Nonlinear Influence and Interaction Effect on the Imbalance of Metro-Oriented Dockless Bike-Sharing System

Abstract

Dockless Bike-Sharing (DBS) is an eco-friendly, convenient, and popular form of ride-sharing. Metro-oriented DBS systems have the potential to promote sustainable transportation. However, the availability of DBS near metro stations often suffers from either scarcity or overabundance. To investigate the factors contributing to this imbalance, this paper examines the nonlinear influences and interactions that impact the DBS system near metro stations, with Shenzhen, China serving as a case study. An ensemble learning approach is employed to predict the imbalance state. Then, the machine learning interpretation method (i.e., SHapley Additive exPlanations) is used to quantify the contribution of effects, discover the strength of interactions between factors and uncover their underlying interactive connections. The results indicate the influence of external factors and the relations between pairwise variables (e.g., road density and the day of the week) for each imbalanced state. Provide two quantized sets of factors that can result in the supply-demand imbalance and support future transport planning decisions to enhance the accessibility and sustainability of Metro-oriented DBS systems.

Comparison of the Workday and Non-Workday Carbon Emission Reduction Benefits of Bikeshare as a Feeder Mode of Metro Stations

“…Global warming has attracted widespread attention around the world, and more and more countries are devoting their efforts to carbon emission reduction actions [1][2][3][4]. As a low-carbon and environmentally friendly green mode of transport, bikeshare is receiving increasing attention in the process of sustainable urbanization, with its features of reducing vehicle miles traveled (VMT), lowering vehicle emissions, easing traffic congestion, increasing commuting alternatives, facilitating public transit use, high travel convenience, and low use costs [5][6][7][8].…”