Masuda, Satoki (The University of Tokyo), Hato, Eiji (The University of Tokyo)

Predictive Traffic Control for Evacuation Contraflow Using ZDD Sampling

Scheduled for presentation during the Regular Session "S35b-Optimization, Control, and Learning for Efficient and Resilient ITS" (FR-EA-T35), Friday, November 21, 2025, 14:50−15:30, Surfers Paradise 2

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

Efficient real-time traffic management during evacuations is critical for mitigating the impacts of disasters. This study proposes real-time contraflow operations formulated as a discrete-variable Model Predictive Control (MPC) problem. To address the combinatorial complexity, we develop a novel sampling-based optimization algorithm that integrates Zero-suppressed binary Decision Diagrams (ZDD) with the cross-entropy method. The macroscopic fundamental diagram is employed to aggregate zone-based traffic dynamics, enabling rapid state prediction. Numerical experiments show that the ZDD-based sampling achieves 2.2 to 4.7 times faster computation compared to a baseline method. A case study in Tokyo demonstrates that the proposed controller keeps traffic below critical congestion thresholds and improves trip completion by approximately 15% relative to the scenario without contraflow. These results underscore the potential of discrete-variable MPC with ZDD acceleration as a practical decision-support tool for dynamic traffic management during disasters.