111 citations · 469 across the 13 of their papers we have counts for
13 papers
Deep trip generation with graph neural networks for bike sharing system expansion
Yuebing Liang, Fangyi Ding, Guan Huang +1
Bike sharing is emerging globally as an active, convenient, and sustainable mode of transportation. To plan successful bike-sharing systems (BSSs), many cities start from a small-s…
Quantifying the uneven efficiency benefits of ridesharing market integration
Xiaohan Wang, Zhan Zhao, Hongmou Zhang +2
Ridesharing is recognized as one of the key pathways to sustainable urban mobility. With the emergence of Transportation Network Companies (TNCs) such as Uber and Lyft, the ridesha…
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…
Joint Demand Prediction for Multimodal Systems: A Multi-task Multi-relational Spatiotemporal Graph Neural Network Approach
Yuebing Liang, Guan Huang, Zhan Zhao
Dynamic demand prediction is crucial for the efficient operation and management of urban transportation systems. Extensive research has been conducted on single-mode demand predict…
Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns
Yuebing Liang, Zhan Zhao, Lijun Sun
Missing data is an inevitable and ubiquitous problem for traffic data collection in intelligent transportation systems. Despite extensive research regarding traffic data imputation…
Identifying Hidden Visits from Sparse Call Detail Record Data
Zhan Zhao, Haris N. Koutsopoulos, Jinhua Zhao
Despite a large body of literature on trip inference using call detail record (CDR) data, a fundamental understanding of their limitations is lacking. In particular, because of the…
NetTraj: A Network-based Vehicle Trajectory Prediction Model with Directional Representation and Spatiotemporal Attention Mechanisms
Yuebing Liang, Zhan Zhao
Trajectory prediction of vehicles in city-scale road networks is of great importance to various location-based applications such as vehicle navigation, traffic management, and loca…
Individual Mobility Prediction: An Interpretable Activity-based Hidden Markov Approach
Baichuan Mo, Zhan Zhao, Haris N. Koutsopoulos +1
Individual mobility is driven by demand for activities with diverse spatiotemporal patterns, but existing methods for mobility prediction often overlook the underlying activity pat…