding and hot spots in the analysis results of residents' travel behavior; According to the road network centrality in the analysis results of residents' travel behavior, the traffic design of key paths and nodes is optimized.

2. The method for analyzing residents' travel behavior based on road network centrality according to claim 1 is characterized in that: The riding behavior characteristics of residents in the study area are analyzed based on the trajectory data of shared bicycles. The riding behavior characteristics include riding frequency, riding time and riding distance, specifically including: Count the frequency of cycling in different time periods and analyze the time distribution of cycling activities; Draw a spatial distribution map of cycling frequency to show cycling activity in different areas; Count the number of orders with different riding times and analyze the distribution of riding time; Draw a scatter plot or linear regression graph to show the relationship between riding time and distance; Count the number of orders with different riding distances and analyze the distribution of riding distances; Based on urban planning and residents’ travel needs, analyze the travel purposes corresponding to different cycling distances.

3. The method for analyzing residents' travel behavior based on road network centrality according to claim 2 is characterized in that: The above method takes cycling behavior characteristics as dependent variables, cycling supply, road network centrality and urban built environment data as independent variables, constructs linear regression models under different search radii, and obtains the analysis results of residents' travel behavior based on the constructed linear regression model, which specifically includes: Select different search radius according to the research purpose and regional characteristics; Under each search radius, a linear regression method was used to build a model and conduct regression analysis on the independent variables and the dependent variables; Analyze the degree and direction of the influence of independent variables on dependent variables in the model; According to the degree and direction of the influence of the independent variables on the dependent variables, the changing patterns of residents' travel behavior under different search radii are analyzed.

4. A resident travel behavior analysis system based on road network centrality, characterized in that: include: A data acquisition unit is used to acquire shared bicycle trajectory data, urban road network data, and urban built environment data in the study area, wherein the shared bicycle trajectory data is used to reflect the riding behavior characteristics of residents; A network construction unit, which is used to construct a road network model based on urban road network data by taking streets as analysis units, and to determine nodes and edges in the road network model; A centrality calculation unit, used to calculate the proximity centrality, betweenness centrality and directness centrality of each street unit in the road network model using a network analysis tool; A behavior analysis unit, used to analyze the riding behavior characteristics of residents in the study area according to the trajectory data of the shared bicycles, wherein the riding behavior characteristics include riding frequency, riding time and riding distance; The modeling and analysis unit is used to construct a linear regression model under different search radii with cycling behavior characteristics as dependent variables and cycling supply, road network centrality and urban built environment data as independent variables, and obtain the analysis results of residents' travel behavior based on the constructed linear regression model; The specific formula for calculating the proximity centrality of each street unit in the road network model is: ； Among them, the Representation Node i The proximity centrality of n represents the total number of nodes in the road network model, Representation Node i To Node j The shortest distance; The specific formula for calculating the betweenness centrality of each street unit in the road network model is: ； Among them, the Representation Node k The betweenness centrality of Representation Node i To Node j The total number of shortest paths, Indicates the nodes passed through in these shortest paths k the number of The specific formula for calculating the direct centrality of each street unit in the road network model is: ； Among them, the represents the direct centrality of node i, represents the Euclidean distance between node i and node j, represents the shortest network distance between node i and node j; The system also includes proposing an optimization strategy for urban street cycling based on the analysis results of residents' travel behavior, specifically including: Adjust the number and distribution of shared bikes based on the frequency of riding and hot spots in the analysis results of residents' travel behavior; According to the road network centrality in the analysis results of residents' travel behavior, the traffic design of key paths and nodes is optimized.

5. A computing device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for analyzing resident travel behavior based on road network centrality described in any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for analyzing resident travel behavior based on road network centrality as described in any one of claims 1 to 3.

Images