34 citations · 90 across the 16 of their papers we have counts for
5 papers · 1 filter
ICN: Interactive Convolutional Network for Forecasting Travel Demand of Shared Micromobility
Yiming Xu, Qian Ke, Xiaojian Zhang +1
Accurate shared micromobility demand predictions are essential for transportation planning and management. Although deep learning models provide powerful tools to deal with demand…
Real-Time Forecasting of Dockless Scooter-Sharing Demand: A Spatio-Temporal Multi-Graph Transformer Approach
Yiming Xu, Xilei Zhao, Xiaojian Zhang +1
Accurately forecasting the real-time travel demand for dockless scooter-sharing is crucial for the planning and operations of transportation systems. Deep learning models provide r…
Estimating Wildfire Evacuation Decision and Departure Timing Using Large-Scale GPS Data
Xilei Zhao, Yiming Xu, Ruggiero Lovreglio +5
With increased frequency and intensity due to climate change, wildfires have become a growing global concern. This creates severe challenges for fire and emergency services as well…
Do e-scooters fill mobility gaps and promote equity before and during COVID-19? A spatiotemporal analysis using open big data
Xiang Yan, Wencui Yang, Xiaojian Zhang +3
The growing popularity of e-scooters and their rapid expansion across urban streets has attracted widespread attention. A major policy question is whether e-scooters substitute exi…
Micromobility Trip Origin and Destination Inference Using General Bikeshare Feed Specification (GBFS) Data
Yiming Xu, Xiang Yan, Virginia P. Sisiopiku +3
Emerging micromobility services (e.g., e-scooters) have a great potential to enhance urban mobility but more knowledge on their usage patterns is needed. The General Bikeshare Feed…