  This study attempts to investigate the relationship between crash occurrence
at signalized intersections and real-time traffic, signal timing, and weather
characteristics based on 23 signalized intersections in Central Florida. The
intersection and intersection-related crashes were collected and then divided
into two types, i.e., within intersection crashes and intersection entrance
crashes. Bayesian conditional logistic models were developed for these two
kinds of crashes, respectively. For the within intersection models, the model
results showed that the through volume from "A" approach (the traveling
approach of at-fault vehicle), the left turn volume from "B" approach
(near-side crossing approach), and the overall average flow ratio (OAFR) from
"D" approach (far-side crossing approach), were found to have significant
positive effects on the odds of crash occurrence. Moreover, the increased
adaptability for the left turn signal timing of "B" approach and more priority
for "A" approach could significantly decrease the odds of crash occurrence. For
the intersection entrance models, average speed was found to have significant
negative effect on the odds of crash occurrence. The longer average green time
and longer average waiting time for the left turn phase, higher green ratio for
the through phase, and higher adaptability for the through phase can
significantly improve the safety performance of intersection entrance area. In
addition, the average queue length on the through lanes was found to have
positive effect on the odds of crash occurrence. These results are important in
real-time safety applications at signalized intersections in the context of
proactive traffic management.
