Bicycle Longitudinal Motion Modeling

TL;DR

This paper adapts a car-following traffic flow model to accurately simulate bicyclist longitudinal motion, validated against real-world data and compared with existing models, offering improved tunability and robustness.

Contribution

The study re-parametrizes the Fadhloun-Rakha model for bicyclists, enabling explicit tuning of physical and environmental factors, and validates its effectiveness against state-of-the-art models.

Findings

The proposed model produces errors comparable to existing models.

It allows straightforward tuning of cyclist and environment parameters.

The model demonstrates robustness through sensitivity analysis.

Abstract

This research effort uses vehicular traffic flow techniques to model bicyclist longitudinal motion while accounting for bicycle interactions. Specifically, an existing car-following model, the Fadhloun-Rakha (FR) model is re-parametrized to model bicyclists. Initially, the study evaluates the performance of the proposed model formulation using experimental datasets collected from two ring-road bicycle experiments; one conducted in Germany in 2012, and the second in China in 2016. The validation of the model is achieved through investigating and comparing the proposed model outputs against those obtained from two state-of-the-art models, namely: the Necessary Deceleration Model (NDM), which is a model specifically designed to capture the longitudinal motion of bicyclists; and the Intelligent Driver Model, which is a car-following model that was demonstrated to be suitable for single-file bicycle traffic. Through a quantitative and qualitative evaluation, the proposed model formulation is demonstrated to produce modeling errors that are consistent with the other two models. While all three models generate trajectories that are consistent with empirically observed bicycle-following behavior, only the proposed model allows for an explicit and straightforward tuning of the bicyclist physical characteristics and the road environment. A sensitivity analysis, demonstrates the effect of varying the different model parameters on the produced trajectories, highlighting the robustness and generality of the proposed model.