3 papers
cs.LG2026
Long-Horizon Traffic Forecasting via Incident-Aware Conformal Spatio-Temporal Transformers
Mayur Patil, Qadeer Ahmed, Shawn Midlam-Mohler +4
Reliable multi-horizon traffic forecasting is challenging because network conditions are stochastic, incident disruptions are intermittent, and effective spatial dependencies vary…
cs.LG2025
Travel Time and Weather-Aware Traffic Forecasting in a Conformal Graph Neural Network Framework
Mayur Patil, Qadeer Ahmed, Shawn Midlam-Mohler
Traffic flow forecasting is essential for managing congestion, improving safety, and optimizing various transportation systems. However, it remains a prevailing challenge due to th…
cs.LG2024
Urban Traffic Forecasting with Integrated Travel Time and Data Availability in a Conformal Graph Neural Network Framework
Mayur Patil, Qadeer Ahmed, Shawn Midlam-Mohler
Traffic flow prediction is a big challenge for transportation authorities as it helps plan and develop better infrastructure. State-of-the-art models often struggle to consider the…