An Analysis the Traffic Accident Using Datamining Technique
Keywords:
Association Rule mining, Apriori Algorithm, Road AccidentAbstract
Association rule mining algorithms are generally used to discover all principles in the database fulfilling some base help and least certainty requirements. So as to diminish the quantity of produced rules, the adjustment of the affiliation rule mining algorithm to mine solitary a specific subset of affiliation rules where the characterization class credit is appointed to one side hand-side was examined in past research. In this examination, a dataset about traffic accidents was gathered from Dubai Traffic Department, UAE. After data preprocessing, Apriori and Predictive Apriori affiliation rules algorithms were connected to the dataset so as to investigate the connection between recorded accidents' variables to mishap seriousness in Dubai. Two arrangements of class affiliation rules were created utilizing the two algorithms and condensed to get the most intriguing standards utilizing specialized measures. Exact outcomes demonstrated that the class affiliation rules produced by Apriori algorithm were more compelling than those created by Predictive Apriori algorithm. More relationship between mishap elements and mishap seriousness level were investigated while applying Apriori algorithm
References
[1] Abdel-Aty, M., and Abdelwahab, H., Analysis and Prediction of Traffic Fatalities Resulting From Angle Collisions Including the Effect of Vehicles‟ Configuration and Compatibility. Accident Analysis and Prevention, 2003.
[2] Bedard, M., Guyatt, G. H., Stones, M. J., &Hireds, J. P., The Independent Contribution of Driver, Crash, and Vehicle Characteristics to Driver Fatalities. Accident analysis and Prevention, Vol. 34, pp. 717- 727, 2002.
[3] Domingos, Pedro & Michael Pazzani (1997) "On the optimality of the simple Bayesian classifier under zero-one loss". Machine Learning, 29:103–137.
[4] Evanco, W. M., The Potential Impact of Rural Mayday Systems on Vehicular Crash Fatalities. Accident Analysis and Prevention, Vol. 31, 1999, pp. 455-462.
[5] E. Frank and I. H. Witten. Generating accurate rule sets without global optimization. In Proc. of the Int‟l Conf. on Machine Learning, pages 144–151. Morgan Kaufmann Publishers Inc., 1998.
[6]Kweon, Y. J., &Kockelman, D. M., Overall Injury Risk to Different Drivers: Combining Exposure, Frequency, and Severity Models. Accident Analysis and Prevention, Vol. 35, 2003, pp. 441-450.
[7]Martin, P. G., Crandall, J. R., &Pilkey, W. D., Injury Trends of Passenger Car Drivers In the USA. Accident Analysis and Prevention, Vol. 32, 2000, pp. 541-557.
[8]National Highway Traffic Safety Administration, Traffic Safety Facts 2005, 2007, P. 54.
[9]Ossenbruggen, P.J., Pendharkar, J. and Ivan, J. 2001, “Roadway safety in rural and small urbanized areas”. Accidents Analysis and Prevention, 33 (4), pp. 485– 498.
[10]Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993.
[11]Rish, Irina. (2001). "An empirical study of the naive Bayes classifier". IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence.
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