Modeling the Frequency of Cyclists’ Red‐Light Running Behavior Using Bayesian PG Model and PLN Model

Red‐light running behaviors of bicycles at signalized intersection lead to a large number of traffic conflicts and high collision potentials. The primary objective of this study is to model the cyclists’ red‐light running frequency within the framework of Bayesian statistics. Data was collected at twenty‐five approaches at seventeen signalized intersections. The Poisson‐gamma (PG) and Poisson‐lognormal (PLN) model were developed and compared. The models were validated using Bayesian p values based on posterior predictive checking indicators. It was found that the two models have a good fit of the observed cyclists’ red‐light running frequency. Furthermore, the PLN model outperformed the PG model. The model estimated results showed that the amount of cyclists’ red‐light running is significantly influenced by bicycle flow, conflict traffic flow, pedestrian signal type, vehicle speed, and e‐bike rate. The validation result demonstrated the reliability of the PLN model. The research results can help transportation professionals to predict the expected amount of the cyclists’ red‐light running and develop effective guidelines or policies to reduce red‐light running frequency of bicycles at signalized intersections.