a traffic accident predictive model based on neural networks algorithm and rough set theory
TRANSCRIPT
A traffic accident predictive model based on neural networks algorithm
and rough set theory
Qiaoru Li1, 2, a, Liang Chen1, 2, b, Changguang Cheng1, c and Yuexiang Pan1, d 1College of Civil Engineering, Hebei University of Technology, Tianjin, 300401, China
2Civil Engineering Technology Research Center of Hebei, Tianjin, 300401, China
Keywords: traffic accident black spots, prediction model, genetic algorithm, BP neural network, rough set, attribute importance
Abstract. The most important and critical step to improve road traffic safety is prediction and
identification of traffic accident black spot. A new prediction model of traffic accident black spots is
proposed based on GA-BP neural network algorithm and rough set theory. First of all, the traffic
accident statistics of Jinwei Road in Tianjin are analyzed. With consideration of static road
conditions, the samples of road accident black spots are obtained by the GA-BP neural network
algorithm. Furthermore, an effective road traffic accident black spot prediction model is established
by utilizing rough set theory with consideration of the impact of real time dynamic conditions. Finally,
a numerical example is illustrated. Experimental results show that the proposed model with the
combination of these two theories can reduce the hybrid and burdensome amount of data, lower the
false alarm rate and improve the forecasting accuracy of accident black spots.
Introduction
Research and remediation for traffic accident black spots have a significant impact for road traffic
safety. The reason is that: Accident black spots experience a larger proportion of traffic accidents and
greater harm. The traffic accident black spots are related to road alignment, intersection, traffic safety
and traffic environment and other factors. With application of accident black spots targeted
improvement measures, road accident rates can be significantly reduced with great economic and
social benefits. Therefore, the determination of accident black spots has been greatly concerned by the
road and traffic management department.
Accident black spots at home and abroad have been studied for several years. There are a variety of
identification methods and theories exist nowadays. For example, accident rate method, the number
of accidents-accident rate method, quality control methods and so on [1]. In recent years, many new
methods come up, such as cause analysis, dynamic clustering analysis, fuzzy statistical analysis, Grey
relational analysis. In 1997, JS Chen et al. proposed critical rate method to identify the dangerous
sections. The method can determine priority for improving the accident black spots, but with the
development of economy and the improvement of people's living standards, the critical rate is always
changing. In China, prominent factor method [2], causes of correspondence analysis methods [3],
Rough set [4], and BP neural network method [5] were Proposed. However, some important factors
are not considered enough in these methods, especially the impact of dynamic factors. Moreover,
original data are difficult to obtain.
In a word, there are many kinds of models of accident black spots which have some advantages and
disadvantages. Although the GA-BP neural network algorithm and rough set theory model were
respectively used before, there are disadvantages of each method. In this paper, the proposed GA-BP
neural network combined with rough set theory accident prediction model is put forward.
Applied Mechanics and Materials Vols. 97-98 (2011) pp 947-951Online available since 2011/Sep/08 at www.scientific.net© (2011) Trans Tech Publications, Switzerlanddoi:10.4028/www.scientific.net/AMM.97-98.947
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Research methods
Road traffic accidents within a region are studied in this paper. The main influencing factors including
driver (driving experience, driving habits, violation rates, etc.), vehicle (vehicle type, the level of
quality, speed, etc.) and weather can be divided into static factors and dynamic factors. In this study, a
"two stage" prediction method is proposed to predict the accident black spots.
The first stage: BP neural network algorithm based on genetic algorithm. The relationship
between road conditions and accident rates is analyzed with considering of road environment, then
start from the static factors to predict the sample set of road traffic accident black spots. Traffic flow,
number of lanes, road alignment, lane width, horizontal curve radius, road conditions longitudinal and
road intersection are chose as specific index.
The second stage: the rough set algorithm used to select the dynamic factor in the accident black
spots sample set to establish rules for triggering early-warning-level model of the accident. Weather,
vehicle speed, saturation, vehicle type, and lighting situation are selected as specific index.
First, trunk road network is divided into units within the region as intersection, sections of a
highway and special facilities. Then accidents of each unit are recorded with the description of road
geometry, traffic parameters and environmental factors. Finally, the accident history data are analyzed
to establish the accident prediction model.
In the next place, selection of the index of accident black spots. The average number of accidents
occurs each year in one section is considered as a standard measure of the degree of driving risk of the
section. The information of accidents occurring on Jinwei Road is analyzed and concluded. The
classification criterion is that the average number of accidents annually is 3, as shown in Table 1.
Table 1 The relationship between the average annual number of accidents and the levels of road safety
Average annual number of accidents Level of security Safety instructions
(0,3] 0 Security
(3,6] 1 General
(6,9] 2 Unsafe
(9,++) 3 Dangerous
The model based on genetic algorithm and BP neural network (first stage): Neural network
algorithm is essentially an error gradient descent algorithm. For complex nonlinear optimization
problem, there are still defects of the algorithm. Neural network method is just a training ground of
samples. It only practises local optimization without global optimization for all weights before the
training, resulting in slow convergence.
Combining neural network and genetic algorithm as a hybrid GA-BP algorithm optimize the
networks. BP neural network method updates the weights for the steepest descent method. The defect
is that easy to fall into local minimum, slow convergence, therefore, the paper utilizing genetic
algorithms to optimize the initial weights of the training. Training starts with the genetic algorithm
global optimization, narrowing the search. Then using the error back propagation algorithm BP
network training get an accurate weight. Finally, make use of the network generalization ability to
predict the input samples [7]. The model is testified by Tianjin Jinwei Highway Accident data. Jinwei
highway starting point K12+510, ending point K145+963, the total length is 133.453km which can be
divided into 267 units. Three years of road traffic accident data from 2007 to 2009 are investigated. In
this illustration, the section unit length is 0.5km. There are 10 factors that affect the accident rate are
considered, as shown in Fig. 1.
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Fig. 1 Static variable factors and their codes of level
For example, with Jinwei highway road traffic accident statistics data by 2007, 80 group samples
are randomly selected for prediction. The first 70 groups are used as training samples and the last 10
groups are used as the testing samples. The initial “population” M = 70, crossover probability 0.7,
mutation rate of 0.01, terminate evolutionary generation is 400, hidden nodes identified as 8, the
initial weights and the threshold range (-1, l), neural network learning rate is 0.7; the error rate is 0.01
in the sample training process. Prediction accuracy rate is 90%, as shown in Table 2.
Table 2 The predictive results of road accident black spots based GA-BP neural network
Serial number 1 2 3 4 5 6 7 8 9 10
The actual accident rate (times/year) 0 0 0 0 0 8 3 3 0 0
The actual level of security 0 0 0 0 0 2 0 0 0 0
Forecast level of security 0 0 0 0 0 2 1 0 0 0
The model based on rough set algorithm (second stage): In the first phase of the forecasting
process, start with the road environment to analyze the relationship between the outstanding road
environment factors and accident rates in order to predict road traffic accident black spot samples.
The second phase of the work based on the first stage prediction adds off-road factors, and use rough
set theory in the second model, and find the final rule accident model.
According to rough set theory, { }( )aig aC−S as Attribute importance of Attribute a [8], that is:
{ }{ } { } { }
))((
))((1
))((
))(())((
)(
)()( (a)Sig a-C
DPosCard
dPosCard
DPosCard
dPosCardDPosCard
C
CC
C
aC
C
aCC
C
aCC −−−−=
−=
−=
γ
γγ
Number of lanes
Variable name Grade standards Coding standard st
atic
var
iab
le f
acto
rs
One lane
Two lanes
Three or more lanes
1
2
3
0
1
Tianjin to weichang
weichang to Tianjin Road direction
Road alignment Straight
Bending
Clear
0
1
0
1
2
General
Blurred
Road signs
Intersection Exist
None
(0,500m]
1
0
0
1
2
(500m,1500m]
(1500m,++)
Horizontal curve radius
Road longitudinal slope
(0,2%]
(2%,4%],
(4%,++)
0
1
2
0
1
Excellent
Good Pavement quality
Emergency parking
bad
Exist
None
2
1
0
1
0
Exist
None
Nearby buildings
Applied Mechanics and Materials Vols. 97-98 949
The attribute importance expresses the importance of the condition attributes to the decision
attributes under the condition of the current information.
Empirical analysis
The first model built on the resulting of accident black spots, For example, a black point unit
experienced 11 traffic accidents. Dynamic factors of the 11 accidents are analyzed specifically, and
then the cell-road dynamics prominent factors of sections are obtained. Finally, highlight the factors
that determine the occurrence of black spots in the code level. According to the specific circumstances
of the case to determine what is in the accident black spots.
For example, one accident black spots of a collection of eight sections of a typical accident case of
Jinwei highway accident data in 2007, the reasons for preliminary analysis are considered as
conditions set of rough set theory. The dynamic factors are divided into vehicle speeding, light
conditions, weather conditions, vehicles constitute, saturation (traffic), wind speed, the six aspects,
denoted { }654321 ,,,,,C xxxxxx= . Here make the severity of the accident as the decision set,
denoted { }yD = . As shown in Table 3. Table 4 is the decision table.
Table 3 Dynamic variable factors and their codes of level
Number Variable name Grade standards Coding standard
x1 Vehicle speed The speed limit 0
Speeding violation 1
x2 Light conditions
Adequate sunlight during the day
0
light at night 1
No light at night 2
x3 Weather conditions good 0
bad 1
x4 Vehicles pose Small cars 0
medium-sized car 1
x5 Saturation C ≤1 0
C>1 1
x6 Wind speed
0≤Wind level≤3 0
3<Wind level≤6 1
Wind level >6 2
Table 4 Causes of the accident black spot road decision table
Accident number
Condition attributes Decision attribute
x1 x2 x3 x4 x5 x6 y
a1 0 0 1 0 1 1 1
a2 0 2 0 0 1 0 0
a3 0 1 1 1 0 0 2
a4 0 1 1 1 1 1 2
a5 0 0 1 0 0 2 1
a6 1 1 0 0 1 0 1
a7 0 2 1 1 0 0 2
a8 1 2 0 0 1 1 1
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The attributes importance in Table 4 is calculated according to Importance formula. The condition
attributes 54321 ,,,, xxxxx relative to the decision attribute y is more important ,in which the most
important one is breaking the speed limit, The condition attribute 6x (wind speed) is not important to
the decision attribute y, Therefore, it can be omitted.
According to statistical analysis of a large number of accidents, the following rules are made: in the
case of no speeding ( 1x is 0), the decision attribute is 0 and the sum of five codes is 0 or 1 or 2; the
decision attribute is 1 and the sum of five codes addition is 3 or 4; the decision attribute is 2 and the
sum of five codes addition is 5. In the case of speeding ( 1x is 1), the decision attribute is 0 and the sum
of five codes addition is 0 or 1; the decision attribute is 1 and the sum of five codes addition is 2 or 3;
the decision attribute is 2 and the sum of five codes addition is 4 or 5. Decision attribute y is 2 (i.e., the
major accidents may occur) the system will alert.
Based on the above analysis, the unit can be determined as the accident black spots when the
decision attributes is 2 corresponding to condition attributes. That is to say, the road traffic accidents
are likely to happen at this point. Warning must be sent to the road users of this section. There are
many condition attributes, such as vehicle speed, light conditions, weather conditions, driver
composition, traffic volume, vehicles constituted and so on. Obtaining accurate condition attributes
state will have an extremely important influence on accident black spots judgment.
Conclusions
An accident prediction model is developed with the combination of GA-BP neural network and rough
set theory. The combination of the two theories applies to improve the forecasting accuracy of
accident black spots and reduce false alarm. There are many reasons for road accidents. Obviously,
some factors of road traffic accidents have a direct impact, but some of them of the first stage in the
prediction model are difficult to quantify. Therefore, these factors are put into the rough set model in
the second stage analysis. Static prominent factors of road and dynamic prominent factors are
combined to achieve accurate prediction of accident black spots. How to accurately obtain the
condition attributes is extremely important in the model application process. According to the specific
parts of the road conditions, choosing accurate and comprehensive condition attribute factors is the
key to the problem.
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Applied Mechanics and Materials Vols. 97-98 951
Advanced Transportation 10.4028/www.scientific.net/AMM.97-98 A Traffic Accident Predictive Model Based on Neural Networks Algorithm and Rough Set Theory 10.4028/www.scientific.net/AMM.97-98.947
DOI References
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http://dx.doi.org/10.1109/72.265957