Modeling roadway incident risk using aggregated real-time detector data

This report applies previously developed methodology from authors Abdel-Aty et. al. in a 2005 Institute of Transportation Engineers (ITE) Journal to predict roadway conditions with high risk of incidents. The methodology, which includes logistic regression modeling and hazard ratio estimation, is applied to a large, high frequency dataset generated by roadway detectors in the I-80 corridor in the San Francisco-Oakland-Berkley area. Results differ from the original ITE paper and model features do not show strong relationships with increased incident risk on the I-80 corridor with the possible exception of standard deviation in speeds. Concluding thoughts offer insights into reasons the methodology may have failed.