CN110968919B - Road section driving risk state evaluation method based on ArcGIS - Google Patents
Road section driving risk state evaluation method based on ArcGIS Download PDFInfo
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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
Technical Field
The application relates to the technical field of road traffic safety, in particular to a road section driving risk state evaluation method based on ArcGIS.
Background
Along with the development of national economy, the infrastructure construction of China also reaches a certain scale, the road network is of a first scale, but when the road brings people with a quick and efficient travel mode, the road manager and users are bothered by high accident rate and high mortality rate, the vehicle safety problem of a high-risk road section is increasingly prominent, and a set of relatively systematic road driving risk assessment method which can be oriented to the whole world is lacking.
The existing research is mainly used for evaluating the traffic risk depending on the traffic accident or traffic conflict, then the relation between the traffic risk and each influence factor is qualitatively or quantitatively analyzed by adopting methods such as regression analysis, time sequence analysis, system analysis and the like, and finally a relation model between the traffic accident and the influence factors is established. In addition, a road traffic safety evaluation index system or model is established based on BP neural network, gray theory, delphi method, data envelope analysis and the like. However, the above evaluation of the traveling risk ignores the influence of various sources of the traveling risk on the road section, different effects of the sources of the risk and different spatial distribution intensities on the road traffic safety evaluation index, reduces the evaluation accuracy of the traveling risk, and increases the evaluation risk of the traveling risk.
Disclosure of Invention
The application aims to provide a road section driving risk state evaluation method based on ArcGIS, which aims to solve the problem of low evaluation accuracy of the existing driving risk.
In order to achieve the above purpose, the application adopts the following technical scheme:
a road section driving risk state evaluation method based on ArcGIS comprises the following steps:
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.
Further, pile-by-pile risk source data include pile-by-pile flat curve radius R, pile-by-pile longitudinal slope i, and traffic Q.
Further, recoding pile-by-pile risk source data to obtain pile-by-pile flat curve radius code R 1 (m) pile-by-pile longitudinal slope coding i 1 (%) and traffic coding Q 1 (pcu/(h·ln))。
Further, pile-by-pile coordinates and pile-by-pile risk source data are imported into ArcMap software to obtain pile-by-pile coordinate XY data and pile-by-pile risk source XY data displayed on a map.
Further, an Arcmap is adopted to take each road section number (Value) as a line field, so that a road line graph based on each coordinate point set is formed; the width of the road buffer zone is 90-120m on each side of the road.
Further, the NoData value in the risk source raster data converted from the pile-by-pile risk source XY data point set is encoded to be 0, and finally the risk source raster data is output.
Further, the risk sources include plane linearity, vertical section linearity and traffic environment, wherein the intensity of the plane linearity is represented by a pile-by-pile flat curve radius R 1 Representing the strength of the profile of the longitudinal section by pile-by-pile longitudinal slope i 1 Representing the traffic quantity Q for the intensity of the traffic environment 1 And (3) representing.
Further, mountain highway A j The value is 0.5, other highways A j The value is 0.9.
Compared with the prior art, the application has the following beneficial technical effects:
according to the road section driving risk state evaluation method based on the ArcGIS, the road section numbers are given to each road section according to the design speed of the road, piles are arranged, and pile-by-pile coordinates and pile-by-pile risk source data are collected; 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; converting each coordinate point set into risk source grid data, establishing a driving risk model according to road section grid data and risk source grid data, comprehensively considering the comprehensive effect of various risk sources on driving risk states, evaluating road section driving risk levels and suitability, and enabling the result to be more comprehensive and reliable; according to the application, the spatial distribution intensities of various risk sources are different, the road section and the spatial distribution of the risk sources and output results thereof are expressed based on ArcGIS, and the method is simple and easy to operate, and the results are more visual.
The method can be applied to engineering design stage, engineering reconstruction and extension stage or built engineering, can evaluate the running risk states of newly built and built roads, provides theoretical support for the safety guarantee and management of high-risk road sections, perfects a road running risk evaluation system, can assist designers in optimizing road alignment and road line facility design based on ArcGIS, assist the designers in carrying out special design on accident black spot road sections, assist road management staff in reasonably planning road reconstruction and extension engineering, distributing guiding traffic volume, and arranging safety protection and monitoring facilities, and can effectively improve the safety guarantee and safety management level of roads.
Drawings
FIG. 1 is a flow chart of the present application.
Fig. 2 is a road raster pattern.
Fig. 3 is a risk source intensity distribution grid, fig. 3a is a planar linear intensity distribution grid, fig. 3b is a vertical section linear intensity distribution grid, and fig. 3c is a traffic environment intensity distribution grid.
Fig. 4 is a road segment suitability score grid graph.
Detailed Description
The application is described in further detail below with reference to the attached drawing figures:
the technical solutions of the present application will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, shall fall within the scope of the application.
The road section driving risk state evaluation method based on ArcGIS is provided by considering that road section driving risk sources are various, the risk source effects are different and the spatial distribution intensities are different.
As shown in fig. 1, the road section driving risk state evaluation method based on ArcGIS includes the following steps:
(1) According to the design speed of the road, numbering (Value) of each road section, setting pile by pile, and then collecting pile by pile coordinates (X, Y) and pile by pile risk source data of each road section;
specifically, the road section numbers are respectively 1n according to the design speed of the road, wherein the design speed is 120km// h; road section numbers with the design speed of 100km/h are respectively 2n; road section numbers with the design speed of 80km/h are respectively 3n; road section numbers with the design speed of 60km/h are respectively 4n; road section numbers with the design speed of 40km/h are 5n respectively; road section numbers with the design speed of 30km/h are 6n respectively; road section numbers with the design speed of 20km/h are 7n respectively.
The pile-by-pile risk source data comprise pile-by-pile flat curve radius R, pile-by-pile longitudinal slope i and traffic volume Q;
recoding pile-by-pile risk source data to obtain pile-by-pile flat curve radius code R 1 (m) pile-by-pile longitudinal slope coding i 1 (%) and traffic coding Q 1 (pcu/(h.ln)), the specific coding rules are as in tables 1 to 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 into different variables
(2) Establishing a road map coordinate model based on the pile-by-pile coordinates and the pile-by-pile risk source data; specifically, building a road map coordinate model based on pile-by-pile coordinates and pile-by-pile risk source data based on geographic information system software (ArcGIS); specifically, importing 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 a map;
(3) Forming a road line graph according to the pile-by-pile coordinate XY data and the pile-by-pile risk source XY data in the road map coordinate model, and establishing a road buffer area at two sides of the formed road line graph; specifically, an ArcMap is adopted, and road section numbers (values) are used as line fields to form a road line graph based on each coordinate point set; the width of the road buffer zone is 90-120m on each side of the road.
(4) Converting the road buffer vector data obtained in the step (3) into raster data;
(5) Converting the pile-by-pile risk source XY data point set displayed on the map in the step (2) into risk source raster data, encoding a NoData value in the risk source raster data into 0, and finally outputting the risk source raster data;
(6) Establishing a driving risk model according to the road section raster data and the risk source raster data, wherein the grid x of the road section j is subjected to the total risk level D xj The higher the value is, the higher the comprehensive driving risk of the road section is, as shown in formula (1).
Wherein: h represents a risk source;
w h the weight of the risk source is represented, the value range is 0-1, and the larger the value is, the higher the relative driving risk brought by the risk source for all road sections is;
h y representing the intensity of the risk source h in the grid y, wherein the value range is 0-1, and the value is takenThe larger the value is, the higher the risk brought by the risk source is; wherein the strength of the planar line shape is measured by pile-by-pile flat curve radius (R 1 ) The strength of the longitudinal profile is expressed by pile-by-pile longitudinal slope (i 1 ) Indicating the traffic volume (Q 1 ) A representation;
i hxy the effect of grid y representing risk source h on road segment grid x, expressed as 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 sensitivity of the road section to the risk source is represented, the value range is 0-1, and the larger the value is, the more easily the road section is affected by the risk source, so that the driving risk is higher.
(7) Road section driving suitability score grid graph model H based on total risk level suffered by grid x of 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, the value range is 0-1, and the higher the value is, the higher the driving suitability of the road section is;
A j the driving suitability of the road section j is represented, the value range is 0-1, and the higher the value is, the higher the driving suitability of the road section is; mountain highway A j The value is 0.5, other highways A j The value is 0.9;
D xj the higher the value is, the higher the comprehensive driving risk of the road section is, as shown in formula (1);
z=2.5, k is the scale factor of the half saturation function, and its initial value is 0.5.
The following description of the method is made by taking 3 mountain expressways with design speed of 80km/h as an example, and the specific procedures are as follows:
(1) The 3 roads are respectively numbered (Value) as 31,32,33, then pile-by-pile coordinates (X, Y), pile-by-pile flat curve radius (R), pile-by-pile longitudinal slope (i) and traffic volume (Q) of each road section are collected, and recoded into different variables (R 1 、i 1 、Q 1 ) As shown in table 4;
table 4 raw data table collected and built
| Pile number | X | Y | Value | R | i | Q | R 1 | i 1 | Q 1 |
| 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) The original data Table collected and established in step (1) was imported into ArcMap 10.5 software using ArcToolbox-Conversion Tools-Excel To Table function of ArcMap 10.5, and XY data was displayed on the map.
(3) The XY point set is converted into a broken Line road by adopting the ArcToolbox-Data Management Tools-Features-Points To Line function of ArcMAP 10.5, the road section number (Value) is selected as a Line field, and a Buffer zone is established for the road by adopting the Geoporosingsin-Buffer function of ArcMAP 10.5, wherein the Buffer zone width is 100m respectively at two sides of the road.
(4) The road buffer vector data obtained in the step (3) is converted into Raster data by adopting the ArcToolbox-Conversion Tools-To Master-Feature To Raster function of Arcmap 10.5 and is output, and the resolution of the output Raster is 30m, as shown in figure 2.
(5) The XY Point set displayed on the map in the step (2) is converted into risk source grid data by adopting the ArcToolbox-Conversion Tools-To-Point To-Raster function of Arcmap 10.5, and pile-by-pile flat curve radius (R 1 ) Pile-by-pile longitudinal slope (i) 1 ) And traffic volume (Q) 1 ) As a value field, the output grid resolution is 30m. The NoData value in the raster data is then encoded to 0 using the ArcToolbox-Spatial Analyst Tools-Map Algebra-Raster Calculator function of ArcMap 10.5, where the Map algebraic expression is Con (IsNull ("ras"), 0, "ras"), and finally the risk source raster data is output, as shown in fig. 4.
(6) Calculating the comprehensive traveling risk of the road section according to the formula (1), wherein the higher the value is, the higher the comprehensive traveling risk of the road section is:
wherein: h represents a risk source, which is respectively a plane line shape (h=1), a vertical section line shape (h=2) and a traffic environment (h=3);
w h the weights of the risk sources, the plane line shape, the vertical section line shape and the traffic environment are 0.85,0.85,0.7 respectively;
h y representing the intensity of the risk source h in the grid y, and the plane linear intensity is represented by the pile-by-pile flat curve radius R 1 Representing the strength of the profile of the longitudinal section by pile-by-pile longitudinal slope i 1 Representing the traffic quantity Q for the intensity of the traffic environment 1 The value range is 0-1, the value strategies are shown in tables 1-3, and the spatial distribution of the risk source intensity is shown in figure 3;
i hxy the effect of grid y representing risk source h on road segment grid x, expressed as 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 a risk source, wherein the maximum influence distance of the pile-by-pile flat curve radius, the pile-by-pile longitudinal slope and the traffic is 30m;
S jh the sensitivity of road segments to risk sources is represented, and the sensitivity of all road segments to pile-by-pile flat curve radius, pile-by-pile longitudinal slope and traffic is 0.7,0.7,0.6, respectively.
(7) And (3) calculating and outputting a driving suitability score grid chart of the road section according to the formula (2). The driving risk state of the road section is evaluated according to fig. 4, the higher the grid value is, the higher the driving suitability of the road section is, the lower the driving risk is, and the result shows that the driving risk at the K0+450-K0+550, K1+450-K2+650 and K3+000-K3+800 positions is higher (the driving suitability score is smaller than 0.35).
Wherein: j represents different road segments, the values are j=1, j=2, j=3, and x represents the grids of the road segments;
H xj a driving suitability score of the grid x of the road section j is represented and is an output result;
A j the driving suitability of the road section j is represented, and the driving suitability of 3 road sections is 0.5;
D xj the higher the value is, the higher the comprehensive driving risk of the road section is, as shown in formula (1);
z=2.5, k is a scale factor of a half saturation function, its initial value is 0.5, and the correction value is 0.387917.
The method can be applied to engineering design stage, engineering reconstruction and extension stage or built engineering, can evaluate the running risk states of newly built and built roads, provides theoretical support for the safety guarantee and management of high-risk road sections, perfects a road running risk evaluation system, can assist designers in optimizing road alignment and road line facility design based on ArcGIS, assist the designers in carrying out special design on accident black spot road sections, assist road management staff in reasonably planning road reconstruction and extension engineering, distributing guiding traffic volume, and arranging safety protection and monitoring facilities, and can effectively improve the safety guarantee and safety management level of roads.
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.
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