3 papers
cs.LG2024
Predicting the duration of traffic incidents for Sydney greater metropolitan area using machine learning methods
Artur Grigorev, Sajjad Shafiei, Hanna Grzybowska +1
This research presents a comprehensive approach to predicting the duration of traffic incidents and classifying them as short-term or long-term across the Sydney Metropolitan Area.…
cs.NE2023
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…
physics.soc-ph2022
Traffic disruption modelling with mode shift in multi-modal networks
Dong Zhao, Adriana-Simona Mihaita, Yuming Ou +6
A multi-modal transport system is acknowledged to have robust failure tolerance and can effectively relieve urban congestion issues. However, estimating the impact of disruptions a…