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

Abstract EN

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.