18 citations · 70 across the 8 of their papers we have counts for
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cs.NE2023★ 1 cited
Training Physics-Informed Neural Networks via Multi-Task Optimization for Traffic Density Prediction
Bo Wang, A. K. Qin, Sajjad Shafiei +3
Physics-informed neural networks (PINNs) are a newly emerging research frontier in machine learning, which incorporate certain physical laws that govern a given data set, e.g., tho…
cs.LG2023★ 6 cited
TrafFormer: A Transformer Model for Predicting Long-term Traffic
David Alexander Tedjopurnomo, Farhana M. Choudhury, A. K. Qin
Traffic prediction is a flourishing research field due to its importance in human mobility in the urban space. Despite this, existing studies only focus on short-term prediction of…