Machine Learning Approaches for Micromobility User Behavior Analysis

With widespread adoption globally, micromobility like bikes, e-scooters, and e-bikes has attracted increasing attention due to its ability to complement existing transportation modes and promote sustainable transportation. Understanding micromobility user behaviors in urban areas is essential for improving safety and comfort, as well as for informing infrastructure development and policy. Prior investigations on micromobility user behaviors primarily relied on statistical and kinematic modeling approaches. Although these methods have proven effective in characterizing user behaviors at both macroscopic and microscopic levels, the advent of artificial intelligence (AI)-powered data analytics and behavioral modeling is revolutionizing the field. Recently, advanced machine learning models, such as gradient boosting decision tree, graph convolutional network, and inverse reinforcement learning, has introduced new momentum into micromobility user behavior research. This article explores recent developments, research opportunities, and future directions in this field, leveraging the power of more generic AI approaches.