Modeling the illegal lane-changing behavior of bicycles on road segments: Considering lane-changing categories and bicycle heterogeneity

Abstract Road segments using traffic markings to separate the vehicle lane and the adjacent bicycle lane are widely adopted on urban roads. Bicycles’ illegal lane-changing behavior (ILC) is commonly observed on such road segments, resulting in severe influences of traffic efficiency and bringing hidden dangers. However, existing models disregard different categories of ILC and heterogeneities of ILC between electric bikes (e-bikes) and regular bikes (r-bikes), which has limitations in representing the realistic ILC. To address these disadvantages and understand how heterogeneities affect the ILC, this paper analyzes the behavioral characteristics of ILC using field data and proposes a new cellular automaton (CA) model. Corresponding rules are set for different categories of ILC in the model, and simultaneously the bicycle heterogeneities of ILC are also considered in rules-making. Simulation results indicate that as the proportion of e-bikes increases, the frequency of ILC increases. Furthermore, vehicle volume decreases due to the occurrence of ILC, and the effect is more evident with a higher proportion of e-bikes. Findings of the study can help us better understand the ILC, and the proposed simulation model is useful for pre-evaluating unimplemented designs for the mixed traffic road segment.