A Novel Framework to Use Association Rule Mining for classification of traffic accident severity
Introduction: Traffic accidents are an undesirable burden on society. Every year around one million deaths and more than ten 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 diverge in each country. Traffic accident data analysis is very useful in revealing the factors that affect the accidents in different countries. This article was written in the year 2016 in the Institute of Technology & Science, Mohan Nagar, Ghaziabad, up, India.
Methology: We propose a framework to utilize association rule mining (arm) for the severity classification of traffic accidents data obtained from police records in Mujjafarnagar district, Uttarpradesh, India.
Results: The results certainly reveal some hidden factors which can be applied to understand the factors behind road accidentality 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 category. The information extracted provides important information which can be employed to adapt preventive measures to overcome the accident severity in Muzzafarnagar district.
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S. Kumar & D. Toshniwal, “A novel framework to analyze road accident time series data”, Journal of Big Data, vol. 3, no. 8, pp.1-11, 2016. doi:10.1186/s40537-016-0044-5
World Health Organization (who), Global status report on road safety 2013. Supporting a decade of
action, Luxembourg: who, 2013. Available: http://www.who.int/violence_injury_prevention/road_safety_status/2013/en/
M. Karlaftis & A. Tarko, “Heterogeneity considerations in accident modeling”, Accid. Anal. Prev., vol. 30, no. 4, pp. 425-433, 1998.
S. Kumar & D. Toshniwal, “Analysis of hourly road accident counts using hierarchical clustering and
cophenetic correlation coefficient (cpcc)”, Journal of Big Data, vol. 3, no. 13, pp. 1-11, 2016.
P. N. Tan, M. Steinbach & V.Kumar, Introduction to data mining, Boston: Pearson Addison-Wesley, 2006,
p. 769.
S. Kumar & D. Toshniwal, “Analysing road accident data using association rule mining”, in International
conference on computing communication and security (icccs-2015), Kanyakumari, India, Nov. 2-3, 2015.
J. Han & M. Kamber, Data mining: concepts and techniques, United States: Morgan Kaufmann Publishers,
P. J. Ossenbruggen et al.,”Roadway safety in rural and small urbanized areas”, Accidents Analysis and Prevention, vol. 33, no. 4, pp. 485-498, 2001.
L. Mussone et al., “An analysis of urban collisions using an artificial intelligence model”, Accident Analysis and Prevention, vol. 31, pp. 705-718, 1999
L. Chang & W. Chen, “Data mining of tree based models to analyze freeway accident frequency”,
Journal of Safety Research, vol. 36, pp. 365- 375, 2005.
D. Oña, G. López, R. Mujalli & F. J. Calvo, “Analysis of traffic accidents on rural highways using latent
class clustering and bayesian networks”, Accid Anal Prev, vol. 51, pp. 1-10, 2013.
S. Kumar & D. Toshniwal, “A data mining framework to analyze road accident data”, Journal of Big Data, vol. 2, no. 1, pp. 1-18, 2015. doi:10.1186/s40537-015-0035-y
S. Kumar, D. Toshniwal & M. Parida, “A comparative analysis of heterogeneity in road accident data using data mining techniques”, Evolving Systems. doi: 10.1007/s12530-016-9165-5
K. Geurts, G. Wets, T. Brijs & K. Vanhoof, “Profiling of high frequency accident locations by use of association rules”. Transportation Research Record Journal of the Transportation Research Board, vol. 1840, 2003. doi:10.3141/1840-14
L. Thakali, T. Kwon & L. Fu, “Identification of crash hotspots using kernel density estimation and Kriging
methods: a comparison”, J. Mod. Transp., vol. 23, no. 3, pp. 93-106, 2015.
S. Kumar & D. Toshniwal, “A data mining approach to characterize road accident locations”, J. Mod.
Tran sp., vol. 24, no. 1, pp. 62-72, 2016.
J. Abellan, G. López & J. Ona,“Analysis of Traffic Accident Severity using Decision Rules via Decision
Trees”, Expert System with Applications, vol. 40, no. 15, pp. 6047-6054, 2013. doi:10.1016/j.eswa.2013.05.027
T. B. Tesema, A. Abraham & C. Grosan, “Rule mining and classification of road accidents using adaptive
regression trees”, Int J Simulation, vol. 6, pp. 80-94, 2005.
T. Kashani, A. S. Mohaymany & A. Rajbari, “A Data Mining Approach to Identify Key Factors of Traffic
Injury Severity”, Promet-Traffic & Transportation, vol. 23, pp. 11-17, 2011.
B. Depaire, G. Wets & K. Vanhoof, “Traffic Accident Segmentation by means of Latent Class Clustering”,
Accident Analysis and Prevention, vol. 40, pp. 1257-1266, 2008.
O. H. Kwon, W. Rhee & Y. Yoon, “Application of Classification Algorithms for Analysis of Road Safety Risk Factor Dependencies”, Accident Analysis and Prevention, vol. 75, pp. 1-15, 2015.
R. Agrawal & R. Srikant,“Fast Algorithms for Mining Association Rules in Large Databases”, in
Proceedings of the 20th International Conference on Very Large Data Bases, Santiago de Chile, Chile, Sept. 12-15, 1994, pp. 487-499.
