A greenway route optimization layout method, system, device and medium based on outdoor track

Abstract

The application discloses an outdoor track-based greenway route selection optimization layout method, system, device and medium, relates to the field of greenway planning, design and construction, and comprises the following steps: acquiring outdoor track data in a certain region range; performing spatial intersection analysis on the research range and the outdoor track data to generate the outdoor track data in the research range; calculating the speed of each track point according to the distance and time difference of adjacent track points of the outdoor track data in the research range, and removing the track points exceeding the upper limit of the cycling speed to obtain slow track data; converting the slow track data into track line data, generating a track line density distribution map according to the track line data, selecting a section with a line density greater than a set density value in the track line density distribution map as a popular greenway route, and incorporating the popular greenway route into an urban and regional greenway route optimization layout scheme. The application improves the scientificity of the urban and regional greenway route layout scheme, and enhances the use efficiency and service value of the greenway.

Description

Technical Field

[0001] This application relates to the field of greenway planning, design and construction, and in particular to a method, system, equipment and medium for optimizing greenway route selection based on outdoor trajectories. Background Technology

[0002] Currently, traditional greenway planning methods still dominate, relying on manual surveys, experience-based judgments, qualitative analysis, and static data analysis. Spatial analysis and quantitative evaluation are insufficient, resulting in limitations such as strong subjectivity, low efficiency, and a disconnect from user needs. While the application of big data technology in greenway planning and construction has made positive progress, it is still largely in the exploratory stage, with the following shortcomings: First, data from shared bicycles and Keep fitness apps are mainly concentrated in urban greenways located within built-up areas, making it difficult to cover greenways outside the central urban area; second, outdoor trajectory data involves a mix of walking, cycling, and driving modes, and data cleaning and selection have not yet been conducted; third, the in-depth mining and utilization of outdoor trajectory data is insufficient, and detailed research on popular greenway routes by time period has not been performed; fourth, the use of multi-source data exhibits significant subjectivity in normalization processing and determination of influence weights, failing to objectively reflect users' actual usage of greenway routes.

[0003] Therefore, it is necessary to provide a technical method based on in-depth analysis of outdoor trajectory data that can be applied to the selection of various types of greenways, so as to improve the scientific nature of urban and regional greenway route layout schemes and solve the above-mentioned technical problems. Summary of the Invention

[0004] The purpose of this application is to provide a method, system, equipment, and medium for optimizing the layout of greenways based on outdoor trajectories, which can improve the scientific nature of urban and regional greenway route layout schemes.

[0005] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for optimizing the layout of greenways based on outdoor trajectories, including: Obtain outdoor trajectory data within a specific area; Spatial intersection analysis is performed between the study area and the outdoor trajectory data to generate outdoor trajectory data within the study area; the study area is a specified range within the area. The speed of each trajectory point is calculated based on the distance and time difference between adjacent trajectory points in the outdoor trajectory data within the research area, and trajectory points exceeding the upper limit of cycling speed are removed to obtain slow-moving trajectory data. The slow-moving trajectory data is converted into trajectory line data, and a trajectory line density distribution map is generated based on the trajectory line data. Sections with line density greater than a set density value in the trajectory line density distribution map are selected as popular greenway routes, and these popular greenway routes are incorporated into the urban and regional greenway route optimization layout scheme.

[0006] Secondly, this application provides a greenway route selection optimization layout system based on outdoor trajectories, including: The data acquisition module is used to acquire outdoor trajectory data within a certain area. The intersection analysis module is used to perform spatial intersection analysis between the study area and the outdoor trajectory data to generate outdoor trajectory data within the study area; the study area is a specified range within the area. The trajectory extraction module is used to calculate the speed of each trajectory point based on the distance and time difference between adjacent trajectory points in the outdoor trajectory data within the research area, and remove trajectory points that exceed the upper limit of cycling speed to obtain slow-moving trajectory data. The route determination module is used to convert the slow-moving trajectory data into trajectory line data, generate a trajectory line density distribution map based on the trajectory line data, select the sections in the trajectory line density distribution map with a line density greater than a set density value as popular greenway routes, and incorporate the popular greenway routes into the urban and regional greenway route optimization layout scheme.

[0007] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for optimizing the layout of greenways based on outdoor trajectories.

[0008] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for optimizing the layout of greenways based on outdoor trajectories.

[0009] According to the specific embodiments provided in this application, this application has the following technical effects: This application utilizes spatial intersection analysis based on real outdoor trajectory data to accurately pinpoint the actual activity range of people, preventing a disconnect between greenway planning and actual travel needs, and improving route practicality. By calculating trajectory point speeds to eliminate high-speed anomalies, effective trajectories conforming to slow-moving characteristics are selected, ensuring that greenway route selection aligns with the patterns of walking, cycling, and other slow-moving travel, thus improving route adaptability. Converting slow-moving trajectories into trajectory lines and generating density distribution maps allows for intuitive identification of high-frequency traffic sections, scientifically determining popular greenway routes, and making the layout more aligned with public usage habits. This method replaces experience-based planning with data-driven approaches, reducing subjective judgment bias, optimizing the efficiency of greenway spatial layout, improving the matching degree between the greenway network and population activities, enhancing the utilization efficiency and service value of greenways, and contributing to the scientific and rational construction of urban and regional greenway systems. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is an application environment diagram of a greenway route selection optimization layout method based on outdoor trajectory in one embodiment of this application.

[0012] Figure 2 This is a flowchart illustrating a method for optimizing the layout of greenways based on outdoor trajectories, provided as an embodiment of this application.

[0013] Figure 3 This is a schematic diagram of the functional modules of a greenway route selection and optimization layout system based on outdoor trajectory, provided in an embodiment of this application.

[0014] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0016] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] The greenway route selection and optimization layout method based on outdoor trajectory provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 101 communicates with server 102 via a network. A data storage system can store the data that server 102 needs to process. The data storage system can be set up independently, integrated into server 102, or placed in the cloud or on another server. Terminal 101 can send outdoor trajectory data to server 102. After receiving the outdoor trajectory data, server 102 extracts popular greenway routes based on the outdoor trajectory data. Server 102 can then feed back the obtained popular greenway routes to terminal 101.

[0018] The terminal 101 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 102 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0019] In one exemplary embodiment, such as Figure 2 As shown, a method for optimizing the layout of greenways based on outdoor trajectories is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 102 as an example, the explanation includes the following steps 201 to 206.

[0020] Step 201: Obtain outdoor trajectory data within a certain area. The outdoor trajectory data includes trajectory points, their coordinates, time, and other information.

[0021] In a specific application example, step 201 includes steps 11 to 13.

[0022] Step 11: Obtain raw outdoor trajectory data within a specific area. In this example, trajectory data shared by users within a specific area is obtained through an outdoor website. The data is in GPX format and includes the coordinates and time (in the format "year-month-day hour:minute:second") of the origin, destination, and waypoints.

[0023] Step 12: Convert the original outdoor trajectory data into SHP format to obtain a geographic coordinate system trajectory SHP file. Specifically, use the "GPX to Feature" tool of Geographic Information System (GIS) to preprocess the original outdoor trajectory data and convert it into geographic coordinate system trajectory SHP format to obtain a geographic coordinate system trajectory SHP file.

[0024] Step 13: Convert the geographic coordinate system trajectory SHP file to a specified projected coordinate system to obtain outdoor trajectory data within the area. Specifically, use the GIS "projection" tool to convert the geographic coordinate system trajectory SHP file to a specified projected coordinate system trajectory SHP file.

[0025] Step 202: Perform spatial intersection analysis on the study area and the outdoor trajectory data to generate outdoor trajectory data within the study area; the study area is the range specified within the area.

[0026] In a specific application example, step 202 includes steps 21 to 22.

[0027] Step 21: Obtain the study scope file, convert the study scope file into SHP format, and simultaneously convert it to the specified projection coordinate system to obtain the study scope SHP file.

[0028] In this example, a file representing the specified study area is obtained and preprocessed to generate a Study Area SHP file with a specified projected coordinate system. If the file is a geographic coordinate system file, the GIS "Projection" tool is used to convert the geographic coordinate system Study Area SHP file into a Study Area SHP file with the same projected coordinate system as the trajectory SHP file.

[0029] Step 22: Perform spatial intersection analysis between the SHP file of the study area and the outdoor trajectory data to obtain the outdoor trajectory data within the study area. Specifically, use the "intersection" tool in GIS to perform spatial intersection analysis between the SHP file of the study area and the trajectory SHP file to generate a trajectory SHP file within the study area in a specified projected coordinate system.

[0030] Step 203: Calculate the speed of each trajectory point based on the distance and time difference between adjacent trajectory points in the outdoor trajectory data within the research area, and remove trajectory points that exceed the upper limit of cycling speed (such as 40km / h) to obtain slow-moving trajectory data.

[0031] In a specific application example, to ensure that user trajectory data reflects slow-moving behavior, outdoor trajectory data needs to be cleaned and selected. Based on the outdoor trajectory data within the research area, the distance and time difference between adjacent trajectory points are calculated, and then the movement speed of the trajectory points is calculated: ; ; ; in, The moving speed of the trajectory point. For displacement, For time intervals, and These are the coordinates of two adjacent trajectory points. and The recording time for two adjacent trajectory points.

[0032] Based on the movement speed of the trajectory points, the user's mode of transportation is determined. When the movement speed exceeds the cycling speed limit (e.g., 40 km / h), the user is considered to be traveling by motor vehicle or other similar means; when the movement speed is below the cycling speed limit (e.g., 40 km / h), the user is considered to be traveling by cycling or walking, or other slow-moving modes. Accordingly, trajectory data exceeding the cycling speed limit (e.g., 40 km / h) is removed, and a slow-moving trajectory SHP file is generated.

[0033] Step 204: Convert the slow-moving trajectory data into trajectory line data, generate a trajectory line density distribution map based on the trajectory line data, select the sections in the trajectory line density distribution map with a line density greater than a set density value as popular greenway routes, and incorporate the popular greenway routes into the urban and regional greenway route optimization layout scheme.

[0034] In a specific application example, step 204 includes steps 41 to 43.

[0035] Step 41: Use the "point set to line" tool in GIS to convert the slow-moving trajectory data within the study area into a trajectory line SHP file.

[0036] Step 42: Use the "line density analysis" tool in GIS to generate a trajectory line density distribution map based on the trajectory line data.

[0037] Line density analysis is a spatial analysis method used to calculate the distribution density of linear features within a unit area. It can quantify the spatial distribution density of linear features. Areas with high line density indicate a concentration of outdoor tracks and significant spatial overlap; these high-density sections represent frequently used, popular routes. The principle of line density analysis is to define a circular neighborhood (search radius) centered on a raster cell, calculate the total length (weighted) of all line features within the circular neighborhood, and divide this length by the area of ​​the circle to obtain the line density per unit area. The formula is: Where Density is the linear density. Let be the length of the i-th line within the circular neighborhood. Let be the weight of the i-th line within the circular neighborhood. Let be the search radius, and k be the number of lines within the circular neighborhood.

[0038] Step 43: Classify the greenway according to line density, select high line density sections as popular greenway routes, and incorporate them into the urban and regional greenway route optimization layout plan as the technical basis for greenway route selection.

[0039] Step 205: Extract the time information of the slow-moving trajectory data, filter out trajectory data for different seasons, generate a seasonal trajectory line density distribution map, and select the sections in the seasonal trajectory line density distribution map with line density greater than a set density value as popular greenway routes in different seasons, incorporate them into the urban and regional greenway route optimization and refinement layout plan, and provide technical basis for the layout of greenway service facilities and landscape greening configuration in all four seasons.

[0040] In a specific application example, step 205 includes steps 51 to 54.

[0041] Step 51: Extract spring slow-moving trajectory data within the research area, and use the GIS "point set to line" and "line density analysis" tools to calculate and generate popular greenway routes in spring.

[0042] In this example, the temporal information of slow-moving trajectory data within the study area is extracted, retaining the trajectory data for the spring period (e.g., March 1st to May 31st), and saving it separately to generate spring trajectory data. Using the GIS "point-to-line" tool, the spring trajectory data is converted into a line SHP file. Using the GIS "line density analysis" tool, a line density distribution map is generated based on the line SHP file. Line density values ​​are categorized, and high-density sections are selected as popular greenway routes in spring, incorporated into the regional greenway route selection optimization and detailed layout plan, providing a technical basis for the layout of greenway service facilities and landscape greening configuration in spring.

[0043] Step 52: Extract summer slow-moving trajectory data within the research area, and use the GIS tools of "point set to line" and "line density analysis" to calculate and generate popular greenway routes in summer.

[0044] In this example, the temporal information of slow-moving trajectory data within the study area is extracted, and trajectory data for the summer period (e.g., June 1st to August 30th) is retained and saved separately to generate summer trajectory data. Using the GIS "point-to-line" tool, the summer trajectory data is converted into a line SHP file. Using the GIS "line density analysis" tool, a line density distribution map is generated based on the line SHP file. Line density values ​​are categorized, and high-density sections are selected as popular greenway routes in summer, incorporated into the regional greenway route selection optimization and detailed layout plan, providing a technical basis for the layout of greenway service facilities and landscape greening configuration in summer.

[0045] Step 53: Extract autumn slow-moving trajectory data within the research area, and use the GIS "point set to line" and "line density analysis" tools to calculate and generate popular greenway routes in autumn.

[0046] In this example, the temporal information of slow-moving trajectory data within the study area is extracted, and trajectory data for the autumn period (e.g., September 1st to November 30th) is retained and saved separately to generate autumn trajectory data. Using the GIS "point-to-line" tool, the autumn trajectory data is converted into a line SHP file. Using the GIS "line density analysis" tool, a line density distribution map is generated based on the line SHP file. Line density values ​​are categorized, and high-density sections are selected as popular greenway routes in autumn, incorporated into the regional greenway route selection optimization and detailed layout plan, providing a technical basis for the layout of greenway service facilities and landscape greening configuration in autumn.

[0047] Step 54: Extract winter slow-moving trajectory data within the research area, and use the GIS "point set to line" and "line density analysis" tools to calculate and generate popular greenway routes in winter.

[0048] In this example, the temporal information of slow-moving trajectory data within the study area is extracted, and trajectory data for the winter period (e.g., December 1st to February 28th) is retained and saved separately to generate winter trajectory data. Using the GIS "point-to-line" tool, the winter trajectory data is converted into a line SHP file. Using the GIS "line density analysis" tool, a line density distribution map is generated based on the line SHP file. Line density values ​​are categorized, and high-density sections are selected as popular greenway routes in winter, incorporated into the regional greenway route selection optimization and detailed layout plan, providing a technical basis for the layout of greenway service facilities and landscape greening configuration in winter.

[0049] Step 206: Extract the time information of the slow-moving trajectory data, filter out the trajectory data for daytime and nighttime, generate a daytime and nighttime trajectory line density distribution map, and select the sections in the daytime and nighttime trajectory line density distribution map with line density greater than a set density value as popular greenway routes for daytime and nighttime, and incorporate them into the urban and regional greenway route optimization and refinement layout plan, so as to provide technical basis for the layout of daytime and nighttime greenway service facilities and landscape lighting configuration.

[0050] In a specific application example, step 206 includes steps 61 and 62.

[0051] Step 61: Extract daytime slow-moving trajectory data within the research area, and use the GIS tools of "point set to line" and "line density analysis" to calculate and generate popular greenway routes during the day.

[0052] In this example, the time information of slow-moving trajectory data within the study area is extracted, retaining the trajectory data for daytime periods (e.g., 06:00–18:00) and saving it separately to generate daytime trajectory data. Using the GIS "point-to-line" tool, the daytime trajectory data is converted into a line SHP file. Using the GIS "line density analysis" tool, a line density distribution map is generated based on the line SHP file. Line density values ​​are categorized, and high-density sections are selected as popular daytime greenway routes, incorporated into the regional greenway route selection optimization and detailed layout plan, providing a technical basis for the layout of daytime greenway service facilities and landscape greening configuration.

[0053] Step 62: Extract nighttime slow-moving trajectory data within the research area, and use the GIS tools of "point set to line" and "line density analysis" to calculate and generate popular greenway routes at night.

[0054] In this example, the time information of slow-moving trajectory data within the study area is extracted, and trajectory data for nighttime periods (e.g., 06:00–18:00) is retained and saved separately to generate nighttime trajectory data. Using the GIS "point-to-line" tool, the nighttime trajectory data is converted into a line SHP file. Using the GIS "line density analysis" tool, a line density distribution map is generated based on the line SHP file. Line density values ​​are categorized, and high-density sections are selected as popular nighttime greenway routes, incorporated into the regional greenway route selection optimization and detailed layout plan, providing a technical basis for the layout of nighttime greenway service facilities and landscape lighting configuration.

[0055] This application overcomes the limitations of relying on experience-based and qualitative analysis in the planning and construction of greenways. It utilizes user-shared outdoor trajectory data to develop a method for calculating the movement speed of adjacent trajectory points. Through data selection, it obtains slow-moving trajectory data, identifies popular greenway routes based on line density analysis, and further calculates popular greenway routes in spring, summer, autumn, and winter, as well as popular daytime and nighttime routes. These are incorporated into the optimized and refined layout plan for urban and regional greenway routes, providing a technical basis for the layout of greenway service facilities in different time periods, seasonal landscaping, and nighttime lighting configuration.

[0056] Specifically, this application provides a standardized, quantitative, implementable, replicable, and scalable technical process and method for optimizing the layout of greenways, based on user-shared outdoor trajectory data. This avoids the inconsistency issues of multi-source data, improves data reliability, and is applicable to various types of greenways, including urban and regional greenways. It significantly reduces the survey and investigation costs for greenway selection, improves the accuracy and efficiency of greenway selection, enhances the scientific nature of urban and regional greenway layout schemes, and contributes to promoting high-quality greenway development. This application can be implemented using software such as ArcGIS and QGIS, or through programming with Arcpy and Python. The process is rigorous, concise, and clear, with broad application scenarios.

[0057] Based on the same inventive concept, this application also provides a system for implementing the methods described above. The solution provided by this system is similar to the solution described in the methods above; therefore, specific limitations in one or more system embodiments provided below can be found in the limitations of the methods described above, and will not be repeated here.

[0058] In one exemplary embodiment, such as Figure 3 As shown, a greenway route selection optimization layout system based on outdoor trajectory is provided, which includes the following functional modules.

[0059] The data acquisition module 301 is used to acquire outdoor trajectory data within a certain area.

[0060] The intersection analysis module 302 is used to perform spatial intersection analysis on the study area and the outdoor trajectory data to generate outdoor trajectory data within the study area; the study area is a range specified within the area.

[0061] The trajectory extraction module 303 is used to calculate the speed of each trajectory point based on the distance and time difference between adjacent trajectory points in the outdoor trajectory data within the research area, and remove trajectory points that exceed the upper limit of cycling speed to obtain slow-moving trajectory data.

[0062] The route determination module 304 is used to convert the slow-moving trajectory data into trajectory line data, generate a trajectory line density distribution map based on the trajectory line data, select the sections in the trajectory line density distribution map with a line density greater than a set density value as popular greenway routes, and incorporate the popular greenway routes into the urban and regional greenway route optimization layout scheme.

[0063] The seasonal route determination module 305 is used to extract the time information of the slow-moving trajectory data, filter the trajectory data of different seasons, generate a seasonal trajectory line density distribution map, and select the sections in the seasonal trajectory line density distribution map with a line density greater than a set density value as popular greenway routes in different seasons.

[0064] The day and night route determination module 306 is used to extract the time information of the slow-moving trajectory data, filter the trajectory data for day and night, generate a day and night trajectory line density distribution map, and select the sections in the day and night trajectory line density distribution map with a line density greater than a set density value as popular greenway routes for day and night.

[0065] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores outdoor trajectory data within a specified area. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a greenway route selection optimization layout method based on outdoor trajectories.

[0066] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer equipment to which the present application is applied. Specific computer equipment may include, for example, [the following is a list of possible additional structures]. Figure 4 The diagram shows more or fewer components, or combinations of certain components, or different component arrangements.

[0067] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0068] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0069] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0070] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0071] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, etc., and are not limited to these.

[0072] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0073] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for optimizing the layout of greenways based on outdoor trajectories, characterized in that, include: Obtain outdoor trajectory data within a specific area; Spatial intersection analysis is performed between the study area and the outdoor trajectory data to generate outdoor trajectory data within the study area; the study area is a specified range within the area. The speed of each trajectory point is calculated based on the distance and time difference between adjacent trajectory points in the outdoor trajectory data within the research area, and trajectory points exceeding the upper limit of cycling speed are removed to obtain slow-moving trajectory data. The slow-moving trajectory data is converted into trajectory line data, and a trajectory line density distribution map is generated based on the trajectory line data. Sections with line density greater than a set density value in the trajectory line density distribution map are selected as popular greenway routes, and these popular greenway routes are incorporated into the urban and regional greenway route optimization layout scheme.

2. The method for optimizing the layout of greenways based on outdoor trajectories according to claim 1, characterized in that, To obtain outdoor trajectory data within a specific area, including: Obtain raw outdoor trajectory data within a specific area; The original outdoor trajectory data is converted into SHP format to obtain a geographic coordinate system trajectory SHP file; The geographic coordinate system trajectory SHP file is converted to a specified projected coordinate system to obtain outdoor trajectory data within the region.

3. The method for optimizing the layout of greenways based on outdoor trajectories according to claim 2, characterized in that, Spatial intersection analysis is performed between the study area and the outdoor trajectory data to generate outdoor trajectory data within the study area, including: Obtain the study scope file, convert the study scope file into SHP format, and simultaneously convert it to a specified projection coordinate system to obtain the study scope SHP file; Spatial intersection analysis is performed between the SHP file of the research area and the outdoor trajectory data to obtain the outdoor trajectory data within the research area.

4. The method for optimizing the layout of greenways based on outdoor trajectories according to claim 1, characterized in that, The moving speed of the trajectory point is calculated using the following formula: ； ； ； in, The moving speed of the trajectory point. For displacement, For time intervals, and These are the coordinates of two adjacent trajectory points. and The recording time for two adjacent trajectory points.

5. The method for optimizing the layout of greenways based on outdoor trajectories according to claim 1, characterized in that, Sections with line density greater than a set density value in the trajectory line density distribution map are selected as popular greenway routes, including: A circular neighborhood is delineated in the trajectory line density distribution map with the grid cell as the center, and the search radius is determined; Based on the length of each line within the circular neighborhood and the area of ​​the circular neighborhood, the formula is used. Calculate the linear density of the circular neighborhood; where Density is the linear density. Let be the length of the i-th line within the circular neighborhood. Let be the weight of the i-th line within the circular neighborhood. Let be the search radius, and k be the number of lines within the circular neighborhood.

6. The method for optimizing the layout of greenways based on outdoor trajectories according to claim 1, characterized in that, The method further includes: The time information of the slow-moving trajectory data is extracted, the trajectory data of different seasons are filtered out, a seasonal trajectory line density distribution map is generated, and the sections in the seasonal trajectory line density distribution map with line density greater than a set density value are selected as popular greenway routes in different seasons.

7. The method for optimizing the layout of greenways based on outdoor trajectories according to claim 1, characterized in that, The method further includes: The time information of the slow-moving trajectory data is extracted, and the trajectory data for daytime and nighttime are filtered out to generate a daytime and nighttime trajectory line density distribution map. The sections in the daytime and nighttime trajectory line density distribution map with line density greater than a set density value are selected as popular greenway routes for daytime and nighttime.

8. A greenway route selection and optimization layout system based on outdoor trajectory, applied to the greenway route selection and optimization layout method based on outdoor trajectory as described in any one of claims 1-7, characterized in that, The system includes: The data acquisition module is used to acquire outdoor trajectory data within a certain area. The intersection analysis module is used to perform spatial intersection analysis between the study area and the outdoor trajectory data to generate outdoor trajectory data within the study area; the study area is a specified range within the area. The trajectory extraction module is used to calculate the speed of each trajectory point based on the distance and time difference between adjacent trajectory points in the outdoor trajectory data within the research area, and remove trajectory points that exceed the upper limit of cycling speed to obtain slow-moving trajectory data. The route determination module is used to convert the slow-moving trajectory data into trajectory line data, generate a trajectory line density distribution map based on the trajectory line data, select the sections in the trajectory line density distribution map with a line density greater than a set density value as popular greenway routes, and incorporate the popular greenway routes into the urban and regional greenway route optimization layout scheme.

9. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the greenway route selection optimization layout method based on any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the greenway route selection optimization layout method based on outdoor trajectory as described in any one of claims 1-7.

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