6 citations · 8 across the 5 of their papers we have counts for
5 papers
Learning Universal Human Mobility Patterns with a Foundation Model for Cross-domain Data Fusion
Haoxuan Ma, Xishun Liao, Yifan Liu +4
Human mobility modeling is critical for urban planning and transportation management, yet existing approaches often lack the integration capabilities needed to handle diverse data…
NUMOSIM: A Synthetic Mobility Dataset with Anomaly Detection Benchmarks
Chris Stanford, Suman Adari, Xishun Liao +10
Collecting real-world mobility data is challenging. It is often fraught with privacy concerns, logistical difficulties, and inherent biases. Moreover, accurately annotating anomali…
Deploying scalable traffic prediction models for efficient management in real-world large transportation networks during hurricane evacuations
Qinhua Jiang, Brian Yueshuai He, Changju Lee +1
Accurate traffic prediction is vital for effective traffic management during hurricane evacuation. This paper proposes a predictive modeling system that integrates Multilayer Perce…
An Attention-Based Multi-Context Convolutional Encoder-Decoder Neural Network for Work Zone Traffic Impact Prediction
Qinhua Jiang, Xishun Liao, Yaofa Gong +1
Work zone is one of the major causes of non-recurrent traffic congestion and road incidents. Despite the significance of its impact, studies on predicting the traffic impact of wor…
Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis
Xishun Liao, Qinhua Jiang, Brian Yueshuai He +3
Human mobility plays a crucial role in transportation, urban planning, and public health, but current approaches face important limitations. Existing deep learning models tend to o…