Navigating the Congestion Maze: Geospatial Analysis and Travel Behavior Insights for Dockless Bike-Sharing Systems in Xiamen

Shared bicycles have emerged as a transformative force in urban transportation, effectively addressing the perennial 'last mile' challenge faced by commuters. The limitations of station-based bike-sharing systems, constrained by point-to-point travel, have spurred the popularity of the dockless model, offering flexible rentals and eliminating docking infrastructure constraints. However, the rapid growth of the sharing economy has introduced new challenges, notably an imbalance between supply and demand, leading to issues like the unavailability of bicycles and insufficient parking spaces during peak hours. To address these challenges, this study introduces a novel variable, Congestion Density (C), to quantitatively measure dynamic congestion levels in dockless bicycle-sharing systems. Leveraging real-time shared bike information from Xiamen, China, we present a sophisticated clustering framework for congested spots, identifying 563 congested spots categorized into Over-crowded, Semi-crowded, and Light-crowded clusters. Strikingly, these clusters align with established subway lines and bus stops, revealing a prevalent trend of integration between subway/bus services and bike-sharing. Overall, this study proposes parking lot management plans and policy recommendations based on the dynamics of crowded parking spaces, geographical characteristics, and land functional attributes. Our findings provide crucial insights for implementing bike-sharing electric fences and understanding urban mobility patterns, contributing to sustainable urban transportation.