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

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

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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 ...

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

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

    • International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.4, August 2013 EVALUATION OF PARTICLE SWARM OPTIMIZATION ALGORITHM IN PREDICTION OF THE CAR ACCIDENTS ON THE ROADS: A CASE STUDY Farhad Soleimanian Gharehchopogh1, Zahra Asheghi Dizaji2, and Zahra Aghighi3 1 Department of Computer Engineering, Urmia Branch, Islamic Azad University, Iran Bonab.farhad@gmail.com 2,3 Department of Computer Engineering, Science and Research Branch, IslamicAzad University, West Azerbaijan, Iran. 2 Zahra_ashegi@yahoo.com, 3zaghighi@yahoo.com 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 DOI:10.5121/ijcsa.2013.3401 1
    • International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.4, August 2013 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. Human Vehicle Feature, behavior-skills and habits, Disability, mental status,… Segmentstechnical fault Safety, Convenience Accident Environment Weather condition,light radiation status,… Road Geometric properties، Road surface condition، ...Traffic 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. 2
    • International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.4, August 2013 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 highrisk 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 3
    • International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.4, August 2013 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)) Part 1 Part 2 Part 3 Equation (2) xi(t+1)=xi(t)+vi(t+1) Part 1 Part 2 4 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. 4
    • International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.4, August 2013 Read pre-processed data Start Initialization The data set used for testing and training Fitness Calculate Send training data for PSO algorithm to learn Experiences updates N o Calculate new values N o You have reached the desired criteria? Evaluation of training data Y es Yes Number of correct answers Finish Assessment results and determine the percent error Figure( 3)- PSO algorithm Figure( 2)- The proposed method 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). 5
    • International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.4, August 2013 Table (1): The simulated proposedmethod 1-Ds=read data from dataset 2-Train_ds=get many random record for training 3-Exam_ds=get many random record for examine 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. Rules=Get best rules that outputposition algorithm 5. For each particle, update the of pso and the velocity according (1) and (2). Go to step 2. 5-Result= Evaluated according to the rules 6-Review and evaluate result 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 6
    • International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.4, August 2013 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. 7
    • International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.4, August 2013 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. 8
    • International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.4, August 2013 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 9
    • International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.4, August 2013 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 Precision Data that classified correctly Data that classified incorrectly Kappa percentage Training data Testing data Whole data Numbers 130 15 579 145 724 Percentage 89% 11% 80% 80% 20% 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. 10
    • International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.4, August 2013 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. 11
    • International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.4, August 2013 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: Zahra_ashegi@yahoo.com 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: zaghighi@yahoo.com 12