50 citations · 206 across the 34 of their papers we have counts for
8 papers · 1 filter
Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System
Xiaotong Guo, Hanyong Xu, Dingyi Zhuang +2
The rapid growth of the ride-hailing industry has revolutionized urban transportation worldwide. Despite its benefits, equity concerns arise as underserved communities face limited…
Large Language Models for Travel Behavior Prediction
Baichuan Mo, Hanyong Xu, Ruoyun Ma +4
Travel behavior prediction is a core problem in transportation demand management and is traditionally addressed using numerical models calibrated on observed data. With recent adva…
Uncertainty-Aware Probabilistic Graph Neural Networks for Road-Level Traffic Accident Prediction
Xiaowei Gao, Xinke Jiang, Dingyi Zhuang +4
Traffic accidents present substantial challenges to human safety and socio-economic development in urban areas. Developing a reliable and responsible traffic accident prediction mo…
Uncertainty Quantification in the Road-level Traffic Risk Prediction by Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network(STZINB-GNN)
Xiaowei Gao, James Haworth, Dingyi Zhuang +2
Urban road-based risk prediction is a crucial yet challenging aspect of research in transportation safety. While most existing studies emphasize accurate prediction, they often ove…
Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction
Xinke Jiang, Dingyi Zhuang, Xianghui Zhang +3
Understanding Origin-Destination (O-D) travel demand is crucial for transportation management. However, traditional spatial-temporal deep learning models grapple with addressing th…
ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-temporal Graph Attention and Bidirectional Recurrent United Neural Networks
Zepu Wang, Dingyi Zhuang, Yankai Li +4
Traffic data serves as a fundamental component in both research and applications within intelligent transportation systems. However, real-world transportation data, collected from…
Fairness-enhancing deep learning for ride-hailing demand prediction
Yunhan Zheng, Qingyi Wang, Dingyi Zhuang +2
Short-term demand forecasting for on-demand ride-hailing services is one of the fundamental issues in intelligent transportation systems. However, previous travel demand forecastin…
Uncertainty Quantification of Spatiotemporal Travel Demand with Probabilistic Graph Neural Networks
Qingyi Wang, Shenhao Wang, Dingyi Zhuang +2
Recent studies have significantly improved the prediction accuracy of travel demand using graph neural networks. However, these studies largely ignored uncertainty that inevitably…