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.
Highlights
In recent years, the bicycle has been widely used as an important traffic mode, especially for a commuting trip or recreational trip [1, 2]
The primary objective of this study is to model the cyclists’ red-light running frequency within the framework of Bayesian statistics
This study evaluated the application of PG model and PLN model developed using Bayesian statistical techniques for modeling the frequency of cyclists’ red-light running behavior at signalized intersection
Summary
The bicycle has been widely used as an important traffic mode, especially for a commuting trip or recreational trip [1, 2]. Bicycles provide users with convenient, flexible, and affordable mobility, constituting an important supplementation to the urban transit system. Bicycle has been recognized as an environmentally friendly mode of transport [3,4,5,6]. A study in 2010 showed that average bicycle modal share for urban trips accounts for 38% in China [7]. Because of the advantage of no pollution emission, low carbon, and low noise, the government is showing an interest in promoting bicycles [3,4,5,6, 8]
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