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A Study on Traffic Accident Analysis Using Support Vector Machines
Topic Area:
C2 Traffic Operations, Control, & Management
Paper Number:
Authors:
Hironobu HASEGAWA1, Masaru FUJII2, Mikiharu ARIMURA3, Tohru TAMURA4
Title:
A Study on Traffic Accident Analysis Using Support Vector Machines
Abstract: In Japan, the number of traffic accident fatalities has tended to decrease each year since 1992, but the
number of traffic accidents has continued to increase and the number of injuries resulting from traffic accidents
has also remained high. Therefore, measures to prevent traffic accidents are still important despite the decline
in fatalities. When considering traffic safety measures, it is effective to extract dangerous locations with high
fatality and injury accident rates and then analyze the details of the factors involved in such accidents. Due to
numerous factors, however, it is difficult to effectively and efficiently process large quantities of traffic accident
data. For this reason, previous traffic analyses are reviewed, and a Support Vector Machine (hereinafter
referred to as “SVM”), which has become the focus of attention as a data mining method, is chosen. The
SVM is applied to the traffic accident data analysis. The effectiveness of and problems surrounding a SVM
are examined in this study. The classification rate of the SVM toward non-learning data was approximately
70%.
1
Hironobu HASEGAWA
Postgraduate student
Division of Civil and Environmental Eng.
Muroran Institute of Technology
Address: 27-1 Mizumoto, Muroran, Hokkaido,
050-8585, JAPAN
Fax: +81-143-46-5289
E-mail: [email protected]
2
Masaru FUJII
Postgraduate student
Division of Civil and Environmental Eng.
Muroran Institute of Technology
Address: 27-1 Mizumoto, Muroran, Hokkaido,
050-8585, JAPAN
Fax: +81-143-46-5289
E-mail: [email protected]
3
Mikiharu ARIMURA
Docon Co., Ltd.
Address: Atsubetsu-chuo 1-5-4-1, Atsubetsu-ku, Sapporo, Hokkaido,
004-8585, JAPAN
Fax: +81-11- 801-1520
E-mail: [email protected]
4
Tohru TAMURA
Professor & Dr. of Engineering
Dept. of Civil Engineering and Architecture
Muroran Institute of Technology
Address: 27-1 Mizumoto, Muroran, Hokkaido,
050-8585, JAPAN
Fax: +81-143- 46-5288
E-mail: [email protected]
1. Introduction
In Japan, the number of traffic accident fatalities reached a record high in 1970. Thanks to constant
improvements in road structures, vehicles and legal systems for increased driving safety, however, the death
toll has dropped yearly and was reduced by approximately 40% in 2005 as compared with 1970. On the other
hand, the number of traffic accidents, which had been decreasing since 1969, once again began to rise in 1977
and increased 1.3-fold in 2005 as compared with 1969. As a result, the death toll is declining, but the number
of traffic accidents triggering fatal accidents is increasing yearly and traffic accidents are still one of the biggest
problems facing society today. Numerous factors have been considered, including the safety of environments
and vehicles surrounding drivers, driving conditions and more. Among these factors, accidents caused by
drivers or vehicles can be avoided through the development of legal systems, strengthening of controls and
enhancement of vehicle safety. However, an effective and efficient countermeasure is necessary in each case
for accidents caused by driving conditions, which are greatly related to regional characteristics such as weather,
landscape, traffic demand and trip characteristics.
When considering traffic safety measures, it is effective to extract dangerous locations with high fatality and
injury accident rates and then analyze the details of the factors involved in such accidents. Due to numerous
factors, however, it is difficult to effectively and efficiently process large quantities of traffic accident data.
For this reason, previous traffic analyses are reviewed, and a Support Vector Machine (hereinafter referred to as
“SVM”), which has become the focus of attention as a data mining method, is chosen. The SVM is applied to
the traffic accident data analysis. The effectiveness of and problems surrounding a SVM are examined in this
study.
2. Review of previous studies and positioning of this study
Traffic accident analysis methods include those based on crash experiments and those based on statistical
analysis of actual traffic accident data. When examining each detailed factor of an accident and
countermeasures for these factors at accident locations, crash experiment methods have a comparative
advantage. However, these methods entail a great deal of work, and are ineffective in detecting information in
large quantities of data such as unique tendencies and cause-and-effect relationships. They are therefore
unsuitable for comprehensive understanding of factors in traffic accidents and the planning of countermeasures.
For example, it is difficult to discover accidents of the same type using these methods. As a result, statistical
analysis is used for comprehensive classification analysis of large quantities of accident data.
Examples of existing studies using macro data such as integrated traffic accident data include those
conducted by Mori1),2), Goto3), and Hirasawa4) in Japan. Mori et al. analyzed the relationship between traffic
accident countermeasures and reductions in the number of accidents. They attempted to estimate the
occurrence of traffic accidents (accident density) using explanatory variables such as pedestrians, bicycles, and
vehicle traffic2). However, the analysis was conducted in individual categories such as road structures and side
walks, and can therefore not be considered a comprehensive classification analysis. The study with risk maps
by Goto et al. shows risks of traffic accidents that reflect local characteristics. It does not, however, quantify
the relationship between factors and risks of traffic accidents, which can be the key to planning traffic safety
measures. Hirasawa et al. are developing an accident analysis system using GIS as a tool for planning traffic
safety measures. Their system is effective for analyzing data individually and specifically with a focus on
accident locations. Yet, it is still riddled with many problems as an analysis tool for planning of traffic safety
measures, including responses to the same type of accidents in consideration of local circumstances 4).
On the other hand, in the United States, Miao Chong tried to discriminate levels of injury to drivers using
the machine learning approach5). Miao et al. discriminated five classes (i.e. “No Injury,” “Possible Injury,”
“Non-incapacitating Injury,” “Incapacitating Injury” and “Fatal Injury”) using four methods (i.e. “neural
networks trained using hybrid learning approaches,” “decision trees,” “Support Vector Machines” and “a
concurrent hybrid model involving decision trees and neural networks”). As a result, the method of
“concurrent hybrid model involving decision trees and neural networks” showed the best performance in terms
of discrimination, and the adoption of SVM failed. However, the causes behind the failure have not been
examined carefully, and the authors concluded with this short statement, “From an intelligent systems point of
view, it is interesting to note the failure of SVMs in modeling the complexity of different injury classes.”
Accident data accumulated daily is assumed to often be used for aggregative purposes. For example, the
data are sorted by accident type, then shared and used to develop important policies. However, it is obvious
that the quality of accident data is insufficient for the planning of micro accident measures, whereas accident
data can be used to show macro declining trends of traffic accidents.
The purpose behind studying how to use the data mining method for traffic accident analysis is the
preparation of an efficient analysis method for micro accident data such as road structures, implementation
methods and vehicle behavior records at the time of an accident. The SVM used in this study is a two-value
discrimination method based on the Optimal Separating Hyperplane proposed by Vapnik et al. in the 1960s.
Vapnik himself expanded the method to a model capable of being adapted to non-linear classification by
incorporating a kernel function in the 1990s. As a result of this expansion, the SVM has become one of the
best methods with high recognition performance6). High discrimination performance with unknown data
means that the SVM can be used as a basic algorithm for diagnosis models of accident measures. The
objective of this study is to examine and sort out issues related to adapting the SVM to traffic accident analysis.
3. Outline of the SVM
The SVM is a method for forming a discrimination tool for two class patterns using linear threshold elements.
Figure 1 shows the conceptual diagram of the SVM.
x2
H1:f(x)=1
H0:f(x)=0
ξi
xi
ξj
xj
1/ w
H2:f(x)=1
x1
Fig. 1: Conceptual Diagram of SVM
A discriminant function f(x) for distinguishing the data groups
and is required. The f(x) maximizes
both the frontiers (H1 and H2) of areas containing the data and the distance 1 w between hyperplanes
separating the data. If the data can be completely divided into two classes, it is referred to as a hard margin,
and if not, it is referred to as a soft margin. A brief explanation of a soft margin is given below.
First, when the training data composed of N components in xi (i=1~ℓ) and the class level yi[-1or 1] is given,
the problem of soft margin optimization is defined as formula (1).

1 2
w  C  i , w  R N ,ｂ  R ,   R 
w ,
2
i 1
(1)
subject to ｙ i ( w  xi  ｂ )  1   i ,ｉ  1,    , 
minimize
 i  0 ,ｉ 1,    , 
The w is the weight vector against an input in the formula. The dual problem in formula (2) is gained by
introducing Lagrange multipliers  i and  i into the optimization problem (primal problem).
LD ( ) 
maximize


 ｙ
subject to
i
i 1

 i 
i 1
1 
 i jｙ iｙ j xiT x j

2 i , j 1
 0
i
(2)
0   i  C , ｉ  1,    , 
The separation by a curved surface should be considered to enable more complex discrimination.
First, the input datum xi is mapped to the high-dimensional feature space.
x   ( x)  (1 ( x) , 2 ( x) ,    )
(3)
The amount corresponding to the feature space in formula (2) is the value calculated by an inner product of
the vector, which can be substituted by a kernel function. This substitution is referred to as a kernel trick. It
is then applied to formula (2) and the following optimization problem is gained.

maximize W ( )    i 

i 1

subject to
 ｙ
i
i 1
(4)
, 0   i  C , ｉ  1,    , 
 0
i
1 
 i jｙ iｙ j K ( xi , x j ) ,  R 
2 i , j 1
In this case, the bias b can be gained by the formula (5).
(5)
b  ｙ i   jｙ j K ( x j , xi )

jSv
Sv shows a set of vectors and j shows an arbitrary support vector. At last, the discriminant function f(x)
becomes formula (6).
(6)
f ( x)  wT xi  ｂ    jｙ j K ( x j , x)  ｂ 
j  Sv
When the bias b is a fixed value, the equal sign in formula (4) disappears and the problem becomes a
quadric maximization problem. The optimized solution can be gained by updating  in the following
formula using the steepest descent method.

W ( ) 
αi  min Ｃ , max( 0 , αi 
)
αi 

(7)
ω
ηi 
（ 0  ω  2） , ｉ  1,    , 
K ( xi , xi )
The ω shows the convergence ratio. The convergence assessment for the optimization problem is
conducted by comparing object function values of primal and dual problems.
Pr oportion 
Primal object function v alue  - dual object function v alue 
Primal object function v alue   1



αi  2W( )  Cξi
i 1

i 1

α  W( )  Cξ 1
i 1
i

ξi  max 0 , 1  ｙ （i

i 1

αｙ
j 1
j
j
ε
(8)
i

K ( xi , x j )  ｂ）  , (i  1,    , )

4. Traffic Accident Classification by the SVM
(1) Outline of the classification method
Figure 2 shows the outline of the accident analysis flow proposed in this study. First, target accidents are
extracted, then dimensions are aggregated using principal components analysis, and finally major accidents are
classified by the SVM.
Creation of the data set
1.Extraction of target accidents
2.Dimensional aggregation
3.Class labeling
Setting of SVM parameters
•Parameter C of the soft margin
•Parameter r of the GAUSS kernel
Verification of models with unun-learning data
Classification of major accidents
Fig. 2: Accident Classification Flow
(2) Preprocessing of data
a) Traffic accident data
In Japan, the traffic accident statistical data offered by the Institute for Traffic Accidents Research and Data
Analysis are available to the general public (ITARDA data) as aggregated traffic accident data. ITARDA data
include types of accidents and behavior, levels of damage caused by accidents, information on parties involved,
shapes and conditions of roads at accident locations, roadside environments and local characteristics.
b) Creation of a data set
In this study, after all error data have been removed, 149 accidents are extracted from approximately 7,500
accidents that occurred in the Oshima and Hiyama districts of Hokkaido between FY1999 and FY2004, under
the conditions that 1) accidents occurred at intersections of the DID area and 2) perpetrators were age 65 or
older. The data were then set as an example of an SVM discrimination problem. Not all of the items related
to each accident are necessarily accident factors. Therefore, ten items that can be described as factors are
chosen. Furthermore, the factors are reduced by principal component analysis to intuitively understand road
problems (Table 1). Since results of this analysis indicate that the cumulative contribution rate exceeded 60%
before the third principal components, the number of factors is three.
Table 1: Results of principal component analysis
Interpretation of factors and principal components
Principal component 1: Driving environment
Month of accident
Road condition
Ratio of day-and-night on weekdays
Principal component 2: Road structure
Number of lanes
Width of road
Representative width of sidewalks
Extension of median zone setting
Traffic volume for 12 hours during the daytime on weekdays
Principal component 3: Usage characteristics
Designated maximum speed
Mix rate of large-sized vehicles for 12 hours during the daytime on weekdays
Contribution rate (%)
Principal
Component 1
Principal
Component 2
Principal
Component 3
0.003
-0.065
0.046
0.067
-0.319
0.222
-0.587
0.429
-0.325
-0.414
-0.459
-0.470
-0.397
-0.420
-0.398
-0.262
-0.130
0.317
0.218
-0.124
-0.113
-0.078
0.191
-0.01
-0.188
-0.148
35.177
0.568
0.361
13.804
0.407
-0.359
12.170
In this study, two classes are designated as classification classes: major accidents involving fatalities and
serious injuries, and other accidents resulting in minor injuries.
A principal component point gained by principal component analysis is set as the input xi, and the index yi[1,1] is given to each piece of data for classification (Fig. 3).
Major accidents
PCS2
PCS3
0
6
Road
structure
-2
Usage characteristic
2
4
6
Non-major accidents
4
-4
2
0
-2
-6
-4
-6
-6
-4
-2
0
PCS1
Driving
2
4
6
environment
Fig. 3: Distribution of Data
(3) Setting of model parameters
From the data set created in the preceding section, twenty pieces of data (ten data of y = 1 and ten data of y = -1)
are extracted as training data. The convergence ratio, convergence condition and the maximum number of
cycles are set as ω 1.0 ,   10 4 and 8×106 respectively.
In addition, the Gauss kernel is used for the kernel function K ( xi , x j ) in formula (9).
 x  x 2 
i
j
 , ｉ ,ｊ  1,    , 
K ( xi , x j )  exp 
(9)
2


2ｒ


The discrimination performance of the SVM is affected by the parameter C of the soft margin and the
Gauss kernel parameter r. An appropriate value should therefore be calculated using iterative counting. The
best result (C=50 and r=0.1) gained from several trials is used in this test.
(4) Verification of models with non-learning data
129 pieces of data are extracted as verification data from the data set after removing the training data. The
verification data are shown in Fig. 4. The red and blue data in the figure indicate major accidents and nonmajor accidents, respectively.
Major accidents
PCS2
PCS3
0
6
Road
structure
-2
Usage characteristic
2
4
6
Non-major accidents
4
-4
2
0
-2
-6
-4
-6
-6
-4
-2
0
2
4
6
PCS1 environment
Driving
Fig. 4: Distribution of Verification Data
Verification data are classified by the created SVM. Figure 5 presents the classification results. The red,
blue and green data in the figure indicate major accidents, non-major accidents and discrimination thresholds,
respectively.
Major accidents
Non-major accidents
PCS2
PCS3
0
6
Road
structure
-2
Usage characteristic
2
4
6
Discrimination thresholds
4
-4
2
0
-2
-6
-4
-6
-6
-4
-2
0
2
4
6
PCS1 environment
Driving
Fig. 5: Results of data discrimination by the SVM
The classification rate of the SVM for non-learning data is 73%, which means the SVM correctly classified
94 out of 129 example samples. The SVM also classified seven out of ten samples for fatal/major accidents.
5. Conclusion
In this study, we attempted to classify fatal/major accidents and other minor accidents in ITARDA data using
the SVM. This study made it clear that accident data are a mixture of discrete and continuous quantities, and
that the SVM cannot simply be applied. This study was aimed at data from accidents that occurred at DID
intersections and involved elderly persons. After extraction of the data, principal component analysis was
conducted to determine the distribution of accident data, and the data were then converted into continuous
quantities for three axes: driving environment, road structures and usage characteristics. The percentage of
fatal/major accidents is approximately 1% of the sample data set. This percentage is too low for creating a
training data set, so data other than that from fatal/major accidents was divided to create minor accident data
identical in size to that of fatal/major accidents. Multiple training data were then created and a discriminant
function was gained using the SVM. The classification rate of the SVM toward non-learning data was
approximately 70%, and a discriminant function with a classification rate identical to that for the small amount
of fatal/major accident data was also gained. It is possible to predict areas with the possibility of fatal/major
accidents using sensitivity analysis of the discriminant function based on the amount of the principal
component. This is a challenge that will be tackled in the future.
In this study, the SVM is used as a data mining method for existing accident statistical data. The authors
find themselves in a dilemma because even if macro data were to be analyzed, the results could still only be
applied to macro policy planning. Collectable data as continuous quantities, e.g. the feature quantity of an
intersection’s “shape” and speeds of vehicles approaching intersections, are suitable for analysis by the SVM.
Thus, there is a fair possibility that the SVM can be utilized as a data analyzing method in the ITS society.
References
1) Mori and Ikeda: A Study on an Estimation Method of Traffic Accident Reduction Impact by Road Projects,
Conference Material 3-13, pp. 17 - 20, 2003 of the Fourth Road Committee on Advanced Road
Technology
2) Mori, Ikeda and Miyashita: Evaluation of Road Safety Facilities using a Road Traffic Accident Database,
The National Institute for Land and Infrastructure Management, “FY2005 NILIM Research Map,”
(http://www.nilim.go.jp/lab/bcg/siryou/tnn/tnn0253pdf/ks025319.pdf)
3) Goto, Saito, Hirasawa: A study on analysis and evaluation methods of traffic accident risk, FY2005
Research paper collection of Japan Society of Civil Engineers, Hokkaido Branch, No.62, 2006
4) Hirasawa, Takada, Asano: Development of Traffic Accident Analysis Systems, FY2003 Research paper
collection at The 47th Hokkaido Regional Development Bureau Technical Research Presentation Meeting
5) Miao Chong, Ajith Abraham and Marcin Paprzycki: Traffic Accident Data Mining Using Machine
Learning Paradigms, Fourth International Conference on Intelligent Systems Design and Applications,
2004
6) N. Cristianini & J. Shawe-Taylor : An Introduction to Support Vector Machines and Other Kernel-Based
Learning Methods, Cambridge University Press, 2000