Sensing Travel Source–Sink Spatiotemporal Ranges Using Dockless Bicycle Trajectory via Density-Based Adaptive Clustering

The travel source–sink phenomenon is a typical urban traffic anomaly that reflects the imbalanced dissipation and aggregation of human mobility activities. It useful for pertinently balancing facilities optimizing structures to accurately sense spatiotemporal ranges source–sinks, such as public transportation station optimization, sharing resource configurations, or stampede precautions among moving crowds. Unlike remote sensing using visual features, it challenging arbitrarily shaped areas trajectories. This paper proposes density-based adaptive clustering method identify patterns. Firstly, field utilized construct stable neighborhood origin destination points. Then, binary statistical hypothesis tests are proposed source sink core Finally, expansion strategy employed detect spatial temporal durations sources sinks. experiments conducted bicycle trajectory data in Shanghai show can extract significantly events. patterns detected by have practical reference, meaning they provide insights into redistribution bike-sharing resources.