CN108710967B - Expressway traffic accident severity prediction method based on data fusion and support vector machine - Google Patents
Expressway traffic accident severity prediction method based on data fusion and support vector machine Download PDFInfo
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Abstract
The invention discloses a highway traffic accident severity prediction method based on data fusion and a support vector machine, which comprises the following steps: 1. collecting l variable factors such as road conditions, driver conditions, vehicle conditions and the like when m traffic accidents occur to form a sample set, and recording the severity value r of each traffic accidenti(ii) a 2. Carrying out dimensionality reduction and normalization on the variable factors of the m collected accident samples; 3. constructing a traffic accident severity prediction model by applying a support vector machine algorithm; 4. and (4) substituting the variable factor vector x of the accident to be predicted after dimensionality reduction into the traffic accident severity prediction model established in the step (3) to obtain a severity prediction result of the accident to be predicted. The method can accurately predict the severity of the highway accident.
Description
Technical Field
The invention belongs to the field of traffic accident analysis and prediction, and particularly relates to a highway traffic accident severity prediction method based on data fusion and a support vector machine.
Background
At present, the analysis of the severity of the accident at home and abroad mainly stays at the level of a single data source and a traditional statistical analysis method, the consideration of influencing factors is less, the analysis is often incomplete, and the model error is larger. As technology advances, data collection is becoming easier. A huge amount of data can be collected about factors that are relevant to traffic accidents, such as road geometry, coil data, weather conditions, road visibility, accident driver conditions, etc. How to control the severity of the accident within a certain range based on the analysis of mass data through a scientific method is an important point to be solved urgently.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a method for predicting the severity of an expressway traffic accident, which can accurately predict the severity of the expressway accident.
The technical scheme is as follows: the invention adopts the following technical scheme:
the expressway traffic accident severity prediction method based on data fusion and a support vector machine comprises the following steps:
(1) collecting l variable factors such as road conditions, driver conditions and vehicle conditions when m traffic accidents occur, and forming a sample set S ═ (S)1,s2,…,sm) Wherein s isi＝(f1i,f2i,…,fli)T，fhiThe h variable factor of the accident numbered i; recording the severity value, r, of each traffic accidentiThe severity value of the accident numbered i, h 1.. l, i 1.. m;
(2) reducing and normalizing the variable factors of the m collected accident samples, and setting a dimension-reduced sample si' for n dimensions, i.e. retaining n variable factors, n<l，si′＝(f1i′,f2i′,…,fni′)T，fki' is a variable factor retained after dimensionality reduction, and k is 1.. n;
the formula of the normalization process is:
xki＝(fki′-MinValue)/(MaxValue-MinValue)
wherein xkiAs a variable factor fki' normalized value, MinValue is { fk1′,fk2′,…,fkm', and MaxValue is { fk1′,fk2′,…,fkmThe maximum value in';
(3) applying a support vector machine algorithm to construct a traffic accident severity prediction model, wherein the model is as follows:
wherein, f (x) is the severity prediction result of the accident to be predicted, and x is the variable factor vector of the accident to be predicted after dimensionality reduction; x is the number ofjFor the j accident sample after dimension reduction normalization, yjThe severity value of the jth accident in the sample set is taken; alpha is alphajCorresponding pull for jth accident sampleThe Greenland multiplier, b is the intercept, and K (·) is the kernel function;
(4) and (3) reducing the dimension of the variable factor vector of the accident to be predicted according to the dimension reduction method in the step (2) to obtain a variable factor vector x of the accident to be predicted after dimension reduction, and substituting x into the traffic accident severity prediction model established in the step (3) to obtain a severity prediction result of the accident to be predicted.
The road conditions include a gradient direction, a flat curve direction, positive or negative represented by 0 or 1, respectively.
The driver condition comprises the age and the sex of the driver; wherein the sex of the driver is represented by 0 or 1 for male or female.
The vehicle condition includes an age of the accident vehicle.
And (3) reducing the dimension of the variable factor sample set S by adopting a principal component analysis method or an independent component analysis method in the step (2).
The kernel function K (-) is a Gaussian kernel function, and the expression is as follows:
where σ is a kernel width adjustment parameter.
Has the advantages that: compared with the prior art, the highway traffic accident severity prediction method based on data fusion and the support vector machine disclosed by the invention has the following advantages: 1. multiple data sources are considered instead of a single accident data source, the multiple data sources can enable the model to be established more accurately, and errors of training and testing are smaller; 2. a support vector machine method is used instead of the traditional statistical analysis method, the traditional statistical analysis method is low in operation speed when multivariable is processed, the nonlinear part is complex, and the logic is unclear. The method for predicting the severity of the highway traffic accident based on the data fusion and the support vector machine can well solve the problems.
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FIG. 1 is an overall control flow diagram of the present invention;
FIG. 2 is a graph of an incident month distribution in the incident data set;
fig. 3 is an age distribution diagram of an accident driver in a vehicle data set.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described below with reference to the accompanying drawings.
The data set adopted by the embodiment is a multi-source data set for accidents, weather, driver conditions and road conditions in 2011 to 2015 years in a certain city. As shown in fig. 1, the method for predicting the severity of a highway traffic accident based on data fusion and a support vector machine comprises the following steps:
(1) collecting l variable factors such as road conditions, driver conditions and vehicle conditions when m traffic accidents occur, and forming a sample set S ═ (S)1,s2,…,sm) Wherein s isi＝(f1i,f2i,…,fli)T，fhiThe h variable factor of the accident numbered i; recording the severity value, r, of each traffic accidentiThe severity value of the accident numbered i, h 1.. l, i 1.. m;
the relevant information of accident occurrence is recorded in the original accident data set and the driver data set respectively, and the road relevant information is recorded in the road information database, and part of the original data is shown in fig. 2 and fig. 3. Firstly, according to records in an original accident data set, fusing a multi-element data set and a database, comprising the steps of data cleaning, classification parameter digitization, variable value conversion, merging sample variables by using parameters common among databases and the like, wherein the steps comprise the following steps:
(1-1) removing irrelevant elements such as 'accident number', 'road pile number', 'road number' and the like in the original accident data set from the sample variables; deleting samples containing '0' and 'unknown' in variables 'weather', 'light' and 'accident type';
(1-2) converting the variable 'gradient direction' and 'flat curve direction' from 'positive/negative' to '1/0'; in the embodiment, the accident severity is divided into 2 levels according to whether a person is injured, namely, the variable 'accident severity' in the original accident data set is binarized, wherein 1 is used for indicating an accident without injury, and 0 is used for indicating an accident with injury; converting the variable 'production year of the accident vehicle' into 'age of the accident vehicle', and concretely converting the formula:
wherein, VehyriIndicating the age of the accident vehicle in the accident occurrence year in the accident numbered i,
val (year) as the year of production of the accident vehiclei) The year of the accident with number i.
(1-3) adding the 'driver age' and 'driver sex' in the driver data set to the accident data set through the label 'accident number', and adding the road related variables to the accident data set through the label 'road number', so that the fusion of the multivariate data is realized.
In the present embodiment, the road condition includes a gradient direction, a flat curve direction, positive or negative represented by 0 or 1, respectively; the driver condition includes driver age, driver gender; wherein the sex of the driver is represented by 0 or 1 for male or female; the vehicle condition includes an age of the accident vehicle.
(2) Reducing and normalizing the variable factors of the m collected accident samples, and setting a dimension-reduced sample si' for n dimensions, i.e. retaining n variable factors, n<l，si′＝(f1i′,f2i′,…,fni′)T，fki' is a variable factor retained after dimensionality reduction, and k is 1.. n;
the formula of the normalization process is:
xki＝(fki′-MinValue)/(MaxValue-MinValue)
wherein xkiAs a variable factor fki' normalized value, MinValue is { fk1′,fk2′,…,fkm' } minimum value, MaxValue is-fk1′,fk2′,…,fkmThe maximum value in';
the variable factor sample set S may be subjected to dimensionality reduction using Principal Component Analysis (PCA) or Independent Component Analysis (ICA).
(3) Applying a support vector machine algorithm to construct a traffic accident severity prediction model, wherein the model is as follows:
wherein, f (x) is the severity prediction result of the accident to be predicted, and x is the variable factor vector of the accident to be predicted after dimensionality reduction; x is the number ofjFor the j accident sample after dimension reduction normalization, yjThe severity value of the jth accident in the sample set is taken; alpha is alphajA Lagrange multiplier corresponding to the jth accident sample, b is an intercept, and K (·) is a kernel function;
in this embodiment, the SPSS modeler is applied to establish a regression analysis model of the support vector machine, including establishing a kernel function, convergence accuracy, and the like. The kernel function K (·) adopts a Gaussian kernel function, and the expression is as follows:
where σ is a kernel width adjustment parameter.
(4) And (3) reducing the dimension of the variable factor vector of the accident to be predicted according to the dimension reduction method in the step (2) to obtain a variable factor vector x of the accident to be predicted after dimension reduction, and substituting x into the traffic accident severity prediction model established in the step (3) to obtain a severity prediction result of the accident to be predicted.
In the embodiment, the data of the highway traffic accident in 2015 is used as a test sample, and the method for predicting the severity of the traffic accident provided by the invention is verified, so that higher prediction accuracy is obtained.
Claims (7)
1. The expressway traffic accident severity prediction method based on data fusion and a support vector machine is characterized by comprising the following steps of:
(1) collecting l variable factors such as road conditions, driver conditions and vehicle conditions when m traffic accidents occur, and forming a sample set S ═ (S)1,s2,…,sm) Wherein s isi＝(f1i,f2i,…,fli)T，fhiThe h variable factor of the accident numbered i; recording the severity value, r, of each traffic accidentiThe severity value of the accident numbered i, h 1.. l, i 1.. m;
(2) reducing and normalizing the variable factors of the m collected accident samples, and setting a dimension-reduced sample siIs n dimension, n<l，si′＝(f1i′,f2i′,…,fni′)T，fki' is a variable factor retained after dimensionality reduction, and k is 1.. n;
the formula of the normalization process is:
xki＝(fki′-MinValue)/(MaxValue-MinValue)
wherein xkiAs a variable factor fki' normalized value, MinValue is { fk1′,fk2′,…,fkm', and MaxValue is { fk1′,fk2′,…,fkmThe maximum value in';
(3) applying a support vector machine algorithm to construct a traffic accident severity prediction model, wherein the model is as follows:
wherein, f (x) is the severity prediction result of the accident to be predicted, and x is the variable factor vector of the accident to be predicted after dimensionality reduction; x is the number ofjFor the j accident sample after dimension reduction normalization, yjThe severity value of the jth accident in the sample set is taken; alpha is alphajA Lagrange multiplier corresponding to the jth accident sample, b is an intercept, and K (·) is a kernel function;
(4) and (3) reducing the dimension of the variable factor vector of the accident to be predicted according to the dimension reduction method in the step (2) to obtain a variable factor vector x of the accident to be predicted after dimension reduction, and substituting x into the traffic accident severity prediction model established in the step (3) to obtain a severity prediction result of the accident to be predicted.
2. The method of claim 1, wherein the road conditions include a slope direction, a flat curve direction, and positive or negative represented by 0 or 1, respectively.
3. The method for predicting the severity of an expressway traffic accident according to claim 1, wherein the driver's condition includes driver age, driver gender; wherein the sex of the driver is represented by 0 or 1 for male or female.
4. The method of claim 1, wherein the vehicle condition comprises an age of an accident vehicle.
5. The method for predicting the severity of an expressway traffic accident based on data fusion and a support vector machine according to claim 1, wherein the principal component analysis is adopted in the step (2) to reduce the dimension of the variable factor sample set S.
6. The method for predicting the severity of an expressway traffic accident based on data fusion and a support vector machine according to claim 1, wherein in the step (2), an independent component analysis method is adopted to perform dimension reduction on the variable factor sample set S.
7. The method for predicting the severity of an expressway traffic accident based on data fusion and a support vector machine according to claim 1, wherein the kernel function K (-) is a gaussian kernel function and the expression is:
where σ is a kernel width adjustment parameter.
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| CN113112794A (en) * | 2021-03-31 | 2021-07-13 | 四川省气象服务中心(四川省专业气象台 四川省气象影视中心) | Traffic accident occurrence rate prediction method based on space-time meteorological grid |
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| CN101271625A (en) * | 2008-04-03 | 2008-09-24 | 东南大学 | Method for detecting freeway traffic event by integration supporting vector machine |
| CN103646534A (en) * | 2013-11-22 | 2014-03-19 | 江苏大学 | A road real time traffic accident risk control method |
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