Exploring the Spatiotemporal Patterns of Shared Bicycle Usage: A Case Study of MetroBike in Austin, Texas

Keywords: Shared bicycles, mobility analysis, big geodata, community detection
Abstract Type: Paper Abstract

Abstract

As car dependency rises, Austin, Texas faces increased urban traffic congestion and worsening air pollution due to emissions. To counter these challenges, the City of Austin has expanded bike lanes, introduced shared vehicle parking zones, improved connections to public transit networks, and prioritized equitable access to sustainable transit services. This study investigates the spatiotemporal mobility patterns of Austin's MetroBike. Using the "Austin MetroBike Trips" dataset provided by the Austin government, we analyze trip origins, destinations, and spatiotemporal trends to identify high-use areas and peak times. Methodologically, we apply time series analysis to measure trajectory similarity and employ community detection techniques to uncover clusters of usage patterns and shared travel characteristics. The results can inform city planners and policymakers on optimizing bike infrastructure, enhancing connectivity with other transit modes, and targeting equitable expansion of MetroBike services. This study contributes to a growing work in sustainable urban mobility, providing actionable recommendations for enhancing low-emission transportation in rapidly urbanizing areas.