Postprint version. Published in 9th International Conference on Applications of Advanced Technology in Transportation (AATT) Proceedings: Chicago, IL, September 1, 2006, pages 250-256.
NOTE: At the time of publication, the author A. Pande was not yet affiliated with Cal Poly.
Data mining is the analysis of large "observational" datasets to find unsuspected relationships that might be useful to the data owner. It typically involves analysis where objectives of the mining exercise have no bearing on the data collection strategy. Freeway traffic surveillance data collected through underground loop detectors is one such "observational" database maintained for various ITS (Intelligent Transportation Systems) applications such as travel time prediction etc. In this research data mining process is used to relate this surrogate measure of traffic conditions with rear-end crash occurrence on freeways. Crash and dual loop detector data from 36.25-mile instrumented Interstate-4 corridor in Orlando (FL) are used in this study. The research problem is set up as a classification problem and separate data mining based classifiers are developed to discriminate crashes belonging to different categories from normal conditions on the freeway. Based on the models developed in this study one can identify the traffic conditions prone to rear-end crashes 5–10 minutes prior to the crash. The findings of this research are proposed to be used as a proactive traffic management system which could warn the drivers about potential rear-end crashes.
Civil and Environmental Engineering