Developing deep learning models for predicting urban bike-sharing usage patterns

Abstract

Urban traffic systems are facing significant challenges due to the ever-growing number of vehicles on the road, leading to increased congestion and suboptimal traffic flow. Traditional research focusing on individual traffic flows is often insufficient to meet the complex demands of modern urban transportation. While studying integrated shared single-vehicle flows offers a potential solution to mitigate these issues, the unique characteristics of shared bikes present substantial obstacles to accurate traffic flow research. These obstacles include the high liquidity, sparsity, and variability of shared bikes, the vagueness of travel characteristics, the lack of correlation between travel groups, and the unpredictability of travel patterns. The study endeavors to confront the challenges above by proposing an innovative model that correlates multiuser interactions and elucidates behavioral dynamics. This model utilizes a deep clustering method to analyze the evolution of superlarge-scale shared bike systems in Beijing. It uncovers the complex mechanisms governing user behavior and employs a neural network algorithm to predict shared bike users’ travel patterns effectively. By focusing on the theoretical and algorithmic aspects of behavioral dynamics for large-scale shared single-vehicle flows, this study offers a unique contribution to the field, with significant implications for multi-traffic flow management and urban planning in scenarios with extensive multi-traffic flows.