A Novel Framework to Use Association Rule Mining for classification of traffic accident severity

Meenu Gupta, Vijender Kumar Solanki, Vijay Kumar Singh


Introduction Traffic accidents are an undesirable burden on the society. Every year around one million deaths and more than 10 million injuries are reported due to traffic accidents. Hence, traffic accidents prevention measures must be taken to overcome the accident rate. Different countries have different geographical and environmental conditions and hence the accident factors are different in each country. Traffic accident data analysis is very useful in revealing the factors that affects the accidents in different country. The article was written in the year 2016 in the Institute of Technology & Science, Mohan Nagar, Ghaziabad, UP, INDIA. Methology In this paper, we have proposed a framework to utilize association rule mining (ARM) for classification of severity of traffic accident data obtained from police records in Mujjafarnagar district, Uttarpradesh, India. Results: the results certainly reveal some hidden factors which can be utilized to understand the factors behind road accident in this region. . Conclusions: The framework enables us to find three clusters from the data set. Each cluster represents a type of accident severity, i.e. fatal, major injury and minor/no injury. The association rules exposed different factors that are associated with road accidents in each of the category. The information extracted provides important information which can be certainly utilized to put some preventive measures to overcome the accident severity in Muzzafarnagar district.

Palabras clave

Association rule mining; classification; traffic accident; severity analysis

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Kumar S, Toshniwal D, (2016), A novel framework to analyze road accident time series data, Journal of Big Data, 3(1): 1-11.DOI: 10.1186/s40537-016-0044-5

World Health Organization (WHO), Global status report on road safety 2013. Supporting a de-cade of action.

Karlaftis M, Tarko A, (1998), Heterogeneity considerations in accident modeling. Accid. Anal. Prev. 30(4), 425–433.

Kumar S, Toshniwal D, (2016), Analysis of hourly road accident counts using hierarchical clus-tering and cophenetic correlation coefficient (CPCC), Journal of Big Data, 3(1): 1-11.

Tan PN, Steinbach Mand Kumar V. Introduction to data mining. Boston: Pearson Addison-Wesley; 2006.

Kumar S, Toshniwal D, (2015), Analysing road accident data using association rule mining, In-ternational conference on computing communication and security (ICCCS-2015), dec-2015, Mauritius.

Han J, Kamber M. Data mining: concepts and techniques. USA: Morgan Kaufmann Publishers; 2001.

Ossenbruggen, P. J., J. Pendharkar, et al. (2001). "Roadway safety in rural and small urbanized areas." Accidents Analysis and Prevention 33(4): 485-498.

Mussone, L., A. Ferrari, et al. (1999). "An analysis of urban collisions using an artificial intelli-gence model." Accident Analysis andPrevention 31: 705-718

Chang, L. and W. Chen (2005). "Data mining of treebased models to analyze freeway accident frequency." Journal of Safety Research 36: 365- 375.

Oña JD, López G, Mujalli R, Calvo FJ. Analysis of traffic accidents on rural highways using la-tent class clustering and bayesian networks. Accid Anal Prev. 2013;51:1–10.

Kumar S, Toshniwal D. A data mining framework to analyze road accident data. Journal of Big Data. 2015;2(1):1–18. doi:10.1186/s40537-015-0035-y.

Kumar S, Toshniwal D. A comparative analysis of heterogeneity in road accident data using da-ta mining techniques, Evolving Systems, DOI 10.1007/s12530-016-9165-5.

Geurts K, Wets G, Brijs T, Vanhoof K. Profiling of high frequency accident locations by use of association rules. Transportation Research Record. 2003. doi:10.3141/1840-14.

Thakali L, Kwon T and Fu L, Identification of crash hotspots using kernel density estimation and Kriging methods: a comparison J. Mod. Transp., 23 (3) (2015), pp. 93–106

Kumar S and Toshniwal D, (2016), A data mining approach to characterize road accident loca-tions, J. Mod. Transp., 24(1):62-72.

Abellan J, Lopez G, Ona J (2013). Analysis of Traffic Accident Severity using Decision Rules via Decision Trees. Expert System with Applications. 40:6047-6054. Doi:10.1016/j.eswa.2013.05.027

Tesema TB, Abraham A, Grosan C (2005). Rule mining and classification of road accidents us-ing adaptive regression trees. Int J Simulation. 6:80–94.

Kashani T, Mohaymany AS, Rajbari A (2011). A Data Mining Approach to Identify Key Fac-tors of Traffic Injury Severity. Promet-Traffic & Transportation. 23:11-17.

Depaire B, Wets G, Vanhoof K (2008). Traffic Accident Segmentation by means of Latent Class Clustering. Accident Analysis and Prevention. 40:1257-1266

Kwon OH, Rhee W, Yoon Y (2015). Application of Classification Algorithms for Analysis of Road Safety Risk Factor Dependencies. Accident Analysis and Prevention. 75:1-15

Agrawal R, Srikant R (1994). Fast Algorithms for Mining Association Rules in Large Data-bases. Proceedings of the 20th International Conference on Very Large Data Bases, pp.487-499.

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DOI: https://doi.org/10.16925/in.v13i21.1726

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