93 citations · 119 across the 11 of their papers we have counts for
11 papers
A Machine Learning Method for Predicting Traffic Signal Timing from Probe Vehicle Data
Juliette Ugirumurera, Joseph Severino, Erik A. Bensen +2
Traffic signals play an important role in transportation by enabling traffic flow management, and ensuring safety at intersections. In addition, knowing the traffic signal phase an…
Attention-based Spatial-Temporal Graph Neural ODE for Traffic Prediction
Weiheng Zhong, Hadi Meidani, Jane Macfarlane
Traffic forecasting is an important issue in intelligent traffic systems (ITS). Graph neural networks (GNNs) are effective deep learning models to capture the complex spatio-tempor…
HumanLight: Incentivizing Ridesharing via Human-centric Deep Reinforcement Learning in Traffic Signal Control
Dimitris M. Vlachogiannis, Hua Wei, Scott Moura +1
Single occupancy vehicles are the most attractive transportation alternative for many commuters, leading to increased traffic congestion and air pollution. Advancements in informat…
Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting
Weiheng Zhong, Tanwi Mallick, Hadi Meidani +2
Accurate traffic forecasting is vital to an intelligent transportation system. Although many deep learning models have achieved state-of-art performance for short-term traffic fore…
Simulating the Impact of Dynamic Rerouting on Metropolitan-Scale Traffic Systems
Cy Chan, Anu Kuncheria, Jane Macfarlane
The rapid introduction of mobile navigation aides that use real-time road network information to suggest alternate routes to drivers is making it more difficult for researchers and…
Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting
Tanwi Mallick, Prasanna Balaprakash, Jane Macfarlane
Deep-learning-based data-driven forecasting methods have produced impressive results for traffic forecasting. A major limitation of these methods, however, is that they provide for…