  Real-time safety analysis has become a hot research topic as it can more
accurately reveal the relationships between real-time traffic characteristics
and crash occurrence, and these results could be applied to improve active
traffic management systems and enhance safety performance. Most of the previous
studies have been applied to freeways and seldom to arterials. This study
attempts to examine the relationship between crash occurrence and real-time
traffic and weather characteristics based on four urban arterials in Central
Florida. Considering the substantial difference between the interrupted urban
arterials and the access controlled freeways, the adaptive signal phasing data
was introduced in addition to the traditional traffic data. Bayesian
conditional logistic models were developed by incorporating the Bluetooth,
adaptive signal control, and weather data, which were extracted for a period of
20 minutes (four 5-minute intervals) before the time of crash occurrence. Model
comparison results indicated that the model based on 5-10 minute interval
dataset performs the best. It revealed that the average speed, upstream
left-turn volume, downstream green ratio, and rainy indicator were found to
have significant effects on crash occurrence. Furthermore, both Bayesian random
parameters logistic and Bayesian random parameters conditional logistic models
were developed to compare with the Bayesian conditional logistic model, and the
Bayesian random parameters conditional logistic model was found to have the
best model performance in terms of the AUC and DIC values. These results are
important in real-time safety applications in the context of Integrated Active
Traffic Management.
