September 9, 2023, Filed Under: News & Events, SpeakersReal-Time Forecasting of Dockless Scooter-Sharing Demand: A Spatio-Temporal Multi-Graph Transformer Approach Speaker: Yiming Xu, PhD | Postdoctoral Fellow | School of Architecture, UT Time: September 8, 2023, 2:00 – 2:30 PM (CST) Zoom Link: https://utexas.zoom.us/j/91425104430 Abstract: Accurately forecasting the real-time travel demand for dockless scooter-sharing is crucial for the planning and operations of transportation systems. This study proposes a novel deep learning architecture named Spatio-Temporal Multi-Graph Transformer (STMGT) to forecast the real-time spatiotemporal dockless scooter-sharing demand. The proposed model uses a graph convolutional network (GCN) based on adjacency graph, functional similarity graph, demographic similarity graph, and transportation supply similarity graph to attach spatial dependency to temporal input. The output of GCN is subsequently processed with weather condition information by the Transformer to capture temporal dependency. The proposed model is evaluated for two real-world case studies in Washington, D.C. and Austin, TX, respectively, and the results show that for both case studies, STMGT significantly outperforms all the selected benchmark models, and the most important model component is the weather information. The proposed model can help micromobility operators develop optimal vehicle rebalancing schemes and guide cities to better manage dockless scooter-sharing operations. About: Yiming Xu is a Postdoctoral Fellow in The School of Architecture at the University of Texas at Austin. He received his B.E. and M.E. degrees in transportation engineering from Tongji University, China in 2016 and 2019, and his Ph.D. in civil engineering from the University of Florida in 2023. His research focuses on developing and applying data-driven methods to tackle challenging problems in transportation systems. He specializes in data science, trustworthy machine learning, and deep learning applications in travel behavior analysis and time series modeling to support urban mobility management and operation. Share this:
