Control and Optimization Methods for Traffic Signal Control in Large-scale Congested Urban Road Networks

Aboudolas, Konstantinos; Papageorgiou, Markos; Kosmatopoulos, Elias · 2007 · Crossref

DOI: 10.1109/acc.2007.4282682

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Summary

This paper addresses the challenge of designing real-time traffic signal control strategies for large-scale, congested urban road networks. Motivated by the limitations of existing strategies—which often rely on historical data, heuristics, or computationally expensive algorithms unsuitable for real-time application—the authors propose a generic framework based on the store-and-forward modeling (SFM) paradigm. The primary objective is to minimize and balance link queues to reduce the risk of oversaturation and spillback, particularly under saturated traffic conditions where traditional coordinated systems like SCOOT or SCATS may deteriorate. The study develops three alternative control methodologies derived from the SFM approach. The first is a Linear-Quadratic (LQ) optimal control problem, resulting in a linear multivariable feedback regulator that calculates signal splits in real time, with constraints handled via a knapsack algorithm. The second methodology formulates an open-loop constrained quadratic programming (QPC) problem, introducing independent link green times to better handle queue constraints and nonsaturated conditions. The third methodology employs an open-loop constrained nonlinear optimal control (NOC) problem, utilizing a more accurate nonlinear outflow function and solved via a feasible-direction algorithm. All methods aim to minimize relative link occupancies while respecting physical and operational constraints. To evaluate these methods, the authors conducted a simulation-based investigation using the urban network of Chania, Greece, comprising 16 signalized junctions and 71 links. The performance of the closed-loop LQ regulator was compared against the open-loop QPC and NOC strategies across three scenarios with varying initial queue levels. Results demonstrated that both QPC and NOC outperformed the LQ approach. Specifically, QPC reduced total time spent (TTS) by 4.5% and improved relative queue balance (RQB) by 17.2% compared to LQ. NOC achieved a 6.1% reduction in TTS and a 14.9% improvement in RQB. While NOC was superior in minimizing total time spent due to its more accurate nonlinear model, QPC proved more effective at balancing queues. Computationally, QPC required only 10 seconds per scenario, whereas NOC required 8 minutes, highlighting a trade-off between accuracy and computational speed. The significance of this work lies in demonstrating the efficiency and real-time feasibility of applying rigorous control and optimization methods to large-scale urban networks. The findings suggest that open-loop optimization strategies (QPC and NOC) offer superior performance over linear feedback regulators in congested conditions. Furthermore, the study validates the store-and-forward model as a viable alternative to more complex models like the cell transmission model, offering a balance between modeling accuracy and computational tractability for real-time implementation. The proposed framework provides a robust foundation for developing advanced, traffic-responsive signal control systems capable of handling severe congestion.

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• traffic density