3 citations · 4 across the 2 of their papers we have counts for
2 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…