Prediction of rear-end conflict frequency using multiple-location traffic parameters

•Rear-end conflict frequency in an urban network is investigated.•Vehicle-to-Vehicle data from multiple locations are utilized.•Conflict frequency is correlated with network traffic state.•Zero-Inflated, Negative Binomial and quasi-Poisson models are developed.•High variations in speed and congested conditions increase conflict counts.