8 citations · 17 across the 4 of their papers we have counts for
4 papers · 1 filter
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…
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…
Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting
Tanwi Mallick, Prasanna Balaprakash, Eric Rask +1
Highway traffic modeling and forecasting approaches are critical for intelligent transportation systems. Recently, deep-learning-based traffic forecasting methods have emerged as s…
Graph-Partitioning-Based Diffusion Convolutional Recurrent Neural Network for Large-Scale Traffic Forecasting
Tanwi Mallick, Prasanna Balaprakash, Eric Rask +1
Traffic forecasting approaches are critical to developing adaptive strategies for mobility. Traffic patterns have complex spatial and temporal dependencies that make accurate forec…