111 citations · 469 across the 13 of their papers we have counts for
6 papers · 1 filter
Adapting Node-Place Model to Predict and Monitor COVID-19 Footprints and Transmission Risks
Jiali Zhou, Mingzhi Zhou, Jiangping Zhou +1
The node-place model has been widely used to classify and evaluate transit stations, which sheds light on individual travel behaviors and supports urban planning through effectivel…
Cross-Mode Knowledge Adaptation for Bike Sharing Demand Prediction using Domain-Adversarial Graph Neural Networks
Yuebing Liang, Guan Huang, Zhan Zhao
For bike sharing systems, demand prediction is crucial to ensure the timely re-balancing of available bikes according to predicted demand. Existing methods for bike sharing demand…
Quantifying COVID-19 transmission risks based on human mobility data: A personalized PageRank approach for efficient contact-tracing
Jiali Zhou, Zhan Zhao, Jiangping Zhou
Given its wide-ranging and long-lasting impacts, COVID-19, especially its spatial spreading dynamics has received much attention. Knowledge of such dynamics helps public health pro…
Activity-aware Human Mobility Prediction with Hierarchical Graph Attention Recurrent Network
Yihong Tang, Junlin He, Zhan Zhao
Human mobility prediction is a fundamental task essential for various applications in urban planning, location-based services and intelligent transportation systems. Existing metho…
A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards
Zhan Zhao, Yuebing Liang
Route choice modeling is a fundamental task in transportation planning and demand forecasting. Classical methods generally adopt the discrete choice model (DCM) framework with line…
Bike Sharing Demand Prediction based on Knowledge Sharing across Modes: A Graph-based Deep Learning Approach
Yuebing Liang, Guan Huang, Zhan Zhao
Bike sharing is an increasingly popular part of urban transportation systems. Accurate demand prediction is the key to support timely re-balancing and ensure service efficiency. Mo…