Dockless Shared-Use Mobility: A Pattern Mining Approach

摘要

Human mobility pattern mining is invaluable to transportation decision-making because it enables public and private entities to reduce operational cost, improve system performance, and promote shared mobility modes of transportation. We offer two data mining approaches to a dockless shared mobility service in Washington, D.C. The authors used data collected from two different operators. We outline the geohashing technique combined with data mining methods to provide a promising alternative to the existing model. We intend to provide a better understanding of commuter movements at different levels of spatiotemporal resolution.