CN113449790A - Mountain trunk highway high-risk road section identification method based on SVM - Google Patents
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Abstract
The invention discloses a mountainous area trunk highway high-risk road section identification method based on SVM, which comprises the following steps: s1, constructing an SVM model based on road alignment parameters; s2, acquiring road alignment parameters of the mountain trunk road, and inputting the road alignment parameters into an SVM (support vector machine) model based on the road alignment parameters to obtain an identification result of the high-risk road section of the mountain trunk road; s3, constructing an SVM model based on psychological parameters of a driver; and S4, acquiring the psychological parameters of the driver when the driver drives on the mountain trunk road, and inputting the psychological parameters into an SVM (support vector machine) model based on the psychological parameters of the driver to obtain the identification result of the high-risk road section of the mountain trunk road. The mountainous area trunk road high-risk road section identification method based on the SVM can effectively identify the high-risk road sections existing in the mountainous area trunk road, and is high in reliability and accuracy.
Description
Technical Field
The invention relates to the field of mountain trunk roads, in particular to a mountain trunk road high-risk road section identification method based on an SVM (support vector machine).
Background
The traffic safety problem of mountain trunk roads is increasingly severe due to special geological conditions of mountainous areas, the linear conditions of mountain trunk roads are poor, the operation of mixed traffic flow is complex, and the roads are more adjacent to water and face cliffs.
At present, the identification of the high-risk road sections of the mountain trunk roads in China mainly focuses on the analysis of accident data, the risk evaluation of traffic infrastructure and the like, and the high-risk road sections of the mountain trunk roads are not effectively identified by fully considering the route shape conditions of the mountain trunk roads and the psychological conditions of drivers.
Disclosure of Invention
In view of the above, the present invention aims to overcome the defects in the prior art, and provides an SVM-based method for identifying a high-risk road section of a highway along a mountain trunk, which can effectively identify the high-risk road section existing in the highway along the mountain trunk, and has the advantages of strong reliability and high accuracy.
The invention discloses a mountainous area trunk highway high-risk road section identification method based on SVM, which comprises the following steps:
s1, constructing an SVM model based on road alignment parameters;
s2, acquiring road alignment parameters of the mountain trunk road, and inputting the road alignment parameters into an SVM (support vector machine) model based on the road alignment parameters to obtain an identification result of the high-risk road section of the mountain trunk road;
s3, constructing an SVM model based on psychological parameters of a driver;
and S4, acquiring the psychological parameters of the driver when the driver drives on the mountain trunk road, and inputting the psychological parameters into an SVM (support vector machine) model based on the psychological parameters of the driver to obtain the identification result of the high-risk road section of the mountain trunk road.
Further, step S1 specifically includes:
s11, acquiring road alignment parameters of a trunk road in a test mountain area, and taking the road alignment parameters as characteristic parameters; the road linear parameters comprise road curve radius, road curve chord ratio and road longitudinal slope;
s12, establishing characteristic vector Data based on road alignment parameters according to the characteristic parametersr(ii) a The Datar＝{x1(i),x2(i)… xm(i) }; wherein x isj(i) The m characteristic parameter on the ith road section is obtained;
s13, determining the label quantity based on the road alignment parameters;
s14, taking the feature vectors and the label quantity as samples, taking alpha% samples as training samples, and taking beta% samples as test samples;
and S15, determining a kernel function, and generating an SVM model based on the road alignment parameters.
Further, a Label quantity Label based on road alignment parameters is determined according to the following formular：
Labelr＝{y1(i),y2(i)}；
Wherein, y1(i) And y2(i) All are safety conditions of the ith road section.
Further, in step S15, the kernel function is a Sigmoid kernel function.
Further, step S3 specifically includes:
s31, acquiring psychogenic parameters of a driver when the driver runs on a highway of a trunk in a test mountain area, and taking the psychogenic parameters as characteristic parameters; the psychogenic parameters comprise heart rate increase rate, SDNN and breathing frequency;
s32, establishing a feature vector Data based on psychological parameters of a driver according to the feature parametersp(ii) a The Datap＝{a1(i),a2(i)… ak(i) }; wherein, aj(i) The kth characteristic parameter when the driver drives on the ith road section is obtained;
s33, determining the label quantity based on the psychological parameters of the driver;
s34, taking the feature vector and the label quantity as samples, taking a lambda% sample as a training sample, and taking a mu% sample as a test sample;
and S35, determining a kernel function, and generating the SVM model based on the psychological parameters of the driver.
Further, the Label quantity Label based on the psychological parameters of the driver is determined according to the following formulap：
Labelp＝{b1(i),b2(i)}；
Wherein, b1(i) And b2(i) Are all safe conditions when the driver is driving on the ith road segment.
Further, in step S35, the kernel function is an RBF kernel function.
Further, the safety conditions include safety and high risk; if the risk perception capability of the driver is larger than the threshold value
Then for safety, if the risk perception capability of the driver is less than a threshold value
It is a high risk;
the risk perception capability of the driver is as follows:
wherein, UijRisk perception for the driver; fijSubjective risk for the driver; f. ofijIs the objective risk of the road; i is a road section number; j is the driver number.
Further, the subjective risk degree F of the driverijThe running speed gradient is adopted for representation; the running speed gradient is as follows:
wherein, Delta IvIs the running speed gradient; delta VFortuneStarting point operation speed and end point operation for road section unitDifference in line speed; l-section unit length.
The invention has the beneficial effects that: the invention discloses a mountainous area trunk highway high-risk road section identification method based on SVM, which is characterized in that an SVM model based on road line shape parameters is constructed by acquiring road line shape parameters of mountainous area trunk highways, an SVM model based on driver physiological and psychological parameters is constructed by acquiring physiological and psychological parameters of drivers when the drivers drive on the mountainous area trunk highways, and whether the mountainous area trunk highway sections are safe or high-risk is effectively identified by using the SVM model corresponding to the parameters according to different acquired parameters.
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The invention is further described below with reference to the following figures and examples:
FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
The invention is further described with reference to the accompanying drawings, in which:
the invention discloses a mountainous area trunk highway high-risk road section identification method based on SVM, which comprises the following steps:
s1, constructing an SVM model based on road alignment parameters;
s2, acquiring road alignment parameters of the mountain trunk road, and inputting the road alignment parameters into an SVM (support vector machine) model based on the road alignment parameters to obtain an identification result of the high-risk road section of the mountain trunk road;
s3, constructing an SVM model based on psychological parameters of a driver;
and S4, acquiring the psychological parameters of the driver when the driver drives on the mountain trunk road, and inputting the psychological parameters into an SVM (support vector machine) model based on the psychological parameters of the driver to obtain the identification result of the high-risk road section of the mountain trunk road. The identification result of the mountain area trunk road high-risk road section comprises mountain area trunk road section safety and mountain area trunk road section high-risk.
The SVM is an English abbreviation of a Support Vector Machine, and is called a Support Vector Machine and a Support Vector network in Chinese. The SVM is a supervised learning model and associated learning algorithm that analyzes data in classification and regression analysis.
The method and the device construct the SVM model based on the road alignment parameters and the psychological parameters of drivers, further realize the identification of the high-risk road sections of the mountain trunk roads, and provide technical support for the identification of the high-risk road sections of the sharp-curved steep slopes in the design stage and the operation management stage of the mountain trunk roads.
In this embodiment, the step S1 specifically includes:
s11, acquiring road alignment parameters of a trunk road in a test mountain area, and taking the road alignment parameters as characteristic parameters; the road linear parameters comprise road curve radius, road curve chord ratio and road longitudinal slope; the sharp-curve steep slope section of the mountain trunk road mainly relates to four scenes of straight lines, single curves, same-direction reverse circular curves and multi-curve combination, and the visual range and the radius ratio of the straight line section cannot be quantized, so that the two indexes are eliminated, and the road curve radius, the road curve chord ratio and the road longitudinal slope are finally selected as road linear parameters;
s12, establishing characteristic vector Data based on road alignment parameters according to the characteristic parametersr(ii) a The Datar＝{x1(i),x2(i)… xm(i) }; wherein x isj(i) The m characteristic parameter on the ith road section is obtained;
s13, determining the label quantity based on the road alignment parameters;
s14, taking the feature vectors and the label quantity as samples, taking alpha% samples as training samples, and taking beta% samples as test samples; the value of alpha is 70, and the value of beta is 30;
and S15, determining a kernel function, and generating an SVM model based on the road alignment parameters. Wherein an SVM model based on road alignment parameters is generated using MATLAB software.
In this embodiment, the Label quantity Label based on the road alignment parameter is determined according to the following formular：
Labelr＝{y1(i),y2(i)}；
Wherein,y1(i) and y2(i) All are safety conditions of the ith road section. Wherein, said y1(i) And y2(i) Is denoted by a number, such as 1 or 2; 1 indicates safety and 2 high risk.
In this embodiment, in step S15, the kernel function is a Sigmoid kernel function; compared with the RBF kernel function and the polynomial kernel function, the Sigmoid kernel function has higher identification accuracy on high-risk road sections under different characteristic parameter combinations.
In this embodiment, the step S3 specifically includes:
s31, acquiring psychogenic parameters of a driver when the driver runs on a highway of a trunk in a test mountain area, and taking the psychogenic parameters as characteristic parameters; the psychogenic parameters comprise heart rate increase rate, SDNN and breathing frequency; wherein the SDNN is the total standard deviation of the global normal sinus R-R interval; the change of the psychological indexes of the driver can effectively represent the reaction of the driver to the external environment risk; when the objective risk of a road is improved, the heart rate of a driver is increased, the breathing of the driver is accelerated, and in order to avoid accidents, the driver adopts the behavior of deceleration braking or steering avoidance to cope with the objective risk, so that many indexes such as the heart rate, the heart rate variability, the breathing amplitude, the breathing frequency and the like can be selected as the characteristic parameters of the SVM model input layer, but too many characteristic parameters can increase the training difficulty of the SVM model and reduce the identification precision of the SVM model, and based on the idea of principal component analysis dimension reduction, the heart rate increase rate, the SDNN and the breathing frequency are finally selected as the characteristic parameters of the SVM model input layer;
s32, establishing a feature vector Data based on psychological parameters of a driver according to the feature parametersp(ii) a The Datap＝{a1(i),a2(i)… ak(i) }; wherein, aj(i) The kth characteristic parameter when the driver drives on the ith road section is obtained;
s33, determining the label quantity based on the psychological parameters of the driver;
s34, taking the feature vector and the label quantity as samples, taking a lambda% sample as a training sample, and taking a mu% sample as a test sample; the value of the lambda is 70, and the value of the mu is 30;
and S35, determining a kernel function, and generating the SVM model based on the psychological parameters of the driver. Wherein, MATLAB software is used to generate SVM model based on psychological parameters of driver.
In the embodiment, the Label quantity Label based on the psychological parameters of the driver is determined according to the following formulap：
Labelp＝{b1(i),b2(i)}；
Wherein, b1(i) And b2(i) Are all safe conditions when the driver is driving on the ith road segment. Wherein, b is1(i) And b2(i) Is denoted by a number, such as 1 or 2; 1 indicates safety and 2 high risk.
In this embodiment, in step S35, the kernel function is an RBF kernel function; compared with a Sigmoid kernel function and a polynomial kernel function, the RBF kernel function has higher identification accuracy on high-risk road sections under different characteristic parameter combinations.
In this embodiment, the safety condition includes safety and high risk; if the risk perception capability of the driver is larger than the threshold value
Then for safety, if the risk perception capability of the driver is less than a threshold value
It is a high risk; wherein the threshold value
The value is 1;
the risk perception capability of the driver is as follows:
wherein, UijRisk perception for the driver; fijThe subjective risk of the driver is divided into 10 points and the value is dividedThe higher the subjective risk perception value of the driver, the higher the risk; f. ofijNormalizing the objective risk degree of the road into a (0,10) score interval according to the speed gradient quantization for the objective risk degree of the road, wherein the higher the score is, the higher the objective risk value of the road section is represented; i is a road section number; j is the driver number.
If U is presentijIf the subjective risk degree of the driver is greater than 1, the subjective risk degree of the driver is greater than the objective risk degree of the road, the objective risk of the road section in the driving process is completely recognized by the driver, and the road section is a safe road section;
if U is presentijIf the risk is less than 1, the subjective risk degree of the driver is less than the objective risk degree of the road, which indicates that the driver does not fully recognize the risk of the road segment, and the possibility of traffic accidents caused by neglecting the risk exists, wherein the road segment is a high-risk road segment.
In this embodiment, the subjective risk degree F of the driverijThe running speed gradient is adopted for representation; the running speed gradient is as follows:
wherein, Delta IvIs the running speed gradient; delta VFortuneThe difference value of the starting point running speed and the end point running speed of the road section unit is obtained; l-section unit length.
The running speed gradient is used as the speed variation in the unit road section to measure the safety of the road section, the defect of single index of running speed harmony evaluation is overcome, and the road section with unfavorable traffic safety can be found out more accurately. According to the evaluation experience of the running speed coordination of the adjacent road sections, when the running speed of the adjacent road sections is accelerated, the influence on the safety is generally small; when the running speed of the adjacent road sections is reduced and the reduction amplitude in a short distance is large, the safety is generally considered to be influenced by the excessive reduction speed.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.
Claims (9)
1. A mountainous area trunk road high-risk road section identification method based on SVM is characterized by comprising the following steps: the method comprises the following steps:
s1, constructing an SVM model based on road alignment parameters;
s2, acquiring road alignment parameters of the mountain trunk road, and inputting the road alignment parameters into an SVM (support vector machine) model based on the road alignment parameters to obtain an identification result of the high-risk road section of the mountain trunk road;
s3, constructing an SVM model based on psychological parameters of a driver;
and S4, acquiring the psychological parameters of the driver when the driver drives on the mountain trunk road, and inputting the psychological parameters into an SVM (support vector machine) model based on the psychological parameters of the driver to obtain the identification result of the high-risk road section of the mountain trunk road.
2. The SVM-based method for identifying the high-risk road section of the mountain trunk road according to claim 1, wherein: the step S1 specifically includes:
s11, acquiring road alignment parameters of a trunk road in a test mountain area, and taking the road alignment parameters as characteristic parameters; the road linear parameters comprise road curve radius, road curve chord ratio and road longitudinal slope;
s12, establishing characteristic vector Data based on road alignment parameters according to the characteristic parametersr(ii) a The Datar＝{x1(i),x2(i)…xm(i) }; wherein x isj(i) The m characteristic parameter on the ith road section is obtained;
s13, determining the label quantity based on the road alignment parameters;
s14, taking the feature vectors and the label quantity as samples, taking alpha% samples as training samples, and taking beta% samples as test samples;
and S15, determining a kernel function, and generating an SVM model based on the road alignment parameters.
3. The SVM-based method for identifying the high-risk road section of the mountain trunk road as claimed in claim 2, wherein: determining a Label quantity Label based on road alignment parameters according to the following formular：
Labelr＝{y1(i),y2(i)}；
Wherein, y1(i) And y2(i) All are safety conditions of the ith road section.
4. The SVM-based method for identifying the high-risk road section of the mountain trunk road as claimed in claim 2, wherein: in step S15, the kernel function is a Sigmoid kernel function.
5. The SVM-based method for identifying the high-risk road section of the mountain trunk road according to claim 1, wherein: the step S3 specifically includes:
s31, acquiring psychological parameters generated when a driver drives on a highway of a trunk in a test mountain area, and taking the psychological parameters generated as characteristic parameters; the psychogenic parameters comprise heart rate increase rate, SDNN and breathing frequency;
s32, establishing a feature vector Data based on psychological parameters of a driver according to the feature parametersp(ii) a The Datap＝{a1(i),a2(i)…ak(i) }; wherein, aj(i) The kth characteristic parameter when the driver drives on the ith road section is obtained;
s33, determining the label quantity based on the psychological parameters of the driver;
s34, taking the feature vector and the label quantity as samples, taking a lambda% sample as a training sample, and taking a mu% sample as a test sample;
and S35, determining a kernel function, and generating the SVM model based on the psychological parameters of the driver.
6. According to claimThe SVM-based mountain trunk highway high-risk road section identification method is characterized in that: determining Label quantity Label based on psychological parameters of driver according to the following formulap：
Labelp＝{b1(i),b2(i)}；
Wherein, b1(i) And b2(i) Are all safe conditions when the driver is driving on the ith road segment.
7. The SVM-based method for identifying the high-risk road section of the mountain trunk road as claimed in claim 5, wherein: in step S35, the kernel function is an RBF kernel function.
8. The SVM-based method for identifying high-risk road segments of mountain trunk roads according to claim 3 or 6, wherein: the safety conditions include safety and high risk; if the risk perception capability of the driver is larger than the threshold value
Then for safety, if the risk perception capability of the driver is less than a threshold value
It is a high risk;
the risk perception capability of the driver is as follows:
wherein, UijRisk perception for the driver; fijSubjective risk for the driver; f. ofijIs the objective risk of the road; i is a road section number; j is the driver number.
9. The SVM-based method for identifying the high-risk road section of the mountain trunk road as claimed in claim 8, wherein: the subjective risk degree F of the driverijThe running speed gradient is adopted for representation; the running speed gradient is as follows:
wherein, Delta IvIs the running speed gradient; delta VFortuneThe difference value of the starting point running speed and the end point running speed of the road section unit is obtained; l-section unit length.
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