Basile, Giacomo (Università degli sutdi di Napoli Federico II), Pasquale, Cecilia Caterina (University of Genoa), Petrillo, Alberto (University of Naples Federico II), Sacone, Simona (University of Genova), Santini, Stefania (University of Naples Federico II), Siri, Silvia (University of Genova)

A Multi-Scale Vehicle-Based Traffic Control Architecture Via Deep Reinforcement Learning and CAVs Platooning

Scheduled for presentation during the Invited Session "S24a-Traffic Control and Connected Autonomous Vehicles: benefits for efficiency, safety and beyond" (TH-LM-T24), Thursday, November 20, 2025, 10:50−11:10, Coolangata 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

Connected and Automated Vehicles (CAVs) represent the key enabling technology for reshaping the mobility paradigm and contributing to more efficient, safe, and comfortable ground transportation systems. In this direction, CAVs can be leveraged to develop the vehicle-based control strategies for traffic dynamics regulation and congestion mitigation purposes. To this aim, by leveraging an enriched version of the Cell Transmission Model (CTM), this work proposes a Deep Reinforcement Learning (DRL) multi-scale control architecture, where: i) a Deep Q-Network (DQN) controller, based on the effective traffic conditions, suggests the optimal speed profile and the desired spacing policy to be imposed on CAVs for moving in platoon formation; ii) a distributed PID controller, acting at the microscopic level, aims at properly driving the platoon motion according to the DQN-based traffic control policy. Numerical validations, performed on the real stretch of the A20 freeway in the Netherlands, demonstrate the effectiveness of the proposed multi-scale control architecture in significant reducing the Total Travel Time with improvement of about 18% w.r.t. the uncontrolled traffic flow case.