Urban traffic carbon emission correlation analysis method and system based on space syntax

Abstract

The invention provides an urban traffic carbon emission correlation analysis method and system based on a space syntax, and the method comprises the steps: obtaining the road network vector data of a target city, carrying out the preprocessing of the road network vector data through ArcGIS software, and generating a road grid unit which comprises the road network density, constructing a road network topological space model by adopting a line segment model through DepthmapX software, and calculating a road segment-level space syntactic index of each road segment; aggregating the road section level space syntax indexes and the road network density of the road grid units to all the road grid units; based on the economic activity intensity, generating a road grid unitized carbon emission distribution diagram by using the total urban traffic carbon emission amount; and constructing a multiple regression model, determining a quantitative relationship between the road structure and carbon emission based on the multiple regression model, and making a traffic planning scheme. According to the method, the high-carbon emission area and the key road nodes are accurately recognized, and the method is high in universality and can be popularized to traffic carbon emission analysis and optimization practice of different cities.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of carbon emission evaluation, and particularly relates to a city traffic carbon emission correlation analysis method and system based on spatial syntax. BACKGROUND

[0002] Under the background of global climate change, carbon emission control has become a core task to promote sustainable development. With the proposal of the double-carbon strategy goal of carbon peak and carbon neutrality, cities, as the main spatial unit of energy consumption and carbon emission, are facing great emission reduction pressure. The traffic system, as an important basis for city operation and resident travel, its operation efficiency, road structure and travel mode selection will significantly affect the traffic carbon emission level.

[0003] Therefore, a city traffic carbon emission correlation analysis method and system based on spatial syntax are needed to determine the quantitative relationship between road structure and carbon emission and develop a traffic planning scheme. SUMMARY

[0004] The application aims to provide a city traffic carbon emission correlation analysis method and system based on spatial syntax to solve the technical problems proposed in the background.

[0005] To achieve the above-mentioned purpose, the application provides a city traffic carbon emission correlation analysis method based on spatial syntax, which comprises the following steps: Step 1: Obtain the road network vector data of the target city through the OpenStreetMap platform and preprocess the road network vector data by using the ArcGIS software to generate 1km*1km road grid units including road network density; based on the preprocessed road network vector data, a line segment model is used to construct a road network topology space model by using the DepthmapX software to calculate the road section level spatial syntax index of each road section, wherein the spatial syntax index includes integration, selection and connection; Step 2: The road section level spatial syntax index and the road network density of the 1km*1km road grid unit are subjected to spatial overlay analysis in the ArcGIS software, and the arithmetic mean method is used to aggregate to all 1km*1km road grid units; Step 3: Obtain the city traffic fossil energy types and corresponding energy consumption through the city statistical yearbook, calculate the total city traffic carbon emission, and obtain the economic activity intensity through the earth resource data cloud platform, match the total city traffic carbon emission to the 1km*1km road grid unit based on the economic activity intensity, and generate a 1km*1km road grid unit carbon emission distribution map; Step 4: Construct a multiple regression model, determine the quantitative relationship between road structure and carbon emission based on the multiple regression model, and develop a traffic planning scheme.

[0006] Further, the specific method for obtaining the road network vector data of the target city through the OpenStreetMap platform and pre-processing the road network vector data by using the ArcGIS software in step 1 comprises: obtaining the road network vector data through the OpenStreetMap platform, wherein the road network vector data comprises road geometry data and road attribute data; projecting and converting the road network vector data by using the ArcGIS software to convert into the UTM coordinate system; performing topological inspection on the road network vector data to repair road breaks, overlaps and hanging nodes; and cutting the road network vector data according to the boundary of the urban built-up area to retain the standardized road segments within the research scope.

[0007] Further, the calculation formula of the integration degree of the 1km×1km road grid unit i in step 1 is: wherein, indicates the integration degree of the 1km×1km road grid unit i, d ij is the shortest topological distance between the 1km×1km road grid unit i and the 1km×1km road grid unit j, and n is the total number of 1km×1km road grid units.

[0008] Further, the calculation formula of the selection degree of the 1km×1km road grid unit i in step 1 is: wherein, Choice i indicates the selection degree of the 1km×1km road grid unit i; indicates the total number of shortest paths from point s to point t, indicates the number of nodes passing through in the shortest path from point s to point t.

[0009] Further, the calculation formula of the connection degree of the 1km×1km road grid unit i in step 1 is: wherein, Con i indicates the connection degree of the 1km×1km road grid unit i; a ij indicates the direct connectivity between the 1km×1km road grid unit i and the 1km×1km road grid unit j, and is 1 if connected and 0 otherwise.

[0010] Further, the calculation formula of the total amount of urban traffic carbon emissions in step 3 is: wherein, CCarbon emissions; E j For the first j Consumption of various fossil fuels; K j For the first j The carbon emission coefficient of a type of fossil fuel.

[0011] Furthermore, the expression for the multiple regression model in step 4 is: Among them, C i The carbon emissions of a 1km×1km road grid unit i; For the regression constant term; The road network density is defined as 1km × 1km road grid cell i. The weighting coefficient for road network density; Con i The connectivity of a 1km×1km road grid unit i; The weighting coefficient for road connectivity; I i The integration degree of a 1km×1km road grid unit i; The weighting coefficient for road integration; Choice i The selectivity of road grid cell i is 1km×1km. This represents the weighting coefficient for road selectivity. It is a random disturbance term that satisfies the normal distribution assumption.

[0012] A second aspect of the present invention provides a spatial syntax-based urban traffic carbon emission correlation analysis system, comprising: The first module is used to acquire road network vector data of the target city through the OpenStreetMap platform and preprocess the road network vector data using ArcGIS software to generate 1km×1km road grid cells including road network density; based on the preprocessed road network vector data, the DepthmapX software is used to construct a road network topology spatial model using a line segment model, and calculates the segment-level spatial syntax index for each road segment, which includes integration degree, selectivity degree and connectivity degree; The second module is used to perform spatial overlay analysis of road segment-level spatial syntax indicators and road network density of 1km×1km road grid units in ArcGIS software, and to aggregate them to all 1km×1km road grid units using the arithmetic mean method. The third module is configured to obtain the types of urban traffic fossil energy and corresponding energy consumption from the urban statistical yearbook, calculate the total urban traffic carbon emission, and obtain the economic activity intensity from the earth resource data cloud platform, match the total urban traffic carbon emission to the 1km*1km road grid unit based on the economic activity intensity, and generate a 1km*1km road grid unit carbon emission distribution map. The fourth module is configured to build a multiple regression model, determine the quantitative relationship between the road structure and carbon emission based on the multiple regression model, and develop a traffic planning scheme.

[0013] The third aspect of the present application provides an electronic device, comprising a memory, a processor and a computer program, the computer program is stored in the memory and is configured to be executed by the processor to realize the method of any one of the aspects.

[0014] The beneficial effects brought by the technical scheme of the present application include: 1. The spatial resolution is significantly improved: the spatialization of carbon emission intensity is realized, the high-carbon emission area and key road nodes are accurately identified, the problem of "unable to locate the key of emission reduction" in traditional total amount accounting is solved, and fine spatial emission reduction planning is supported; 2. Structure and emission depth coupling: the system coupling model of "spatial index-traffic carbon emission" is first constructed, the influence mechanism of road network density, integration degree, selection degree and connection degree on carbon emission is quantitatively revealed, and the theoretical research on "how road structure affects carbon emission" is improved; 3. Full-process decision-making closed loop: from data acquisition to strategy generation, a complete closed loop is formed, the system integrates spatial analysis, carbon emission calculation, modeling and decision-making functions, and does not need to rely on multiple tools for splicing, which greatly improves the work efficiency of planning departments; 4. Strong universality and scalability: the method has strong universality and can be popularized to traffic carbon emission analysis and optimization practice in different cities, and adapts to new needs of future low-carbon traffic planning. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 The flowchart of an embodiment of the present application is shown in the figure; Figure 2 The 1km*1km road grid unit carbon emission distribution map of Shenzhen City in 2020 is shown in the figure. DETAILED DESCRIPTION

[0016] In order to make the person skilled in the art better understand the technical scheme of the present application, the technical scheme in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by the person skilled in the art without creative labor are within the protection scope of the present application.

[0017] Embodiment 1 Taking Shenzhen City, Guangdong Province, China as an example, the method is applied to the correlation analysis of urban traffic carbon emission.

[0018] Step 1: Obtain the road network vector data of the target city through the OpenStreetMap platform and preprocess the road network vector data by using the ArcGIS software to generate the 1km×1km road grid unit including the road network density; based on the preprocessed road network vector data, the line segment model is used to construct the road network topology space model by using the DepthmapX software, and the road section level space syntax index of each road section is calculated, wherein the space syntax index includes integration, selection and connection; Further, the specific method of obtaining the road network vector data of the target city through the OpenStreetMap platform and preprocessing the road network vector data by using the ArcGIS software in step 1 includes: Obtain the road network vector data through the OpenStreetMap platform, wherein the road network vector data includes road geometry data and road attribute data; Project and convert the road network vector data by using the ArcGIS software to convert to the UTM coordinate system; perform topology inspection on the road network vector data to repair road breaks, overlaps and hanging nodes; according to the boundary of the urban built-up area, the road network vector data is cut to retain the standardized road sections within the research scope.

[0019] Further, the calculation formula of the integration of the 1km×1km road grid unit i in step 1 is: Wherein, Indicates the integration of the 1km×1km road grid unit i, d ij is the shortest topological distance between the 1km×1km road grid unit i and the 1km×1km road grid unit j, and n is the total number of 1km×1km road grid units.

[0020] Further, the calculation formula of the selection of the 1km×1km road grid unit i in step 1 is: where Choice i represents the choice degree of 1 km × 1 km road grid cell i; represents the total number of shortest paths from point s to point t, represents the number of nodes passed through in the shortest path from point s to point t.

[0021] Further, the calculation formula of the connectivity of 1 km × 1 km road grid cell i in step 1 is: where Con i represents the connectivity of 1 km × 1 km road grid cell i; a ij represents the direct connectivity of 1 km × 1 km road grid cell i and 1 km × 1 km road grid cell j, which is 1 if connected, otherwise 0.

[0022] Step 2: Spatial overlay analysis of road segment-level spatial syntax indicators and road network density of 1 km × 1 km road grid cells in ArcGIS software, and aggregation to all 1 km × 1 km road grid cells by arithmetic mean method; Specifically, the road network vector data of Shenzhen City from 2019 to 2021 was downloaded from the OpenStreetMap platform, which contains road geometry data and attribute data. In ArcGIS software, the data was projected and converted to UTM coordinate system. Topological inspection was carried out to repair road breaks, overlaps and suspended nodes and other errors. Finally, according to the boundary of the built-up area of Shenzhen City, the road network data was cut to obtain the standardized road network data required by the research.

[0023] Specifically, in ArcGIS software, a regular 1 km × 1 km road grid cell system was generated based on the built-up area of Shenzhen City, and each 1 km × 1 km road grid cell was assigned a unique identifier. Through spatial overlay analysis, each road segment with calculated spatial indicators was spatially associated with the 1 km × 1 km road grid cell it was in. For each 1 km × 1 km road grid cell, the arithmetic mean value of the same type of spatial indicators of all road segments within it was calculated. Through this method, the road network density, integration degree, choice degree and connectivity value of each 1 km × 1 km road grid cell were finally obtained. This aggregation process using the arithmetic mean method converts the originally heterogeneous road structure features based on linear features into homogeneous spatial indicators based on 1 km × 1 km road grid cells, thereby obtaining stable spatial comparability and laying a solid foundation for the next correlation analysis with carbon emission data of the same scale.

[0024] Step 3: Obtain the types of urban transportation fossil energy and the corresponding energy consumption from the urban statistical yearbook, calculate the total carbon emissions of urban transportation; Obtain economic activity intensity from the Earth Resources Data Cloud Platform, and match the total carbon emissions of urban transportation to the 1km×1km road grid unit based on economic activity intensity to generate a 1km×1km road grid unit carbon emission distribution map; Further, the calculation formula of the total carbon emissions of urban transportation in step 3 is: Wherein, C is the carbon emissions; E j is the consumption of the j th fossil fuel; K j is the carbon emission coefficient of the j th fossil fuel.

[0025] Specifically, the energy consumption of different types in the statistical yearbook of Shenzhen from 2019 to 2021 is collected, and appropriate carbon emission coefficients are selected according to the “Provincial Greenhouse Gas Inventory Compilation Guide”. The total annual carbon emissions of Shenzhen transportation are calculated using the formula; To realize the double-layer mapping of the 1km×1km road grid unit of the spatial structure index and the carbon emission spatialization, after calculating the total carbon emissions of urban transportation, the total carbon emissions of urban transportation are distributed to the corresponding 1km×1km road grid unit to generate a 1km×1km road grid unit carbon emission distribution map.

[0026] On the one hand, the road structure characteristics are 1km×1km road grid units, and on the other hand, the total carbon emissions are spatialized, both of which realize accurate spatial alignment and matching in the same set of geographical 1km×1km road grid unit system.

[0027] Step 4: Build a multiple regression model, determine the quantitative relationship between road structure and carbon emissions based on the multiple regression model, and develop a transportation planning scheme.

[0028] Further, the expression of the multiple regression model in step 4 is: Wherein, i is the carbon emissions of the 1km×1km road grid unit i; is the regression constant term; is the road network density of the 1km×1km road grid unit i; is the weight coefficient of the road network density; Con i is the connectivity of the 1km×1km road grid unit i; is the weight coefficient of the road connectivity; Ii is the integration degree of the 1km*1km road grid unit i; is the weight coefficient of the road integration degree; Choice i is the selection degree of the 1km*1km road grid unit i; is the weight coefficient of the road selection degree; is a random disturbance term, satisfying the normal distribution assumption.

[0029] Specifically, taking the data of Shenzhen in 2020 as an example for modeling analysis, the results show that: the road network density has the greatest influence on carbon emissions, with an importance of about 75%; the integration degree is second, with an importance of about 22%, and the influence of selection degree and connectivity is relatively weak. The results and the conclusions of the linear model about the influence direction are mutually verified, and the influence strength of each factor is further quantified.

[0030] Specifically, based on the above analysis conclusion, suggestions are put forward for low-carbon transportation planning in Shenzhen: 1. Density control: High attention should be paid to the strong driving effect of road network density on carbon emissions. Under the premise of ensuring traffic accessibility, structural optimization should be carried out in areas with super high density to avoid traffic congestion and emission increase caused by excessive agglomeration. In the planning of new areas, the road network density should be ensured to reach a reasonable threshold of 8km / km 2 , but attention should be paid to the balance and efficiency of road network structure.

[0031] 2. Structural optimization: For the core area with high integration degree, traffic organization should be optimized, public transportation should be developed, and transit traffic should be relieved to alleviate the carbon emission pressure brought by it. For the edge area with low integration degree, the central nature can be improved by increasing key connecting lines and improving road network connectivity to promote the balanced distribution of traffic flow.

[0032] 3. Efficiency improvement: Although the selection degree directly affects the relatively small one, the negative correlation between it and carbon emissions shows that improving the overall traffic efficiency of the road network and reducing detours and redundant trips has emission reduction potential. The selection efficiency of the entire road network can be improved by optimizing intersection design, perfecting the connection between trunk roads and branch roads, and implementing intelligent traffic management.

[0033] Embodiment 2 The second aspect of the application proposes a city traffic carbon emission correlation analysis system based on space syntax, comprising: The first module is configured to acquire road network vector data of a target city through an OpenStreetMap platform, and to preprocess the road network vector data by using ArcGIS software to generate 1km*1km road grid cells including road network density; based on the preprocessed road network vector data, a line segment model is used to construct a road network topology space model by using DepthmapX software to calculate road section level space syntax indexes of each road section, wherein the space syntax indexes include integration, selection and connection; The second module is configured to perform spatial overlay analysis on the road section level space syntax indexes and the road network density of the 1km*1km road grid cells in the ArcGIS software, and to aggregate all 1km*1km road grid cells by using an arithmetic mean method. The third module is configured to acquire city traffic fossil energy types and corresponding energy consumption by using a city statistical yearbook, to calculate total city traffic carbon emission, and to acquire economic activity intensity by using a geospatial data cloud platform, to match the total city traffic carbon emission to the 1km*1km road grid cells based on the economic activity intensity, and to generate a 1km*1km road grid cellized carbon emission distribution map. The fourth module is configured to construct a multiple regression model, to determine a quantitative relationship between road structure and carbon emission based on the multiple regression model, and to formulate a traffic planning scheme.

[0034] Embodiment 3 The third aspect of the present application provides an electronic device, comprising a memory, a processor and a computer program, the computer program is stored in the memory and is configured to be executed by the processor to realize the method of any one of the embodiments 1.

[0035] The content not described in detail in the specification belongs to the prior art known to those skilled in the art. Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can be in the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0036] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0037] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0038] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0039] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, and not to limit the scope of protection, although the above-mentioned embodiments of the present application are described in detail, those skilled in the art should understand: the skilled person in the art can make various changes, modifications or equivalent replacements to the specific embodiments of the present application after reading the present application, but these changes, modifications or equivalent replacements are all within the scope of protection of the claims of the present application.

Claims

1. A method for analyzing the correlation of urban traffic carbon emissions based on space syntax, characterized in that, include: Step 1: Obtain road network vector data of the target city through the OpenStreetMap platform and preprocess the road network vector data using ArcGIS software to generate 1km×1km road grid cells including road network density; based on the preprocessed road network vector data, construct a road network topology spatial model using the line segment model through DepthmapX software, and calculate the segment-level spatial syntax index for each road segment, which includes integration degree, selectivity degree, and connectivity degree; Step 2: Perform spatial overlay analysis on the road segment-level spatial syntax index and the road network density of the 1km×1km road grid unit in ArcGIS software, and aggregate them to all 1km×1km road grid units using the arithmetic mean method. Step 3: Obtain the types of fossil fuels used in urban transportation and their corresponding energy consumption through the city statistical yearbook, and calculate the total carbon emissions from urban transportation; obtain the intensity of economic activities through the Earth Resources Data Cloud Platform, and match the total carbon emissions from urban transportation to 1km×1km road grid units based on the intensity of economic activities, generating a carbon emission distribution map of 1km×1km road grid units. Step 4: Construct a multiple regression model, determine the quantitative relationship between road structure and carbon emissions based on the multiple regression model, and formulate a traffic planning scheme.

2. The urban traffic carbon emission correlation analysis method based on space syntax according to claim 1, characterized in that, The specific methods for obtaining road network vector data of the target city through the OpenStreetMap platform and preprocessing the road network vector data using ArcGIS software in step 1 include: Road network vector data is obtained through the OpenStreetMap platform, which includes road geometry data and road attribute data. ArcGIS software was used to project and transform the road network vector data into a unified UTM coordinate system; topology checks were performed on the road network vector data to repair road breaks, overlaps, and hanging nodes; and the road network vector data was cropped according to the boundaries of the urban built-up area to retain standardized road segments within the study area.

3. The urban traffic carbon emission correlation analysis method based on space syntax according to claim 1, characterized in that, The formula for calculating the integration degree of the 1km×1km road grid cell i in step 1 is: in, d represents the integration degree of a 1km×1km road grid cell i. ij is the shortest topological distance between 1km×1km road grid cell i and 1km×1km road grid cell j, and n is the total number of 1km×1km road grid cells.

4. The urban traffic carbon emission correlation analysis method based on space syntax according to claim 1, characterized in that, The formula for calculating the selectivity of the 1km×1km road grid cell i in step 1 is: Among them, Choice i This represents the selectivity of a 1km × 1km road grid cell i; This represents the total number of shortest paths from point s to point t. This represents the number of nodes traversed in the shortest path from point s to point t.

5. The urban traffic carbon emission correlation analysis method based on space syntax according to claim 1, characterized in that, The formula for calculating the connectivity of the 1km×1km road grid cell i in step 1 is: Among them, Con i a represents the connectivity of a 1km × 1km road grid cell i; ij This indicates the direct connectivity between 1km×1km road grid cell i and 1km×1km road grid cell j. If they are connected, the value is 1; otherwise, it is 0.

6. The urban traffic carbon emission correlation analysis method based on space syntax according to claim 1, characterized in that, The formula for calculating the total carbon emissions from urban transportation in step 3 is as follows: in, C Carbon emissions; E j For the first j Consumption of various fossil fuels; K j For the first j The carbon emission coefficient of a type of fossil fuel.

7. The urban traffic carbon emission correlation analysis method based on space syntax according to claim 1, characterized in that, The expression for the multiple regression model in step 4 is: Among them, C i The carbon emissions of a 1km×1km road grid unit i; For the regression constant term; The road network density is defined as 1km × 1km road grid cell i. The weighting coefficient for road network density; Con i The connectivity of a 1km×1km road grid unit i; The weighting coefficient for road connectivity; I i The integration degree of a 1km×1km road grid unit i; The weighting coefficient for road integration; Choice i The selectivity of road grid cell i is 1km×1km. This represents the weighting coefficient for road selectivity. It is a random disturbance term that satisfies the normal distribution assumption.

8. A spatial syntax-based urban traffic carbon emission correlation analysis system, characterized in that, include: The first module is used to acquire road network vector data of the target city through the OpenStreetMap platform and preprocess the road network vector data using ArcGIS software to generate 1km×1km road grid cells including road network density; based on the preprocessed road network vector data, the DepthmapX software is used to construct a road network topology spatial model using a line segment model, and calculates the segment-level spatial syntax index for each road segment, which includes integration degree, selectivity degree and connectivity degree; The second module is used to perform spatial overlay analysis of road segment-level spatial syntax indicators and road network density of 1km×1km road grid units in ArcGIS software, and to aggregate them to all 1km×1km road grid units using the arithmetic mean method. The third module is used to obtain the types of fossil energy used in urban transportation and their corresponding energy consumption through urban statistical yearbooks, and to calculate the total carbon emissions of urban transportation; to obtain the intensity of economic activities through the Earth Resources Data Cloud Platform, and to match the total carbon emissions of urban transportation to 1km×1km road grid units based on the intensity of economic activities, and to generate a carbon emission distribution map of 1km×1km road grid units. The fourth module is used to construct a multiple regression model, which is used to determine the quantitative relationship between road structure and carbon emissions, and to formulate traffic planning schemes.

9. An electronic device, comprising: A memory, a processor, and a computer program, characterized in that: the computer program is stored in the memory and configured to be executed by the processor to implement the method of any one of claims 1 to 7.

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