CN110968919B - Road section driving risk state evaluation method based on ArcGIS - Google Patents

Abstract

The application discloses a road section driving risk state evaluation method based on ArcGIS, which comprises the steps of numbering each road section according to the design speed of a road, arranging piles by piles, and collecting pile by pile coordinates and pile by pile risk source data; establishing a road map coordinate model based on ArcGIS (geographic information system) software by-pile coordinates and pile-by-pile risk source data, forming a road line graph according to each coordinate in the road map coordinate model, and establishing a road buffer area at two sides of the formed road line graph; converting the road buffer vector data into raster data; the method is characterized in that each coordinate point set is converted into risk source grid data, a driving risk model is built according to road section grid data and risk source grid data, comprehensive effects of various risk sources on driving risk states are comprehensively considered, road section driving risk levels and suitability are evaluated, the result is more comprehensive and reliable, the fact that the spatial distribution intensity of various risk sources is different is considered, the road section, the risk sources and the spatial distribution of output results are expressed based on ArcGIS, the method is simple and easy to operate, and the result is more visual.

Description

An ArcGIS-based road section driving hazard evaluation method

Technical field

The invention relates to the technical field of road traffic safety, and in particular to an ArcGIS-based road section driving hazard evaluation method.

Background technique

With the development of the national economy, my country's infrastructure construction has reached a certain scale, and the highway network has begun to take shape. However, while highways bring people a fast and efficient way to travel, they are also plagued by high accident rates and high mortality rates. For highway managers and users, vehicle safety issues on high-risk road sections have become increasingly prominent, but there is still a lack of a more systematic and overall-oriented road driving risk assessment method.

The assessment of driving risks in existing research mainly relies on traffic accidents or traffic conflicts that have occurred, and then uses regression analysis, time series analysis, system analysis and other methods to qualitatively or quantitatively analyze the relationship between driving risks and various influencing factors, and finally establishes the relationship between traffic accidents relationship model between its influencing factors. In addition, there are also studies that have constructed a highway traffic safety evaluation index system or model based on BP neural network, gray theory, Delphi method, data envelopment analysis, etc. However, the above assessment of driving risks ignores the diverse sources of driving risks on road sections, the impact of different risk source effects and different spatial distribution intensities on highway traffic safety evaluation indicators, which reduces the accuracy of driving risk assessment and increases the risk of traffic accidents. Assessing driving risks.

Contents of the invention

The purpose of the present invention is to provide an ArcGIS-based road section driving risk assessment method to overcome the existing problem of low driving risk assessment accuracy. This application can improve the driving risk assessment accuracy.

In order to achieve the above objects, the present invention adopts the following technical solutions:

An ArcGIS-based road segment driving hazard evaluation method includes the following steps:

Step 1): Number each road section according to the design speed of the road and set it up pile-by-pile, and collect pile-by-pile coordinates and pile-by-pile risk source data;

Step 2): Establish a road map coordinate model based on pile-by-pile coordinates and pile-by-pile risk source data based on ArcGIS software;

Step 3): Form a road polyline diagram based on each coordinate in the road map coordinate model, and establish road buffer zones on both sides of the formed road polyline diagram;

Step 4), convert the obtained road buffer vector data into raster data;

Step 5), convert each coordinate point set into risk source raster data;

Step 6): Establish a driving risk model based on the road segment raster data and risk source raster data, and obtain the total risk level of raster x of road segment j:

In the formula: h represents the risk source;

w _h represents the weight of the risk source, with a value ranging from 0 to 1;

h _y represents the intensity of risk source h in grid y, with a value ranging from 0 to 1;

i _hxy represents the impact of raster y of risk source h on raster x, expressed by a linear distance attenuation function,

where d _xy represents the distance between the risk source raster y and the road segment raster x, and d _hmax represents the maximum influence distance of the risk source;

S _jh represents the sensitivity of the road section to risk sources, with a value ranging from 0 to 1;

Step 7): Calculate the sub-grid graph model H _xj of the driving suitability of the road segment based on the total risk level of the grid The higher the sex:

In the formula: j represents different road segments, x represents the grid of road segments;

H _xj represents the driving suitability score of grid x in road segment j, and the value range is 0 to 1;

A _j represents the driving suitability of road section j, with a value ranging from 0 to 1;

Z=2.5, k is the scale factor of the half-saturated function, and its initial value is 0.5.

Further, the pile-by-pile risk source data includes the pile-by-pile horizontal curve radius R, the pile-by-pile longitudinal slope i, and the traffic volume Q.

Furthermore, the pile-by-pile risk source data is recoded to obtain the pile-by-pile horizontal curve radius code R ₁ (m), the pile-by-pile longitudinal slope code i ₁ (%), and the traffic volume code Q ₁ (pcu/(h·ln)).

Further, the pile-by-pile coordinates and pile-by-pile risk source data are imported into ArcMap software to obtain the pile-by-pile coordinate XY data and the pile-by-pile risk source XY data displayed on the map.

Further, ArcMap is used to use each road segment number (Value) as a line field to form a road polyline diagram based on each coordinate point set; the width of the road buffer zone is 90-120m on both sides of the road.

Further, the NoData value in the risk source raster data converted from the risk source XY data point set one by one is encoded as 0, and the risk source raster data is finally output.

Further, the risk sources include plane alignment, longitudinal section alignment and traffic environment. The strength of the plane alignment is represented by the pile-by-pile horizontal curve radius R ₁ , the strength of the longitudinal section line is represented by the pile-by-pile longitudinal slope i ₁ , and the strength of the traffic environment is represented by The traffic volume Q ₁ represents.

Further, the value of A _j for mountain roads is 0.5, and the value of A _j for other roads is 0.9.

Compared with the existing technology, the present invention has the following beneficial technical effects:

This invention is a road section driving hazard evaluation method based on ArcGIS. Each road section is numbered according to the design speed of the road and set up pile by pile, and the pile-by- pile coordinates and pile-by- pile risk source data are collected; based on ArcGIS software, the pile-by- pile coordinates and pile-by- pile risk are collected. The source data establishes a road map coordinate model, forms a road polyline diagram based on each coordinate in the road map coordinate model, and establishes road buffer zones on both sides of the formed road polyline diagram; converts the road buffer vector data into raster data; converts each The coordinate point set is converted into risk source raster data, and a driving risk model is established based on the road segment raster data and risk source raster data. It comprehensively considers the comprehensive effects of multiple risk sources on driving hazards, and the driving risk level and suitability of the road segment. The evaluation has been carried out and the results are more comprehensive and reliable; this invention takes into account the different spatial distribution intensities of multiple risk sources and expresses the spatial distribution of road sections and their risk sources and output results based on ArcGIS. The method is simple and easy to operate, and the results are more intuitive.

The method of the present invention can be applied to the engineering design stage, the engineering reconstruction and expansion stage or the already constructed project, and can evaluate the driving hazards of newly built and existing highways, provide theoretical support for the safety guarantee and management of high-risk road sections, and improve the highway driving risk evaluation system. , the evaluation of driving hazards on road sections based on ArcGIS can assist designers in optimizing road alignment and design of facilities along the road, assist designers in making special designs for accident black spot sections, and assist highway managers in rationally planning road reconstruction and expansion projects, allocating and guiding traffic The amount, deployment of safety protection and monitoring facilities can effectively improve the level of road safety and safety management.

Description of the drawings

Figure 1 is a flow chart of the present invention.

Figure 2 is a road grid map.

Figure 3 is a risk source intensity distribution raster diagram, Figure 3a is a plane linear intensity distribution raster diagram, Figure 3b is a vertical section linear intensity distribution raster diagram, and Figure 3c is a traffic environment intensity distribution raster diagram.

Figure 4 is a raster diagram of road section driving suitability scores.

Detailed ways

The present invention will be described in further detail below in conjunction with the accompanying drawings:

The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts should fall within the scope of protection of the present invention.

The present invention considers that the driving risk sources of road sections are diverse, the risk source effects are different, and the spatial distribution intensity is different, and an ArcGIS-based driving hazard assessment method for road sections is proposed.

As shown in Figure 1, an ArcGIS-based road segment driving hazard evaluation method includes the following steps:

(1) Number each road section (Value) according to the design speed of the road and set it per pile, and then collect the pile-by-pile coordinates (X, Y) and pile-by-pile risk source data of each road section;

Specifically, each road section is numbered according to the design speed of the road. Road sections with a design speed of 120km//h are numbered respectively as 1n; road sections with a design speed of 100km/h are numbered as 2n; road sections with a design speed of 80km/h are numbered respectively. The road sections with a design speed of 60km/h are numbered 4n; the road sections with a design speed of 40km/h are numbered 5n; the road sections with a design speed of 30km/h are numbered 6n; the road sections with a design speed of 20km/h are numbered respectively. The numbers are respectively 7n.

Among them, the pile-by-pile risk source data includes the pile-by-pile horizontal curve radius R, the pile-by-pile longitudinal slope i and the traffic volume Q;

Recode the risk source data on a per-pile basis to obtain a per-pile horizontal curve radius code R ₁ (m), a per-pile longitudinal slope code i ₁ (%) and a traffic volume code Q ₁ (pcu/(h·ln)). Specific coding rules As shown in Table 1 to Table 3:

Table 1 Rules for recoding pile-by-pile flat curve radii into different variables

Table 2 Rules for recoding pile-by-pile longitudinal slopes into different variables

Table 3 Rules for recoding traffic volumes into different variables

(2) Establish a road map coordinate model based on pile-by-pile coordinates and pile-by-pile risk source data; specifically, build a road map coordinate model based on pile-by-pile coordinates and pile-by-pile risk source data using geographic information system software (ArcGIS); specifically, Import pile-by-pile coordinates and pile-by-pile risk source data into ArcMap software to obtain pile-by-pile coordinate XY data and pile-by-pile risk source XY data displayed on the map;

(3) Form a road line graph based on the pile-by-pile coordinate XY data and pile-by-pile risk source XY data in the road map coordinate model, and establish road buffer zones on both sides of the formed road line graph; specifically, use ArcMap to number each road segment (Value) is used as a line field to form a road polyline graph based on each coordinate point set; the width of the road buffer zone is 90-120m on both sides of the road.

(4) Convert the road buffer vector data obtained in step (3) into raster data;

(5) Convert the risk source XY data point set displayed on the map in step (2) into risk source raster data, code the NoData value in the risk source raster data as 0, and finally output the risk source raster data;

(6) Establish a driving risk model based on the road segment raster data and risk source raster data. The total risk level _D .

In the formula: h represents the risk source;

w _h represents the weight of the risk source, ranging from 0 to 1. The larger the value, the higher the relative driving risk that the risk source brings to all road sections;

h _y represents the intensity of the risk source h in the grid y, and the value range is 0 to 1. The larger the value, the higher the risk brought by the risk source; where the intensity of the plane line is measured by the pile-by-pile flat curve radius (R ₁ ) Expressed, the intensity of the longitudinal section line is expressed by pile-by-pile longitudinal slope (i ₁ ), and the intensity of the traffic environment is expressed by traffic volume (Q ₁ );

i _hxy represents the influence of the raster y of the risk source h on the road segment raster x, expressed by a linear distance attenuation function, where d _xy represents the distance between the risk source raster y and the road segment raster x, and d _hmax represents the maximum influence distance of the risk source;

S _jh represents the sensitivity of the road section to risk sources, with a value ranging from 0 to 1. The larger the value, the more susceptible the road section is to the risk sources, resulting in higher driving risks.

(7) Calculate the driving suitability score grid model H _xj of the road segment based on the total risk level of the grid high:

In the formula: j represents different road segments, x represents the grid of road segments;

H _xj represents the driving suitability score of grid x of road segment j, with a value ranging from 0 to 1. The higher the value, the higher the driving suitability of the road segment;

A _j represents the driving suitability of road section j, with a value ranging from 0 to 1. The higher the value, the higher the driving suitability of road section j; the value of A _j for mountainous roads is 0.5, and the value of A _j for other roads is 0.9;

D _xj represents the total risk level of grid x in road section j. The higher the value, the higher the comprehensive driving risk in road section. See formula (1);

Z=2.5, k is the scale factor of the half-saturated function, and its initial value is 0.5.

The following uses three mountain highways with design speeds of 80km/h as examples to illustrate this method. The specific process is as follows:

(1) Number the three roads (Value) as 31, 32, and 33 respectively, and then collect the pile-by-pile coordinates (X, Y), the pile-by-pile horizontal curve radius (R), the pile-by-pile longitudinal slope (i), and Traffic volume (Q), and recode it into different variables (R ₁ , i ₁ , Q ₁ ) according to Table 1-3, as shown in Table 4;

Table 4 Original data table collected and established

Station number X Y Value R i Q R ₁ i _{1 Q1} K0+025 3318335.1 592221.64 31 9999 0.37 1000 0.1 0.2 0.2 K0+050 3318310.6 592216.68 31 9999 0.37 1000 0.1 0.2 0.2 K0+100 3318261.6 592206.75 31 9999 0.37 1000 0.1 0.2 0.2 K0+150 3318212.6 592196.82 31 9999 -1.1 1000 0.1 0.4 0.2 K0+200 3318163.6 592186.89 31 9999 -1.1 1000 0.1 0.4 0.2 K0+232.069 3318132.1 592180.52 31 9999 -1.1 1000 0.1 0.4 0.2 K0+250 3318114.6 592176.95 31 9999 -1.1 1000 0.1 0.4 0.2 K0+300 3318065.7 592166.6 31 1000 -1.1 1000 0.4 0.4 0.2 K0+350 3318017.1 592154.88 31 1000 -1.1 1000 0.4 0.4 0.2 K0+352.069 3318015.1 592154.35 31 1000 -1.1 1000 0.4 0.4 0.2 … … … … … … … … … … K3+450 3315236.8 590903.94 33 9999 2.9 2000 0.1 0.6 0.8 K3+500 3315192.7 590880.44 33 1410 2.9 2000 0.4 0.6 0.8 K3+550 3315149 590856.01 33 1410 2.9 2000 0.4 0.6 0.8 K3+571.380 3315130.6 590845.18 33 1410 2.9 2000 0.4 0.6 0.8 K3+600 3315106.2 590830.26 33 1410 2.9 2000 0.4 0.6 0.8 K3+650 3315064.3 590803 33 1410 2.9 2000 0.4 0.6 0.8 K3+700 3315023.3 590774.28 33 1410 0.5 2000 0.4 0.2 0.8 K3+734.923 3314995.4 590753.37 33 1410 0.5 2000 0.4 0.2 0.8 K3+750 3314983.5 590744.13 33 1410 0.5 2000 0.4 0.2 0.8 K3+800 3314944.7 590712.58 33 1410 0.5 2000 0.4 0.2 0.8

(2) Use the "ArcToolbox-Conversion Tools-Excel-Excel ToTable" function of ArcMap 10.5 to import the original data table collected and created in step (1) into the ArcMap 10.5 software, and display the XY data on the map.

(3) Use the "ArcToolbox-Data Management Tools-Features-PointsTo Line" function of ArcMap 10.5 to convert the XY point set into a polyline road, select each road segment number (Value) as the line field, and use the "Geoprocessing-Buffer" of ArcMap 10.5 "The function establishes a buffer zone for the road. The width of the buffer zone is 100m on both sides of the road.

(4) Use the "ArcToolbox-Conversion Tools-To Raster-Feature toRaster" function of ArcMap 10.5 to convert the road buffer vector data obtained in step (3) into raster data and output it. The output raster resolution is 30m, such as As shown in Figure 2.

(5) Use the "ArcToolbox-Conversion Tools-To Raster-Point toRaster" function of ArcMap 10.5 to convert the XY point set displayed on the map in step (2) into risk source raster data, and select the pile-by-pile flat curve radius ( R ₁ ), pile-by-pile longitudinal slope (i ₁ ) and traffic volume (Q ₁ ) are used as value fields, and the output raster resolution is 30m. Then use the "ArcToolbox-Spatial Analyst Tools-Map Algebra-Raster Calculator" function of ArcMap 10.5 to encode the NoData value in the above raster data as 0, where the map algebra expression is Con(IsNull("raster"),0,"raster"), and finally output risk source raster data, as shown in Figure 4.

(6) Calculate the comprehensive driving risk of the road section according to formula (1). The higher the value, the higher the comprehensive driving risk of the road section:

In the formula: h represents the risk source, which are plane alignment (h=1), longitudinal section alignment (h=2) and traffic environment (h=3);

w _h represents the weight of risk sources. The weights of plane alignment, longitudinal section alignment and traffic environment are 0.85, 0.85 and 0.7 respectively;

h _y represents the intensity of the risk source h in the grid y. The intensity of the plane line is represented by the pile-by-pile flat curve radius R _1. The strength of the longitudinal section line is represented by the pile-by-pile longitudinal slope i _1. The intensity of the traffic environment is represented by the traffic volume Q ₁ . represents, the value range is 0~1, its value strategy is shown in Table 1-3, and the spatial distribution of risk source intensity is shown in Figure 3;

i _hxy represents the influence of the raster y of the risk source h on the road segment raster x, expressed by a linear distance attenuation function, Among them, d _xy represents the distance between the risk source raster y and the road section raster x, d _hmax represents the maximum influence distance of the risk source, and the maximum influence distance of the pile-by-pile horizontal curve radius, the pile-by-pile longitudinal slope and the traffic volume are all 30m;

S _jh represents the sensitivity of the road section to risk sources. The sensitivity of all road sections to the pile-by-pile horizontal curve radius, the pile-by-pile longitudinal slope and the traffic volume are 0.7, 0.7, and 0.6 respectively.

(7) Calculate and output the driving suitability score raster map of the road segment according to equation (2). According to Figure 4, the driving hazard status of the road section is evaluated. The higher the grid value, the higher the driving suitability of the road section and the lower the driving risk. The results show that K0+450-K0+550, K1+450-K2+650, K3+ The driving risk at 000-K3+800 is high (driving suitability score is less than 0.35).

In the formula: j represents different road segments, the values are j=1, j=2, j=3, x represents the grid of road segments;

H _xj represents the driving suitability score of grid x in road segment j, which is the output result;

A _j represents the driving suitability of road section j, and the driving suitability of the three road sections is all 0.5;

D _xj represents the total risk level of grid x in road section j. The higher the value, the higher the comprehensive driving risk in road section. See formula (1);

Z=2.5, k is the scale factor of the half-saturated function, its initial value is 0.5, and the correction value is 0.387917.

The method of the present invention can be applied to the engineering design stage, the engineering reconstruction and expansion stage or the existing projects, and can evaluate the driving hazards of newly built and existing highways, provide theoretical support for the safety guarantee and management of high-risk road sections, and improve the highway driving risk evaluation system. , the evaluation of driving hazards on road sections based on ArcGIS can assist designers in optimizing road alignment and design of facilities along the road, assist designers in making special designs for accident black spot sections, and assist highway managers in rationally planning road reconstruction and expansion projects, allocating and guiding traffic The amount, deployment of safety protection and monitoring facilities can effectively improve the level of road safety and safety management.

Claims (8)

1. The road section driving risk state evaluation method based on ArcGIS is characterized by comprising the following steps of:

step 1), numbering each road section according to the design speed of the road, arranging each pile, and collecting pile-by-pile coordinates and pile-by-pile risk source data;

step 2), establishing a road map coordinate model based on ArcGIS software and pile-by-pile coordinates and pile-by-pile risk source data;

step 3), forming a road line graph according to each coordinate in the road map coordinate model, and establishing road buffer areas on two sides of the formed road line graph;

step 4), converting the obtained road buffer area vector data into raster data;

step 5), converting each coordinate point set into risk source grid data;

step 6), a driving risk model is established according to the road section grid data and the risk source grid data, and the total risk level suffered by the grid x of the road section j is obtained:

wherein: h represents a risk source;

w _h the weight of the risk source is represented, and the value range is 0-1;

h _y the intensity of the risk source h in the grid y is represented, and the value range is 0-1;

i _hxy the effect of grid y, representing the risk source h, on grid x, is represented by a linear distance decay function,

wherein d is _xy Represents the distance, d, between the risk source grid y and the road segment grid x _hmax Representing the maximum influence distance of the risk source;

S _jh the sensibility of the road section to the risk source is represented, and the value range is 0-1;

step 7), calculating a grid-dividing graph model H of the road section driving suitability based on the total risk level suffered by the grid x of the road section j _xj Thus, the driving risk state of the road section is evaluated, and the higher the grid value is, the higher the driving suitability of the road section is:

wherein: j represents different road segments, x represents a grid of road segments;

H _xj the driving suitability score of the grid x of the road section j is represented, and the value range is 0-1;

A _j the driving suitability of the road section j is represented, and the value range is 0-1;

z=2.5, k is the scale factor of the half saturation function, and its initial value is 0.5.

2. The road segment driving risk state evaluation method based on ArcGIS according to claim 1, wherein the pile-by-pile risk source data comprises pile-by-pile flat curve radius R, pile-by-pile longitudinal slope i and traffic volume Q.

3. The road section driving risk state evaluation method based on ArcGIS according to claim 2, wherein pile-by-pile risk source data are recoded to obtain pile-by-pile flat curve radius codes R ₁ (m) pile-by-pile longitudinal slope coding i ₁ (%) and traffic coding Q ₁ (pcu/(h·ln))。

4. The road section driving risk state evaluation method based on ArcGIS according to claim 1, wherein pile-by-pile coordinates and pile-by-pile risk source data are imported into Arcmap software to obtain pile-by-pile coordinates XY data and pile-by-pile risk source XY data displayed on a map.

5. The road segment driving risk state evaluation method based on ArcGIS according to claim 1, wherein ArcMap is used to form a road line graph based on each coordinate point set by using each road segment number (Value) as a line field; the width of the road buffer zone is 90-120m on each side of the road.

6. The road segment driving risk state evaluation method based on ArcGIS according to claim 1, wherein the NoData value in the risk source raster data converted from the pile-by-pile risk source XY data point set is encoded to 0, and finally the risk source raster data is output.

7. The road segment driving risk state evaluation method based on ArcGIS according to claim 1, wherein the risk sources comprise plane line shape, vertical section line shape and traffic environment, wherein the strength of the plane line shape is represented by pile-by-pile plane curve radius R ₁ Indicating, longitudinal section linePile-by-pile longitudinal slope i for strength of shape ₁ Representing the traffic quantity Q for the intensity of the traffic environment ₁ And (3) representing.

8. The road segment driving risk state evaluation method based on ArcGIS according to claim 1, wherein the mountain highway A is characterized in that _j The value is 0.5, other highways A _j The value is 0.9.