CN113919144A - Mountain area highway accident cause analysis model modeling method and storage medium - Google Patents
Mountain area highway accident cause analysis model modeling method and storage medium Download PDFInfo
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Abstract
The invention relates to a mountain area highway accident cause analysis model modeling method and a storage medium, wherein the modeling method comprises the following steps: step 1: acquiring relevant data of mountain expressway accidents; step 2: dividing the mountain expressway into a plurality of homogeneous road sections; and step 3: constructing a sample data set; and 4, step 4: and respectively establishing single-vehicle and multi-vehicle accident cause analysis models by using a negative binomial regression model. Compared with the prior art, the method has the advantages of high accuracy, high reliability and the like.
Description
Technical Field
The invention relates to the technical field of traffic safety management, road safety and road safety assessment, in particular to a mountain area highway accident cause analysis model modeling method and a storage medium.
Background
In recent years, attention has been paid to traffic safety of mountain highways, and particularly to traffic safety problems in extreme weather and special road sections. Taking a certain province as an example, the total mileage of the highway breaks 7000 km, wherein more than half of the highway sections traverse the mountainous area. Comprehensive analysis of the occurrence mechanism of highway accidents in mountainous areas is of great importance, because professionals in road design, highway management and law enforcement can benefit from the information to reduce serious traffic accidents.
The existing accident occurrence mechanism judgment work mostly adopts the total number of accidents for judgment, and fails to consider the space distribution difference and the improvement measure difference among different types of accidents, such as single-vehicle accidents and multi-vehicle accidents. In addition, foreign researches find that the influence factors of single-vehicle accidents and multi-vehicle accidents are different, and suggest that cause analysis models are respectively established to analyze the occurrence mechanism of the accidents instead of establishing a single cause analysis model based on the total number of the accidents.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a mountain area highway accident cause analysis model modeling method and a storage medium with high accuracy and reliability.
The purpose of the invention can be realized by the following technical scheme:
a mountain area expressway accident cause analysis model modeling method comprises the following steps:
step 1: acquiring relevant data of mountain expressway accidents;
step 2: dividing the mountain expressway into a plurality of homogeneous road sections;
and step 3: constructing a sample data set;
and 4, step 4: and respectively establishing single-vehicle and multi-vehicle accident cause analysis models by using a negative binomial regression model.
Preferably, the accident-related data in step 1 includes mountain expressway accident data, road geometry data, traffic operation data, weather data and road surface slip resistance index data.
More preferably, the step 1 further comprises: the highway accidents are divided into single-vehicle accidents and multi-vehicle accidents according to accident forms.
More preferably, the road geometry data comprises road flatness, longitudinal and transversal line shapes, road section types, average gradient, longitudinal gradient length and gradient difference data; the traffic operation data comprises traffic volume, speed limit, average speed and daily average truck proportion data.
Preferably, the step 2 specifically comprises:
and (4) dividing the homogeneous road sections based on the plane, vertical section and cross section linear design data acquired in the step (1).
More preferably, the homogeneous section is specifically: the properties of the plane, the vertical section and the cross section in the road section are consistent.
Preferably, the step 3 specifically comprises:
and (3) extracting the corresponding number of single-vehicle accidents, the number of multi-vehicle accidents, the road geometric characteristic variable, the traffic operation characteristic variable, the weather characteristic variable and the road surface skid resistance index variable according to the plurality of homogeneous road sections divided in the step (3), and constructing a plurality of sample data sets of safety analysis corresponding to the homogeneous road sections.
More preferably, the step 4 specifically includes:
and performing multiple collinearity detection on the selected factors influencing the traffic accidents, and then respectively establishing cause analysis models of the single-vehicle accidents and the multi-vehicle accidents by using the detected factors influencing the traffic accidents by using a negative binomial regression model method.
More preferably, the negative binomial regression model is specifically:
wherein i is the number of each homogeneous road section starting from 1 on the expressway;
the accident prediction value of the road section i is obtained; variable XiFactors affecting traffic accidents; beta is anThe coefficients are corresponding to the variables; alpha is the discrete coefficient of the negative binomial model.
A storage medium is provided, wherein the mountain area expressway accident cause analysis model modeling method is stored in the storage medium.
Compared with the prior art, the invention has the following beneficial effects:
the mountain area expressway accident cause analysis model modeling method is beneficial to comprehensively analyzing the occurrence mechanism of mountain area expressway accidents, establishes mountain area expressway cause analysis models of single-vehicle accidents and multi-vehicle accidents respectively, considers factors such as road geometric data, traffic operation data, weather data and road surface anti-skid index data, and has higher accuracy and reliability compared with a cause analysis model method based on the total number of accidents.
Drawings
FIG. 1 is a schematic flow chart of a mountain highway accident cause analysis model modeling method according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
The purpose of the invention is: a single-vehicle multi-vehicle accident modeling method for a mountain expressway. The mountain expressway is affected by adverse terrain and extreme weather, and has the characteristics of complex road geometry and driving environment and the like. Therefore, the traffic accident is divided into single-vehicle accidents and multi-vehicle accidents according to the accident form by collecting the traffic accident data, the road geometric data, the traffic operation data, the weather data and the road surface anti-skid index data of the mountain highway, the homogeneous sections of the mountain highway are divided based on the road geometric parameters, the matching is carried out according to the section division data, and then the selected independent variable multiple collinearity is tested. And (3) respectively establishing cause analysis models of single-vehicle accidents and multi-vehicle accidents by using the inspected independent variable through a negative binomial regression model method. The modeling method is beneficial to comprehensively analyzing the occurrence mechanism of the mountain expressway accidents, and the mountain expressway cause analysis models of single-vehicle and multi-vehicle accidents are separately established, so that the method has higher accuracy and reliability compared with the cause analysis model method based on the total number of accidents.
A mountain area highway accident cause analysis model modeling method is shown in a flow chart of figure 1 and comprises the following steps:
step 1: acquiring relevant data of mountain expressway accidents;
the method comprises the steps of obtaining mountain area expressway accident data, road geometric data, traffic operation data, weather data and pavement skid resistance index data;
the method comprises the following steps of obtaining accident data through a traffic management department, and dividing accidents into single-vehicle accidents and multi-vehicle accidents according to accident forms; acquiring data of road flatness, longitudinal and horizontal line shapes, road section types, average gradients, longitudinal slope length and slope difference from road design data; acquiring traffic volume, speed limit, average speed and daily average truck proportion data based on traffic detection equipment; acquiring weather data through a weather management department; acquiring pavement skid resistance index data through a traffic management department;
step 2: dividing the mountain expressway into a plurality of homogeneous road sections;
dividing homogeneous road sections based on the plane, vertical section and cross section linear design data acquired in the step 1, wherein the homogeneous road sections specifically refer to road sections with consistent plane, vertical section and cross section attributes in the road sections;
and step 3: constructing a sample data set;
extracting corresponding single-vehicle accident number, multi-vehicle accident number, road geometric characteristic variable, traffic operation characteristic variable, weather characteristic variable and road surface skid resistance index variable according to the plurality of homogeneous road sections divided in the step 3, and constructing a plurality of safety analysis sample data sets corresponding to the homogeneous road sections;
and 4, step 4: respectively establishing single-vehicle and multi-vehicle accident cause analysis models by using a negative binomial regression model;
carrying out multiple collinearity detection on the selected factors influencing the traffic accidents, and then respectively establishing cause analysis models of the single-vehicle accidents and the multi-vehicle accidents by using the detected factors influencing the traffic accidents by using a negative binomial regression model method;
the variance of the number of incidents may be used as an indicator of the discrete distribution. The variance of the number of incidents can be derived using maximum likelihood estimation. When the variance of the number of accidents is equal to 0, the negative binomial distribution is a poisson distribution. If the variance of the accident number is obviously not equal to 0, the accident data is discrete and is not suitable for adopting Poisson distribution. In the research, if the variance of the accident number is larger than the mean value, a negative binomial regression model is established for analysis. The model is as follows:
wherein i is the number of each homogeneous road section starting from 1 on the expressway;
the accident prediction value of the road section i is obtained; variable XiFactors affecting traffic accidents; beta is anThe coefficients are corresponding to the variables; alpha is the discrete coefficient of the negative binomial model.
The embodiment also relates to a storage medium, and any modeling method is stored in the storage medium.
Example (b):
the invention is tested by utilizing five types of data including high-speed real traffic accident data, road geometric data, traffic operation data, weather data and road surface skid resistance index data in a certain mountain area.
According to the steps l to 3 of the invention, the traffic accident is divided into single-vehicle and multi-vehicle accidents according to the accident form by collecting the traffic accident data of the highway in the mountainous area, the road geometric data, the traffic operation data, the weather data and the road surface antiskid index data. In order to ensure that the properties of planes, longitudinal sections and cross sections in each road section unit are consistent, the high-speed two sides of each road section are divided into 489 microscopic homogeneous road sections and numbered. And extracting traffic accident data, road geometric characteristic variables, traffic operation characteristic variables, weather characteristic variables and road surface skid resistance index characteristic variables of each road section, correspondingly combining the extracted data with the number of single-vehicle and multi-vehicle accidents of each road section, and constructing a sample data set for traffic safety analysis. Collecting sample data, and extracting the traffic accident data of 2017 and 2019 years of Guidu high speed, wherein the single-vehicle accident is 886, and the multi-vehicle accident is 1948; the road geometry variables include: "type of section", "length of tunnel where it is located", "type of plane line", "average curvature", "maximum and minimum difference of curvature", "type of vertical curve", "average slope", "length of longitudinal slope", "rate of change of slope", "combination of plane and longitudinal lines"; the traffic operation characteristic variables include: "daily average traffic volume", "left lane flow ratio", "average speed of big and small vehicles", "average speed of left and right lanes", "standard deviation of section speed", "daily average truck ratio", "weekday average truck ratio", "weekend and daily average truck ratio", "holiday and holiday average truck ratio"; the weather characteristic variables include: "clear day ratio", "rainy day ratio", "fog day ratio", "snow day ratio", "cloudy day ratio"; the road surface antiskid index characteristic variables comprise: road surface Skid Resistance Index (SRI), Skidding Resistance Index.
According to the step 4 of the invention, a cause analysis model of the single vehicle accident is established, and the marginal effect of the single vehicle accident model is analyzed, as shown in tables 1 and 2; and (3) establishing a cause analysis model of the multi-vehicle accident, and analyzing the marginal effect of the multi-vehicle accident model, as shown in tables 3 and 4.
TABLE 1 estimation of parameters of a bicycle accident model
TABLE 2 analysis of side effects of single-car accident model
TABLE 3 Multi-vehicle accident model parameter estimation results
TABLE 4 marginal effect analysis of multiple vehicle accident model
The optimal negative bivariate regression model parameter estimation results and marginal effect analysis of the single-vehicle accidents under the homogeneous method are shown in tables 1 and 2, and the road section type, the average gradient, the gradient change rate, the road surface anti-skid performance index, the road section length and the average daily traffic volume are obvious. The single-vehicle accident risk of the interchange road section in the road section type is 2.18% higher than that of the common main line, and the single-vehicle accident risk of the tunnel road section is 0.40% lower than that of the common main line; the single-vehicle accident rate of the downhill road section is more than that of the uphill road section; the number of the single-vehicle accidents is reduced along with the increase of the gradient change rate; the number of the single-vehicle accidents is reduced along with the increase of the road surface anti-skid performance index; the number of road section accidents is positively correlated with the length of the road section; the number of single car accidents decreases as the average daily traffic increases.
The optimal negative bivariate regression model parameter estimation results and marginal effect analysis of multiple accidents under the homogeneous method are shown in tables 3 and 4, and the road section type, the average gradient, the average speed, the fog day proportion, the road section length and the average daily traffic volume are obvious. The multi-vehicle accident risk of 200m outside the tunnel entrance, the middle part of the tunnel and the interchange main line section is 0.78%, 0.44% and 1.04% higher than that of the common main line, and the multi-vehicle accident risk of 100m outside the tunnel exit is 0.42% lower than that of the common main line; the number of accidents of multiple vehicles is increased by 0.03% when the average speed is increased by 1 km/h; the single-vehicle accident rate of the downhill road section is more than that of the uphill road section; the number of the multiple-vehicle accidents increases along with the increase of the proportion in the foggy day; the number of the multiple-vehicle accidents is positively correlated with the length of the road section; the number of multiple car accidents increases with the average daily traffic.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A mountain area highway accident cause analysis model modeling method is characterized by comprising the following steps:
step 1: acquiring relevant data of mountain expressway accidents;
step 2: dividing the mountain expressway into a plurality of homogeneous road sections;
and step 3: constructing a sample data set;
and 4, step 4: and respectively establishing single-vehicle and multi-vehicle accident cause analysis models by using a negative binomial regression model.
2. The mountain highway accident cause analysis model modeling method according to claim 1, wherein the accident related data in the step 1 comprises mountain highway accident data, road geometry data, traffic operation data, weather data and road surface slip resistance index data.
3. The mountain area highway accident cause analysis model modeling method according to claim 2, wherein the step 1 further comprises the following steps: the highway accidents are divided into single-vehicle accidents and multi-vehicle accidents according to accident forms.
4. The modeling method of the mountain expressway accident cause analysis model according to claim 2, wherein the road geometry data includes road flatness, longitudinal and transversal alignment, road section type, average slope, longitudinal slope length and slope difference data; the traffic operation data comprises traffic volume, speed limit, average speed and daily average truck proportion data.
5. The mountain area highway accident cause analysis model modeling method according to claim 1, wherein the step 2 specifically comprises the following steps:
and (4) dividing the homogeneous road sections based on the plane, vertical section and cross section linear design data acquired in the step (1).
6. The mountain area highway accident cause analysis model modeling method according to claim 5, wherein the homogeneous section specifically comprises: the properties of the plane, the vertical section and the cross section in the road section are consistent.
7. The mountain area highway accident cause analysis model modeling method according to claim 1, wherein the step 3 specifically comprises:
and (3) extracting the corresponding number of single-vehicle accidents, the number of multi-vehicle accidents, the road geometric characteristic variable, the traffic operation characteristic variable, the weather characteristic variable and the road surface skid resistance index variable according to the plurality of homogeneous road sections divided in the step (3), and constructing a plurality of sample data sets of safety analysis corresponding to the homogeneous road sections.
8. The mountain area highway accident cause analysis model modeling method according to claim 1, wherein the step 4 specifically comprises:
and performing multiple collinearity detection on the selected factors influencing the traffic accidents, and then respectively establishing cause analysis models of the single-vehicle accidents and the multi-vehicle accidents by using the detected factors influencing the traffic accidents by using a negative binomial regression model method.
9. The mountain area highway accident cause analysis model modeling method according to claim 8, wherein the negative binomial regression model is specifically:
wherein i is the number of each homogeneous road section starting from 1 on the expressway;
the accident prediction value of the road section i is obtained; variable XiFactors affecting traffic accidents; beta is anThe coefficients are corresponding to the variables; alpha is the discrete coefficient of the negative binomial model.
10. A storage medium, wherein the storage medium stores the mountain highway accident cause analysis model modeling method according to any one of claims 1 to 9.
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Application publication date: 20220111 |