3 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 1 cited
Knowledge-data fusion oriented traffic state estimation: A stochastic physics-informed deep learning approach
Ting Wang, Ye Li, Rongjun Cheng +3
Physics-informed deep learning (PIDL)-based models have recently garnered remarkable success in traffic state estimation (TSE). However, the prior knowledge used to guide regulariz…
eess.SY2024★ 3 cited
A hybrid neural network for real-time OD demand calibration under disruptions
Takao Dantsuji, Dong Ngoduy, Ziyuan Pu +2
Existing automated urban traffic management systems, designed to mitigate traffic congestion and reduce emissions in real time, face significant challenges in effectively adapting…
cs.LG2024
Inferring Heterogeneous Treatment Effects of Crashes on Highway Traffic: A Doubly Robust Causal Machine Learning Approach
Shuang Li, Ziyuan Pu, Zhiyong Cui +3
Highway traffic crashes exert a considerable impact on both transportation systems and the economy. In this context, accurate and dependable emergency responses are crucial for eff…