Identifying Effective Factors and Predicting Road Accidents Using Data Mining Approaches (Case Study of Tehran-Qom)

Document Type : Scientific - Research

Authors

1 MSc. Student, Industrial Engineering Department, Faculty of Engineering, Islamic Azad University, Science and Research Branch of Tehran, Tehran, Iran

2 Assistant Prof. , Industrial Engineering Department, Faculty of Engineering, Islamic Azad University, Science and Research Branch of Tehran, Tehran, Iran

3 Associate Prof. , Industrial Engineering Department, Faculty of Engineering, Islamic Azad University, Science and Research Branch of Tehran, Tehran, Iran

Abstract

The main purpose of this study, Modeling, identifying key factors and investigating various data mining algorithms in traffic accidents in Iran, especially on urban roads. The following was extracted set of rules that can be effective in identifying key factors and their impact in reducing accidents. The data included 5099 records of traffic accidents on the Tehran-Qom road in Tehran during a four-year period. Each accident record consisted of 57 accidents related to accidents. In the pre-processing, the greedy search algorithm was used to find the best sub-set of factors. After the pre-processing, reduced records used. Finally, 8 characteristics, accident factor, type of collision, visual impedance, accident situation, road surface conditions, location geometry, direct causes and type of accident were investigated. To achieve the objectives of this research, seven different data mining techniques were used. 6 Data mining algorithms were used to predict and identify key factors using WEKA data mining software; These algorithms include: J48, PART, Logistic, Classification via Regression, Naïve Bayesian and Multilayer Perceptron. Also, the apriori algorithm was used to extract the rules. The results showed that Perceptron and Part algorithms had the best performance among other algorithms to predict. Algorithm Naïve Bayesian despite being in the range of acceptable has a weaker performance than other algorithms, both in model accuracy and coverage. Several of the most important factors of the set of rules were road accidents, flat and straight geometry, Lack of visibility barrier And attending the accident site.

Keywords

Main Subjects

References

-Anvari, M. B., Tavakoli Kashani, A. and Rabieyan, R. (2017) "Identifying the most important factors in the at-fault probability of motorcyclists by data mining, based on classification tree models", International Journal of Civil Engineering, Vol.  15, No. 4, pp. 653-662
-Castro, Y. and Kim, Y. J. (2016) "Data mining on road safety: Factor assessment on vehicle accidents using classification models”, International Journal of Crashworthiness, Vol.  21, No. 2, pp. 104-111
-Chang, L. Y. and Wang, H. W. (2006) "Analysis of traffic injury severity: An application of non-parametric classification tree techniques", Accident Analysis and Prevention, Vol.  38, No. 5, pp. 1019-1027
-de Oña, J., López, G. and Abellán, J. (2013) "Extracting decision rules from police accident reports through decision trees", Accident Analysis & Prevention, Vol.  50, No., pp. 1151-1160
-Deb, R. and Liew, A. W. C. (2016) "Missing value imputation for the analysis of incomplete traffic accident data", Information Sciences, Vol.  339, No., pp. 274-289
-Geurts, K., Thomas, I., and Wets, G. (2005) "Understanding spatial concentrations of road accidents using frequent item sets”, Accident Analysis and Prevention, Vol.  37, No. 4, pp. 787-799
-Haghighi, F. R. and GholamNejad, R. (2016) "Modeling the risk and safety of passage of students in roadside schools”, Journal of Transportation Engineering Research, Vol.  7, No. 4, pp. 605-614
-Jung, S., Qin, X. and Oh, C. (2016) "Improving strategic policies for pedestrian safety enhancement using classification tree modeling”, Transportation Research Part A: Policy and Practice, Vol.  85, No., pp. 53-64
-Kashani, A. T. and Besharati, M. M. (2017) "Fatality rate of pedestrians and fatal crash involvement rate of drivers in pedestrian crashes: a case study of Iran”, International Journal of Injury Control and Safety Promotion, Vol.  24, No. 2, pp. 222-231
-Kashani, A. T. and Mohaymany, A. S. (2011) "Analysis of the traffic injury severity on two-lane, two-way rural roads based on classification tree models”, Safety Science, Vol.  49, No. 10, pp. 1314-1320
-Kumar, S. and Toshniwal, D. (2017) "Severity analysis of powered two wheeler traffic accidents in Uttarakhand, India”, European Transport Research Review, Vol.  9, No. 2, pp.
-Kwon, O. H., Rhee, W. and Yoon, Y. (2015) "Application of classification algorithms for analysis of road safety risk factor dependencies”, Accident Analysis and Prevention, Vol.  75, No., pp. 1-15
-Mohaymany, A. S., Kashani, A. T. and Ranjbari, A. (2010) "Identifying driver characteristics influencing overtaking crashes”, Traffic Injury Prevention, Vol.  11, No. 4, pp. 411-416
-Montella, A. (2011) "Identifying crash contributory factors at urban roundabouts and using association rules to explore their relationships to different crash types”, Accident Analysis and Prevention, Vol.  43, No. 4, pp. 1451-1463
-Montella, A., Aria, M., D'Ambrosio, A. and Mauriello, F. (2011) "Data-mining techniques for exploratory analysis of pedestrian crashes”,  Transportation Research Record, Vol. 2237,  pp. 107-116.  DOI: 10.3141/2237-12
-Pakgohar, A., Tabrizi, R. S., Khalili, M. and Esmaeili, A. (2010) "The role of human factor in incidence and severity of road crashes based on the CART and LR regression: A data mining approach," 1st World Conference on Information Technology, WCIT-2010, Istanbul, 2011, pp. 764-769.
-Prati, G., Pietrantoni, L. and Fraboni, F. (2017) "Using data mining techniques to predict the severity of bicycle crashes”, Accident Analysis and Prevention, Vol.  101, No., pp. 44-54
-Taamneh, M., Alkheder, S. and Taamneh, S. (2017) "Data-mining techniques for traffic accident modeling and prediction in the United Arab Emirates”, Journal of Transportation Safety and Security, Vol.  9, No. 2, pp. 146-166
-Tao, G., Song, H., Liu, J., Zou, J. and Chen, Y. (2016) "A traffic accident morphology diagnostic model based on a rough set decision tree”, Transportation Planning and Technology, Vol.  39, No. 8, pp. 751-758
-Tavakoli Kashani, A., Rabieyan, R. and Besharati, M. M. (2014) "A data mining approach to investigate the factors influencing the crash severity of motorcycle pillion passengers”, Journal of Safety Research, Vol.  51, No., pp. 93-98
-Yau, K. K. W., Lo, H. P. and Fung, S. H. H. (2006) "Multiple-vehicle traffic accidents in Hong Kong”, Accident Analysis and Prevention, Vol.  38, No. 6, pp. 1157-1161

Files

History

  • Receive Date: 26 October 2017
  • Revise Date: 25 February 2018
  • Accept Date: 27 February 2018

Share

How to cite

Statistics