Scalable Multi-Objective Optimization for Robust Traffic Signal Control in Uncertain Environments

TL;DR

The paper tackles robust traffic signal control in large, uncertain urban networks by formulating a three-objective optimization problem that jointly minimizes average delay, enhances network stability, and improves robustness against variability. It introduces AHMOA, an Adaptive Hybrid Multi-Objective Optimization Algorithm with memory-based evaluation and an adaptive mix of GA, DE, PSO, and local search to navigate high-dimensional, dynamic search spaces. The method incorporates a memory matrix to stabilize evaluations and a robust objective $R$ to favor solutions that perform consistently across hours and conditions, while modeling uncertainty via multiple evaluations and a dynamic weather factor. Empirical validation across four diverse cities (Manhattan, Istanbul, Paris, São Paulo) demonstrates AHMOA's superior Pareto coverage, scalability, and robustness compared with MOEA/D, NSGA3, and NSDE3, indicating strong potential for real-world, city-wide traffic optimization and future integration with connected-vehicle and IoT infrastructures.

Abstract

Intelligent traffic signal control is essential to modern urban management, with important impacts on economic efficiency, environmental sustainability, and quality of daily life. However, in current decades, it continues to pose significant challenges in managing large-scale traffic networks, coordinating intersections, and ensuring robustness under uncertain traffic conditions. This paper presents a scalable multi-objective optimization approach for robust traffic signal control in dynamic and uncertain urban environments. A multi-objective optimization model is proposed in this paper, which incorporates stochastic variables and probabilistic traffic patterns to capture traffic flow dynamics and uncertainty. We propose an algorithm named Adaptive Hybrid Multi-Objective Optimization Algorithm (AHMOA), which addresses the uncertainties of city traffic, including network-wide signal coordination, fluctuating patterns, and environmental impacts. AHMOA simultaneously optimizes multiple objectives, such as average delay, network stability, and system robustness, while adapting to unpredictable changes in traffic. The algorithm combines evolutionary strategies with an adaptive mechanism to balance exploration and exploitation, and incorporates a memory-based evaluation mechanism to leverage historical traffic data. Simulations are conducted in different cities including Manhattan, Paris, Sao Paulo, and Istanbul. The experimental results demonstrate that AHMOA consistently outperforms several state-of-the-art algorithms and the algorithm is competent to provide scalable, robust Pareto optimal solutions for managing complex traffic systems under uncertain environments.

Figures (6)

• Figure 1: An example of a local area in a large-scale traffic network
• Figure 2: Comparison of different urban networks
• Figure 3: Comparison of Algorithm Performances on Average Intersection Delay in Manhattan City
• Figure 4: Comparison of Algorithm Performances on Average Intersection Delay in Istanbul City
• Figure 5: Comparison of Algorithm Performances on Average Intersection Delay in Paris City