185 citations · 294 across the 6 of their papers we have counts for
6 papers
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.…
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…
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…
Trip Table Estimation and Prediction for Dynamic Traffic Assignment Applications
Sajjad Shafiei, Adriana-Simona Mihaita, Chen Cai
The study focuses on estimating and predicting time-varying origin to destination (OD) trip tables for a dynamic traffic assignment (DTA) model. A bi-level optimisation problem is…
A simple contagion process describes spreading of traffic jams in urban networks
Meead Saberi, Mudabber Ashfaq, Homayoun Hamedmoghadam +8
The spread of traffic jams in urban networks has long been viewed as a complex spatio-temporal phenomenon that often requires computationally intensive microscopic models for analy…
Optimal distance- and time-dependent area-based pricing with the Network Fundamental Diagram
Ziyuan Gu, Sajjad Shafiei, Zhiyuan Liu +1
Given the efficiency and equity concerns of a cordon toll, this paper proposes a few alternative distance-dependent area-based pricing models for a large-scale dynamic traffic netw…