EVALUATION OF PARTICLE SWARM OPTIMIZATION ALGORITHM IN PREDICTION OF THE CAR ACCIDENTS ON THE ROADS: A CASE STUDY

International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.4, August 2013

DOI:10.5121/ijcsa.2013.3401 1

EVALUATION OF PARTICLE SWARM

OPTIMIZATION ALGORITHM IN PREDICTION OF THE CAR ACCIDENTS ON THE ROADS: A

CASE STUDY

Farhad Soleimanian Gharehchopogh

1, Zahra Asheghi Dizaji

2, and Zahra Aghighi

3

1Department of Computer Engineering, Urmia Branch, Islamic Azad University, Iran

[email protected] 2,3

Department of Computer Engineering, Science and Research Branch, IslamicAzad

University, West Azerbaijan, Iran.

[email protected], [email protected]

ABSTRACT

Road traffic accidents are the most common accidents that annually Endangers lives of many people in the

world. Our country Iran is one of the countries with highest incidence and mortality due to accidents that

has been introduced. So it’s requires identification of underlay in dimensions in this field. Due to the

increasing amount of car accidents in order to increase volume of information related to car accidents and

needs to explore and reveal hidden dependencies and very long time among this information. So using

traditional methods to discover these complex relations don't response between involved factors and we

need to use new techniques. Considering that main aim of this paper is to find best relationship between

volumes of information in shortest time. So, in this paper, we classify accidents in West Azerbaijan

province in Iran by accident type (damage, injury, death) and we describe it by using Particle Swarm

Optimization (PSO) algorithm.

KEYWORDS

Particle Swarm Optimization, prediction of traffic accidents, Data Mining, classify by type of Accident,

machine learning

1. INTRODUCTION

Accidents are important part of daily life disasters take the lives of many people. Today, the issue

of economic and social costs of accidents and traffic fatalities because of that is the base problem

where challenges the transportation Specialists. This is more important for developing countries,

since, number of road accidents in developing countries is increasing and direct (such as medical

expenses caused by accidents and accident disability care) and indirect costs (such as mental

health problems and depression in family members, loss of the active labor force permanently or

temporarily) is more in comparison to developed countries. Injuries and damages caused by road

accidents is important and significant matter, which unfortunately is usually ignored. Public

health of society is a challenge that demands coordinated and integrated efforts and acts for

effective and continual prevention.

Accident includes errors and defects in function in one of these four factors:human, road,

environment around road and vehicle, While each of these four factors carry out their


2

responsibilities correctly and be impeccable, Probability of accident happens nears to zero, But

when at least one of these four factors find a failure, any bad event are not unexpected. Among

themain elements ofan accident, human is the intelligent one, so that, in addition to its role as an

agent, controlling two other factors role in incidence of accidents is also possible by him [1-2].

Therefore, any accident happens, all judgments is facing to human factor, even if the other two

pillars of the vehicle and road cause an accident to happen. This subject is very important that

repeatedly through the mass media and road and safety officials of country to have 70% share of

human error in incidence of accidents is referred in country [3]. In Figure (1) the chain of

significant factors that caused incident or accident is shown.

Figure(1): a chain of affecting factors of the incidence of accident [1]

Considering thecomplexity of accidents and probability ofits occurrence and also the multiple

involved factors and heterogeneous in this field, we see a different data products which each of

them made from various sources. In order toexploitthelarge numberof data and produced data we

need to connect this data and their hidden available patterns. Conventional methods do not have

feature analysis and detailed, significant and important information extraction in accident analysis

and probability of its occurrence. In this context it is necessary to use the information theory and

data analysis, one of new methods in large and various data analysis is using data mining

techniques.[4-5] One Data bases that contains complete information about the subject of complex

factors possibly associated with accidents, are 114forms. 114form is a type of form that is

completed by the police during a road accident in which a number of important factors and

information about the crash occurred is mentioned in the form .This information's include: This

information includes: type of accident, type of approach, the effective vehicle accidents, human

factors affecting the accidents, the total result of accidents, way affecting faults, In this paper, we

have used a method for prediction of PSO in road accidents based on the data. The rest of the

paper is organized as follows: section2 reviews the work done in this field, the proposed method

is presented in section 3, the proposed method is evaluated in section 4, and conclusions is

presented in section 5.

Human

Feature, behavior-skills

and habits, Disability,

mental status,…

Vehicle

Segmentstechnical fault

Safety, Convenience

Road

Geometric properties،

Road surface condition،

...Traffic

Environment

Weather condition,light

radiation status,…

Accident


3

2. PREVIOUS WORKS

Accident data analysis using data mining techniques have been considered by many researchers

[5-6-7-8-9], used techniques in most of this studies is performed by using predictive analysis

means that the set of inputs is a particular output And predictive analysis to explore the

development of a set of observations or variables based on the similarity or lack of similarity

between these sets. Brijsest applied Search association rules to identify and distinguish high-risk

and low-risk areas, traffic patterns. In this paper, a technique were used for classifying a set of

association rules based on data accident and features of the site that had been divided into black

and white points. Analysis shows that association rules facilitate the identification of events that

often occur together that this will lead to a better understanding of road accidents. The results also

show that using the connect algorithm in addition to analysis possibility accident patterns in high-

risk areas, give the possibility of finding features that lies between low-risk and high-risk areas

which are Discriminatory. The most important results show that although human behavioral

characteristics and role plays an important role in traffic accidents, but accidents in the high risk

areas are not affectless. The main difference in accident patterns between high-risk and low-risk

areas can be expressed in terms of infrastructure or location. It should be noted that this is a

(Belgium) case study [10].

Marukatat used Adaptive Regression Trees to build decision support systems that handle Road

accident analysis [11]. In these studies, classifying, selecting and filtering data, using rules

structure analysis has been done, so rule-based structure analysis of various parameters is done

many times. Based on the results we identified candidate rules. Researchers [12] were studied

cause of occurred accidents using association rules algorithm based on PSO algorithm. In this

paper, the concept of the association entropy is used to compare the characteristics of accidents

and also T-test model and Delphi method [12] to test the health of enhanced algorithm. In this

paper, an algorithm on a database of more than twenty thousand cases, each with 56 features was

tested. The end result showed that the improved algorithm is accurate and stable.

Halim Ferit Bayata et al. were employed Artificial Neural Networks (ANN) to model traffic

accidents. In these studies, the best model according to Akaike statistical criteria, select and

statistical analysis were tested in error condition. It should be noted that this is a case study (in

Turkey) [13]. According to accomplished studies by researchers [14] intensity of driving injuries

in accidents examined by using ANN s and decision trees. In these studies, according to the

results of the model a decision tree method was introduced better than ANN method, and

according to analysis three important factors were identified as follows: non-use of safety belts,

road lighting conditions and use of alcohol.

3. PROPOSED METHOD:

3-1. PSO Optimization Algorithm

PSO algorithm, the first time introduced in 1995 by Eberhart and Kennedy, is inspired by mass

movement of birds that are looking for food. In the first step of the algorithm, a random value is

generated for each particle, in this problem a Set of random values generated for the involved

factors in this type of accident then the fitness value is calculated based on the values which in

this problem fitness value is calculated by comparing random values with available facts (training

data).Now if the value of the fitness function is against zero, values for each particle is updated

from two optimum value, the optimal values are obtained based on the specified values for

involved factors in types of accidents .The first optimum value is the best position that the particle

is successful in reaching to it until now (The best values for the involved parameter sin type of


4

accident in particle itself|), this situation is called best and maintained. The Second optimal value

is used by the algorithm, is the best position ever achieved by a particle population (Best values

for the involved parameters in the accidents types among all particles). This situation is displayed

by gbest. After finding the optimal values, speed and position of each particle is updated by using

(1) and (2) equation.Then based on updated values since fitness function value is not zero or a

number of steps are not finish the cycle continues.

Right side of equation(1) is composed of three: the first parts is the current speed of particle and

second and third are the particle speed change and its rotation toward individual experience and

the best experience of group. If we do not consider the first part of this equation is determined

according to the current position and the best experience and the best experience of group. Thus

the best particles gathered and remain fixed in its situation and the others moving to toward that

particle. In fact, the collective motion of particles without the first part of equation(1), will be the

process which because of that search space gradually comes small, and local search forms around

best particle. In contrast, if we only consider the first part of equation (1) particles go their routine

way until reach to the walls and they do national search.

Equation(1) vi,j(t+1)=wvi,j(t)+c1r1,j(t)(pbesti,j(t)-xi,j(t))+c2r2(t)(gbesti(t)-xi,j(t))

Equation (2) xi(t+1)=xi(t)+vi(t+1)

W: represents the inertia weight, the standard value is 1for local searches considers low value and

for public searches considers high value.

r1, r2: usually is between [0, 1].

C1, c2: are learning coefficients to control the pbest and gbest. The sum of these two numbers

should not be more than 4. Decreases1and increase c2 is a cause to moving toward optimum

particle, and as a result search space becomes smaller. Equation (2) is for getting a place for

particle that it will be transferred there. The right side of the equation consists of two parts: the

first part is the current location of the particle and second part is Particle momentum.

In this paper, we by using PSO algorithm that is a sample of evolutionary algorithms, we tried to

classify the accidents by type of it. Process of this prediction by assuming this the data collected

and additional data are omitted is shown in Figure 2.

Part 1 Part 3

Part 2

Part 1

4

Part 2


5

As shown in Figure2 in first step processing data read by the model then based on need number of

data is considered randomly as training data. The rest of data used as test data to evaluate the

proposed method. After determining the training data based on this information for each and

every particle a series of laws defined and then based on this Initialization the fitted function of

each particle is calculated and chosen the best bit.PSO algorithm finishes by finishing number of

irritations or obtaining the desired criteria. Finally by law using samples of test data examined

and error percentage is determined. The main steps of the model are shown in Table (1).

Figure( 3)- PSO algorithm

Figure( 2)- The proposed method

Y

es

N

o

Read pre-processed

data

Evaluation

of training

data

The data set used for

testing and training

Send training data for

PSO algorithm to

learn

Number of correct

answers

Assessment results

and determine the

percent error

Calculate

new values

Initialization

Start

Fitness Calculate

Experiences updates

You have

reached the

desired criteria?

Finish

Yes

N

o


6

Table (1): The simulated proposedmethod

4. EVALUATION AND ASSESMENT

Examines data of this paper is related to accidents taking place in West Azerbaijan province axis

in a period of the first 6 months of 1390. The received data is from the Transportation and

Terminals organization West Azerbaijanas 114com forms. After an initial preprocessing of the

data some information extracted from this information. The results of these studies indicate that

among the four main causes of accidents Human factors, environmental factors, and the way the

vehicle), the influence of human factors, environmental factors, and the way is far greater than the

vehicle. In Figures (4-5-6-7-8-9-10-11) displayed result of the accidents data analysis.

Figure (4)-climate and its relationship with accidents

1-Ds=read data from dataset

2-Train_ds=get many random record for training

3-Exam_ds=get many random record for examine

Rules=Get best rules that output of pso algorithm

5-Result= Evaluated according to the rules

6-Review and evaluate result

4-Use pso algorithm for train with train_ds

1. Set the swarm size. Initialize the velocity and the position of each particle randomly.

2. For each j, evaluate the fitness value of xj and update the individual best position pbest if

better fitness is found.

3. Find the new best position of the whole swarm. Update the swarm best position gbest if

the fitness of the new best position is better than that of the previous swarm.

4. If the stopping criterion is satisfied, then stop.

5. For each particle, update the position and the velocity according (1) and (2). Go to step

2.


7

Most accidents occurred on Clear days. But according to the frequency of accidents occurring and

a period of time, actually the most of fatal crashes occurred on rainy and snowy days.

Figure(5) –Member of the site and its relationship with accidents

Considering that production of travel around agricultural and residential land are much so rates of

fatal crashes around these sites are higher too.

Figure(6) –type of lineation and its relationship with accidents

Due to the lack of Non-compliance of precedence laws by drivers on the road, especially in

"lineation places" the amount of fatal crashes at locations with drawn and cross lineation is more.


8

Figure (7) - type of the shoulder and its relationship with accident

Considering that the majority of vehicles on the parts of road which have soil shoulder,

passengers get in or get out so The rate of fatal crashes is more in this places

Figure(8) –lighting Status and its relationship with accidents

The figure (8) revealed that most accidents happened during the day.


9

Figure (9) - treatment and its relationship with accidents

Most accidents happen at axis as a form of a dealing with another vehicle which figure (9) is also

confirmed this.

Figure(10) -Human factors to blame for the accident and its relationship with accidents

The human factor is one of the three major accident occurrence, As you can see drivers hurry that

has the highest proportion of deaths from traffic accidents


10

Figure (11) –Cause and its relationship with accidents

According to the results, the main reason for most accidents is, "non conformity of Priority” and

"inattention to up ".

After performing data preprocessing, PSO algorithm used for extract behavior and information

dependency. Using this algorithm, based on the available data, we can have accurate prediction

based on the type of happened accident. The results of the implementation of PSO algorithm are

shown in Table (2).

Table (2)–Resultsontraining data

Percentage Numbers Precision

%89 130 Data that classified correctly

%11 15 Data that classified incorrectly

%80 -Kappa percentage

%80 579 Training data

%20 145 Testing data

724 Whole data

5. CONCLUSION AND FUTURE WORK

ANNs are suitable tools to adapt, learning and classification information. Collective intelligence

and neural networks can be regarded as a response to the challenge. In this paper, we investigated

using PSO algorithm to classify the accident based on their kind, considering the accident

occurrence in terms of significant parameters. According to performed simulations on the input

data (724 counts of accidents taking place in West Azerbaijan Province in Iran) using the

MATLAB tool for prediction results on kind of accidents were obtained with 89% Precision.

Based on these results, PSO algorithm can be used in software and future statistical program for

various data mining.


11

6. ACKNOWLEDGEMENTS

We take this opportunity to thank staff members of West AzerbaijanTransportation and Terminals

Organization for the valuable datasets presented by them in their respective fields. We grateful

for their cooperation during the period of our research .

7. REFERENCES

[1] Oh C., Oh J. S. Ritchie S. G. and Chang M, “Real-time estimation of freeway accident likelihood”,

Annual Meeting CD-Rom 80th Annual Meeting of The Transportation Research Board, pp.1–16, January

7-11, 2001.

[2] Juan de O˜n, Griselda López, Joaquín Abellán,” Extracting decision rules from police accident

reports through decision trees”, science direct,pp. 1151– 1160,2013

[3] Ilna site, http://ilna.ir/news/news.cfm?id=44716,( News code 44716), {Last Available:

2013/02/05}

[4] Hui-Huang Hsu,”Advanced Data Mining technologies in Bioinformatics”, Idea Group Publishing,

pp.1-324, United States of America, 2006

[5] Bastian Bohn, JochenGarcke, Rodrigo Iza-Teran, Alexander Paprotny, Benjamin Peherstorfer, Ulf

Schepsmeier, Clemens-August Thole,” Analysis of Car Crash Simulation Data with Nonlinear Machine

Learning Methods”, Procedia Computer Science, ,pp. 000– 000,2013

[6] Abdelwahab, H., Abdel-Aty, M.,“Development of artificial neural network models to

predict driver injury severity in traffic accidents at signalized intersections”, Transportation Research

Record 1746, pp. 6– 13 , 2001

[7] Chang, Li-Y., Chen, Wen-C., “Data mining of tree-based models to analyze freeway accident

frequency”, Journal of Safety Research, Volume 36 (4), pp. 365– 375, 2005.

[8] Chang, Li-Y., “Analysis of freeway accident frequencies: negative binomial regression

versus artificial neural network”, Safety Science, Volume 43 (8), pp. 541 –557, 2005.

[9] MarkusDeublein, Matthias Schubert, Bryan T. Adey, Jochen Kohler, Michael H. Faber,”

Prediction of road accidents: A Bayesian hierarchical approach”, science direct, pp. 274– 291, 2013.

[10] K. Geurts, G. Wets, T.Brijs, K. Vanhoof, “profiling high frequency accident locations using

association rules”, Transportation Research Record 1840, pp:123-130.,2003

[11] R.Marukatat, “Structure-based rule selection frame work for association rule mining of traffic

accident data”, in Computational Intelligence and Security, vol.4456, pp.231–239, 2007.

[12] Jianfeng Xi, ZhenhaiGao, ShifengNiu, Tongqiang Ding, and GuobaoNing,” A Hybrid Algorithm

of Traffic Accident Data Mining on Cause Analysis”, Mathematical Problems in Engineering, Volume

2013, pp.1-8, 2012

[13] HalimFeritBayata, FatihHattatoglu and NeslihanKarsli ” Modeling of monthly traffic accidents

with the artificial neural network method”,” International Journal of the Physical Sciences”, Vol. 6(2), pp.

244-254, 18 January, 2011

[14] S.Krishnaveni, M.Hemalatha,”A Perspective Analysis of Traffic Accident using Data Mining

Techniques”,” International Journal of Computer Applications (0975 – 8887)”, Volume 23, pp.40-48, June

2011.


12

AUTHORS

Farhad Soleimanian Gharehchopogh is currently Ph.d candidate in department

of computer engineering at Hacettepe University, Ankara, Turkey. And he works

an honors lecture in computer engineering department, science and research and

Urmia branches, Islamic Azad University, West Azerbaijan, Iran. For more

information please visit www.soleimanian.net

Zahra Asheghi Dizaji is a M.Sc. student in Computer Engineering Department,

Science and Research Branch, Islamic Azad University, West Azerbaijan, Iran. Her

interested research area are Data Mining and Meta-heuristic Algorithms. Email:

[email protected]

Zahra Aghighii is a M.Sc. student in Computer Engineering Department, Science

and Research Branch, Islamic Azad University, West Azerbaijan, Iran. Her

interested research area are Networks, Software Developing and Meta-heuristic

Algorithms. Email: [email protected]

EVALUATION OF PARTICLE SWARM OPTIMIZATION ALGORITHM IN PREDICTION OF THE CAR ACCIDENTS ON THE ROADS: A CASE STUDY

Technology