Understanding the e-bicycle overtaking behaviors is very important for analyzing the bicycle traffic and designing the automated driving system in the future. However, currently, few solid insights of overtaking behaviors were made, due to a lack of efficient models to simulate realistic bicycle overtaking trajectories, and especially, the tactical strategies. This paper referred to the latest advancement in the automated driving system, and applied a maximum entropy based inverse reinforcement learning method to repeat realistic bicycle overtaking trajectories, and identify tactical strategy preferences and considerations of trade-off factors (safety, comfort, and efficiency) during an overtaking task. The method was demonstrated on thirty-five e-bicycle overtaking events, and successfully generated overtaking trajectories of similar features (acceleration, jerk, speed, lane deviation, and collision avoidance) as observed trajectories. The results show that, in general, during an overtaking task, the feature that is first considered is the safety-related factor, the second is the speed control and the third is the lateral movement; nevertheless, there are significant individual heterogeneities when deciding overtaking behaviors; in addition, the overtaking behavior is also affected by the type of overtaken bicycle. Implications for research and practice are proposed in this study.
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Master of Science (M.S.)
College of Engineering and Computer Science
Civil, Environmental and Construction Engineering
Civil Engineering; Smart Cities
Length of Campus-only Access
Masters Thesis (Campus-only Access)
Yue, Lishengsa, "Understanding E-Bicycle Overtaking Strategies by Using Inverse Reinforcement Learning" (2020). Electronic Theses and Dissertations, 2020-. 820.
Restricted to the UCF community until June 2026; it will then be open access.