Data-driven based double-layer bicycle simulation model

Tianlu Mao, Zhong Fang, Qinyuan Yan, Ruoyu Meng, Shaohua Liu, Zhaoqi Wang

Abstract: Bicycle motion simulation is fundamental to urban transportation planning, virtual reality and other areas. This article proposes a data-driven based double-layer bicycle simulation model to consider the cyclist's decision-making process and the bicycle's kinematic structure. This proposed model consists of two layers, the decision-making layer and the motion layer. First, the decision-making layer using machine learning algorithms models the decision-making process as a regression problem to output the cyclist's decision. Then, the motion layer applies a bicycle kinematics model to output bicycle motion under physical constraints. In addition, a solution to calculate bicycles' dynamic information is proposed for the data-driven method. Quantitative and qualitative experiments have been conducted, and results show that the double-layer model and the parameter calculation solution can generate realistic bicycle motion simulations.

External IDs:dblp:journals/jvca/MaoFYMLW23