Binjaku, Kleona (University of Genova), Mece, Elinda (Polytechnic University of Tirana), Pasquale, Cecilia Caterina (University of Genoa), Siri, Silvia (University of Genova), Sacone, Simona (University of Genova)

AI-Based Predictive Ramp-Metering Control for Freeway Traffic Systems

Scheduled for presentation during the Invited Session "Traffic Control and Connected Autonomous Vehicles: benefits for efficiency, safety and beyond (2 edition) II" (WeBT4), Wednesday, September 25, 2024, 14:30−14:50, Salon 8

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

Traffic congestion in freeways poses significant challenges, impacting travel times and environmental sustainability. This paper proposes a novel approach to enhance ramp metering control using predictive traffic insights derived from physics-informed LSTM (Long Short-Term Memory) models. By integrating predictive capabilities with established control strategies like ALINEA, the method dynamically adjusts on-ramp flow rates based on anticipated traffic conditions. Real-world traffic data are used to evaluate the effectiveness of the approach, demonstrating improved performance compared to the adoption of conventional controllers.