Electric bike navigation comfort in pedestrian crowds

Abstract

The emergence of electric bikes (e-bikes) has brought a paradigm shift in shared mobility with a promise to move towards the mission of sustainable cities. Whereas an in-depth understanding of e-bike riding characteristics is crucial to effectively design the infrastructure for active mobility, it remains an open area of research. We take the first step towards modelling the e-bike navigation comfort in pedestrian crowds. Through a laboratory-controlled field experiment, we collect trajectories of e-bike riders under different pedestrian crowding levels in both opposite- (meeting) and same-direction (passing) encounters. For each trajectory, we obtain e-bike speed, e-bike lateral distance, and pedestrian crowding after processing the data obtained from four stationary cameras. Considering the riding comfort as a latent variable, we adopt a Bayesian network to represent the relationship between observed and the latent variables. Subsequently, we use fundamental principles of conditional probability to identify the causal effect of pedestrian crowding on e-bike riding comfort. Controlling for the demographic heterogeneity, we also estimate the relationship between the comfort of an e-bike rider, pedestrian crowding, and her riding characteristics (e.g., speed and lateral distance). The results of this study would guide policymakers in ex-ante evaluations of the infrastructure decisions for active mobility.