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

Abstract

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 Bayesianpvalues 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.

Traffic Conflict Identification of E-Bikes at Signalized Intersections

“…Comparing the behaviour differences between e-bike cyclists and traditional cyclists, studies show that e-bikes cyclists would gain higher mobility at the expense of more risk behaviour (Bai et al 2013;Cherry, Cervero 2007). Several scholars found that the factors causing behaviour differences are age, gender and stages of signal phase (Wu et al 2012(Wu et al , 2016Guo et al 2014). Xu et al (2016) identified the interrelationships of these factors and proposed a method to evaluate safety of nonmotorized lanes.…”