44 citations · 63 across the 4 of their papers we have counts for
5 papers
Bidirectional yet asymmetric causality between urban systems and traffic dynamics in 30 cities worldwide
Yatao Zhang, Ye Hong, Song Gao +1
Understanding how urban systems and traffic dynamics co-evolve is crucial for advancing sustainable and resilient cities. However, their bidirectional causal relationships remain u…
Deep Generative Model for Human Mobility Behavior
Ye Hong, Yatao Zhang, Konrad Schindler +1
Understanding and modeling human mobility is central to challenges in transport planning, sustainable urban design, and public health. Despite decades of effort, simulating individ…
Context-aware knowledge graph framework for traffic speed forecasting using graph neural network
Yatao Zhang, Yi Wang, Song Gao +1
Human mobility is intricately influenced by urban contexts spatially and temporally, constituting essential domain knowledge in understanding traffic systems. While existing traffi…
Counterfactual Explanations for Deep Learning-Based Traffic Forecasting
Rushan Wang, Yanan Xin, Yatao Zhang +2
Deep learning models are widely used in traffic forecasting and have achieved state-of-the-art prediction accuracy. However, the black-box nature of those models makes the results…
Context-aware multi-head self-attentional neural network model for next location prediction
Ye Hong, Yatao Zhang, Konrad Schindler +1
Accurate activity location prediction is a crucial component of many mobility applications and is particularly required to develop personalized, sustainable transportation systems.…