Abstract:

Group and retrograde behaviors are two common bicycle riding that generally induce some local self-organization phenomena and reduce traffic efficiency. Herein, an extended cellular aulomata model that considers group and retrograde behaviors is proposed to explore the impacts of the two behaviors on the movement mechanism and traffic characteristics of bicycle flow. Simulation experiments are performed to investigate the impacts of shoulder/following group behavior and retrograde behavior on bicycle flow. Simulation results show that a mixture of shoulder group behavior and retrograde behavior will reduce the traffic capacity, increase the travel time, and decrease the average riding speed. Alternatively, a mixture of following group behavior and retrograde behavior decrease only the average speed and cause a slight oscillation in bicycle flow. These aforementioned negative impacts are related to various factors: group size, group proportion, retrograde proportion, and average bicycle arrival rate. The findings of this study can facilitate improved bicycle flow management (particularly in the case of retrograde behavior).

Key words: traffic engineering, bicycle, cellular automata model, group behavior, retrograde behavior