Understanding Bicycle Riding Behavior and Attention on University Campuses: A Hierarchical Modeling Approach

Abstract

The traffic behavior characteristics within university campuses have received limited scholarly attention, despite their distinct differences from external road networks. These differences include the predominance of non-motorized vehicles and pedestrians in traffic flow composition, as well as traffic peaks primarily coinciding with class transition periods. To investigate the riding behavior of cyclists on university campuses, this study examines cyclist attention, proposes a novel method for constructing a rider attention recognition framework, utilizes a hierarchical ordered logistic model to analyze the factors influencing attention, and evaluates the model’s performance. The findings reveal that traffic density and riding style significantly influence cyclists’ eye-tracking characteristics, which serve as indicators of their attention levels. The covariates of lane gaze time and the coefficient of variation in pupil diameter exhibited significant effects, indicating that a hierarchical ordered logistic model incorporating these covariates can more effectively capture the impact of influencing factors on cyclist attention. Moreover, the hierarchical ordered logistic model achieved a 7.22% improvement in predictive performance compared to the standard ordered logistic model. Additionally, cyclists exhibiting a “conservative” riding style were found to be more attentive than those adopting a “aggressive” riding style. Similarly, cyclists navigating “sparse” traffic conditions were more likely to maintain attention compared to those in “dense” traffic scenarios. These findings provide valuable insights into the riding behavior of university campus cyclists and have significant implications for improving traffic safety within such environments.