50 citations · 57 across the 5 of their papers we have counts for
5 papers
Time Series Supplier Allocation via Deep Black-Litterman Model
Jiayuan Luo, Wentao Zhang, Yuchen Fang +4
Time Series Supplier Allocation (TSSA) poses a complex NP-hard challenge, aimed at refining future order dispatching strategies to satisfy order demands with maximum supply efficie…
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…
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…
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 Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks
Dingyi Zhuang, Shenhao Wang, Haris N. Koutsopoulos +1
Origin-Destination (O-D) travel demand prediction is a fundamental challenge in transportation. Recently, spatial-temporal deep learning models demonstrate the tremendous potential…