Understanding e-bicycle overtaking strategy: insights from inverse reinforcement learning modelling

Abstract

Understanding e-bicycle overtaking strategies (i.e. e-bicycle overtakes bicycle/e-bicycle) is important for developing bicycle simulation models and analyzing bicycle traffic. However, few reliable insights into the process of choosing an overtaking strategy have been made due to the lack of efficient models to mathematically quantify the trade-off strategy considerations. To derive the reward function that manifests the e-bicycle’s overtaking strategy, this paper applied a maximum-entropy-based inverse reinforcement learning method on realistic e-bicycle overtaking trajectories. Strategy features, including driving kinematics, driving style and safety during an overtaking task, were considered by the reward function. Further, by leveraging population-level and individual-level reward functions, this study analyzed the importance and preference of each strategy feature, the coordinated manipulations on a set of features and the heterogeneity caused by individuals in three e-bicycle overtaking types. The findings are an important step in understanding e-bicycle behaviours and developing agent-based bicycle simulation models.