Real-Time Predictive Control Strategy Optimization
Abstract
Traffic congestion has lead to an increasing emphasis on management measures for a more efficient utilization of existing infrastructure. In this context, this paper proposes a novel framework that integrates real-time optimization of control strategies (tolls, ramp metering rates, etc.) with guidance generation using predicted network states for Dynamic Traffic Assignment systems. The efficacy of the framework is demonstrated through a fixed demand dynamic toll optimization problem which is formulated as a non-linear program to minimize predicted network travel times. A scalable efficient genetic algorithm is applied to solve this problem that exploits parallel computing. Experiments using a closed-loop approach are conducted on a large scale road network in Singapore to investigate the performance of the proposed methodology. The results indicate significant improvements in network wide travel time of up to 9% with real-time computational performance.
Cite
@article{arxiv.1901.04571,
title = {Real-Time Predictive Control Strategy Optimization},
author = {Samarth Gupta and Ravi Seshadri and Bilge Atasoy and A. Arun Prakash and Francisco Pereira and Gary Tan and Moshe Ben-Akiva},
journal= {arXiv preprint arXiv:1901.04571},
year = {2024}
}