Evaluation of Particle Swarm Optimization Algorithm in Prediction of the Car Accidents on the Roads a Case Study

7/29/2019 Evaluation of Particle Swarm Optimization Algorithm in Prediction of the Car Accidents on the Roads a Case Study

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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 CARACCIDENTS ON THE ROADS: A

CASESTUDY

Farhad Soleimanian Gharehchopogh1, Zahra Asheghi Dizaji

2, and Zahra Aghighi

3

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

[email protected],3Department 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 its requires identification of underlay in dimensions in this field. Due to theincreasing 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 Azerbaijanprovince in Iran by accident type (damage, injury, death) and we describe it by using Particle SwarmOptimization (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 problemwhere 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 mentalhealth problems and depression in family members, loss of the active labor force permanently ortemporarily) is more in comparison to developed countries. Injuries and damages caused by roadaccidents is important and significant matter, which unfortunately is usually ignored. Publichealth of society is a challenge that demands coordinated and integrated efforts and acts foreffective 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


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responsibilities correctly and be impeccable, Probability of accident happens nears to zero, Butwhen 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 thatrepeatedly through the mass media and road and safety officials of country to have 70% share ofhuman error in incidence of accidents is referred in country [3]. In Figure (1) the chain ofsignificant 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 multipleinvolved factors and heterogeneous in this field, we see a different data products which each ofthem made from various sources. In order toexploitthelarge numberof data and produced data weneed to connect this data and their hidden available patterns. Conventional methods do not havefeature 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 anddata analysis, one of new methods in large and various data analysis is using data miningtechniques.[4-5] One Data bases that contains complete information about the subject of complexfactors possibly associated with accidents, are 114forms. 114form is a type of form that iscompleted by the police during a road accident in which a number of important factors andinformation about the crash occurred is mentioned in the form .This information's include: Thisinformation 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, wehave used a method for prediction of PSO in road accidents based on the data. The rest of thepaper is organized as follows: section2 reviews the work done in this field, the proposed methodis presented in section 3, the proposed method is evaluated in section 4, and conclusions ispresented in section 5.

HumanFeature, behavior-skills

and habits, Disability,

mental status,

VehicleSegmentstechnical fault

Safety, Convenience

RoadGeometric properties

Road surface condition...Traffic

EnvironmentWeather condition,light

radiation status,

Accident


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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 analysismeans that the set of inputs is a particular output And predictive analysis to explore thedevelopment of a set of observations or variables based on the similarity or lack of similaritybetween these sets. Brijsest applied Search association rules to identify and distinguish high-riskand low-risk areas, traffic patterns. In this paper, a technique were used for classifying a set ofassociation rules based on data accident and features of the site that had been divided into blackand white points. Analysis shows that association rules facilitate the identification of events thatoften occur together that this will lead to a better understanding of road accidents. The results alsoshow 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 behavioralcharacteristics 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-riskareas 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 Roadaccident analysis [11]. In these studies, classifying, selecting and filtering data, using rulesstructure analysis has been done, so rule-based structure analysis of various parameters is donemany times. Based on the results we identified candidate rules. Researchers [12] were studiedcause of occurred accidents using association rules algorithm based on PSO algorithm. In thispaper, the concept of the association entropy is used to compare the characteristics of accidentsand also T-test model and Delphi method [12] to test the health of enhanced algorithm. In thispaper, 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 trafficaccidents. In these studies, the best model according to Akaike statistical criteria, select andstatistical analysis were tested in error condition. It should be noted that this is a case study (inTurkey) [13]. According to accomplished studies by researchers [14] intensity of driving injuriesin accidents examined by using ANN s and decision trees. In these studies, according to theresults of the model a decision tree method was introduced better than ANN method, andaccording 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 massmovement of birds that are looking for food. In the first step of the algorithm, a random value isgenerated for each particle, in this problem a Set of random values generated for the involvedfactors in this type of accident then the fitness value is calculated based on the values which inthis problem fitness value is calculated by comparing random values with available facts (trainingdata).Now if the value of the fitness function is against zero, values for each particle is updatedfrom 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


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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 displayedby 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 anumber 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 andsecond and third are the particle speed change and its rotation toward individual experience andthe best experience of group. If we do not consider the first part of this equation is determinedaccording to the current position and the best experience and the best experience of group. Thusthe best particles gathered and remain fixed in its situation and the others moving to toward thatparticle. In fact, the collective motion of particles without the first part of equation(1), will be theprocess 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 routineway 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 toclassify the accidents by type of it. Process of this prediction by assuming this the data collectedand additional data are omitted is shown in Figure 2.

Part 1 Part 3Part 2

Part 1 Part 2


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As shown in Figure2 in first step processing data read by the model then based on need number ofdata 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 andevery particle a series of laws defined and then based on this Initialization the fitted function ofeach particle is calculated and chosen the best bit.PSO algorithm finishes by finishing number ofirritations or obtaining the desired criteria. Finally by law using samples of test data examinedand 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

N

Read pre-processed

data

Evaluation

of training

data

The data set used for

testing and training

Send training data forPSO algorithm to

learn

Number of correct

answers

Assessment results

and determine the

percent error

Calculate

newvalues

Initialization

Start

Fitness Calculate

Experiences updates

You have

reached the

desired criteria?

Finish

Yes

N


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Table (1): The simulated proposedmethod

4. EVALUATION AND ASSESMENT

Examines data of this paper is related to accidents taking place in West Azerbaijan province axisin a period of the first 6 months of 1390. The received data is from the Transportation andTerminals organization West Azerbaijanas 114com forms. After an initial preprocessing of thedata some information extracted from this information. The results of these studies indicate thatamong the four main causes of accidents Human factors, environmental factors, and the way thevehicle), 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


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


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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.


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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 alsoconfirmed 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 thathas the highest proportion of deaths from traffic accidents


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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 informationdependency. Using this algorithm, based on the available data, we can have accurate predictionbased on the type of happened accident. The results of the implementation of PSO algorithm areshown in Table (2).

Table (2)Resultsontraining data

PercentageNumbersPrecision%89130Data that classified correctly%1115Data that classified incorrectly%80-Kappa percentage

%80579Training data%20145Testing data

724Whole data5. CONCLUSION ANDFUTUREWORK

ANNs are suitable tools to adapt, learning and classification information. Collective intelligenceand neural networks can be regarded as a response to the challenge. In this paper, we investigatedusing PSO algorithm to classify the accident based on their kind, considering the accidentoccurrence in terms of significant parameters. According to performed simulations on the inputdata (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.


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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 gratefulfor their cooperation during the period of our research .

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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 andUrmia 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]

Documents

Evaluation of Particle Swarm Optimization Algorithm in Prediction of the Car Accidents on the Roads a Case Study