Market basket analysis of crash data from large jurisdictions and its potential as a decision support tool

Safety Science - Tập 47 - Trang 145-154 - 2009
Anurag Pande1, Mohamed Abdel-Aty1
1Department of Civil and Environmental Engineering, University of Central Florida, Orlando, FL 32816-2450, USA

Tài liệu tham khảo

Abdel-Aty, 2003, Analysis of driver injury severity levels at multiple locations using ordered probit models, Journal of Safety Research, 34, 597, 10.1016/j.jsr.2003.05.009 Abdel-Aty, 2005, Exploring the overall and specific crash severity levels at signalized intersections, Accident Analysis and Prevention, 37, 417, 10.1016/j.aap.2004.11.002 Abdelwahab, 2001, Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections, Transportation Research Record, 1746, 6, 10.3141/1746-02 Agrawal, R., Imielinski, T., Swami, A., 1993. Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD, Washington DC, pp. 207–216. Bayam, 2005, Older drivers and accidents: a meta analysis and data mining application on traffic accident data, Expert Systems with Applications, 29, 598, 10.1016/j.eswa.2005.04.025 Bayardo Jr., R., Agrawal, R., 1999. Mining the most interesting rules. In: Proceedings of the 1999 ACM-SIGKDD International Conference on Knowledge Discovery in Databases and Data Mining, pp. 145–154. Brin, S., Motwani, R., Ullman, J., Tsur, S., 1997. Dynamic itemset counting and implication rules for market basket data. In: Proceedings of the 1997 ACM-SIGMOD International Conference on the Management of Data, pp. 255–264. Brin, 1998, Beyond market baskets: generalizing association rules to dependence rules, Data Mining and Knowledge Discovery, 2, 39, 10.1023/A:1009713703947 Chang, 2005, Analysis of freeway accident frequencies: negative binomial regression versus artificial neural network, Safety Science, 43, 541, 10.1016/j.ssci.2005.04.004 Chang, 2005, Data mining of tree-based models to analyze freeway accident frequency, Journal of Safety Research, 36, 365, 10.1016/j.jsr.2005.06.013 Fukuda, T., Morimoto, Y., Morishita, S., Tokuyama, T., 1996. Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization. In: Proceedings of the 1996 ACM-SIGMOD International Conference on the Management of Data, pp. 13–23. Geurts, 2005, Understanding spatial concentrations of road accidents using frequent item sets, Accident Analysis and Prevention, 37, 787, 10.1016/j.aap.2005.03.023 Golob, 2004, A Method for relating type of cash to traffic flow characteristics on urban freeways, Transportation Research – Part A, Policy and Practice, 38, 52, 10.1016/j.tra.2003.08.002 Greibe, 2005, Accident prediction models for urban roads, Accident Analysis and Prevention, 35, 273, 10.1016/S0001-4575(02)00005-2 Hand, 2001 SAS Institute, 2001 Shankar, 1996, Statistical analysis of accident severity on rural freeways, Accident Analysis and Prevention, 28, 391, 10.1016/0001-4575(96)00009-7 Traffic Safety Facts, 2001. National Highway Traffic Safety Administration. National Center for Statistics and Analysis. Tuzhilin, A., Adomavicius, G., 2002. Handling very large numbers of association rules in the analysis of microarray data. In: Proceedings of the 2002 ACM-SIGKDD International Conference on Knowledge discovery and Data Mining, pp. 396-404. Tuzhilin, A., Liu, B., 2002. Querying multiple sets of discovered rules. In: Proceedings of the 2002 ACM-SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 52–60.