Elaris Computing Nexus
Quantum Inspired Swarm Intelligence for Real Time Adaptive Traffic Signal Control in Urban Transportation Networks
Elaris Computing Nexus
Received On : 30 July 2025
Revised On : 28 September 2025
Accepted On : 03 November 2025
Published On : 10 November 2025
Volume 01, 2025
Pages : 219-229
Abstract
Traffic congestion in the city is one of the most enduring problems in contemporary transport system and it may increase delays in travelling, fuel usage, and environmental degradation. In a bid to solve this problem, this paper presents a Quantum-Inspired Swarm Adaptive Traffic Control (QIS-ATC) model that can be used to optimize real-time signals in a network of intersections. The model will be the integration of the exploratory strength of quantum-inspired computation and the adaptative coordination of swarm intelligence to adapt traffic signal phases dynamically and data-driven based on the dynamic vehicle densities. The suggested QIS-ATC algorithm is applied and tested using a wide range of MATLAB simulations and compared to five available methods such as Fixed Time Control (FTC), Adaptive Control (AC), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). The results of the experiments show that, QIS-ATC results in a 30-45 percent increase in the total efficiency of traffic flows, 20-35 percent in the reduction of the mean value of a waiting time and a significant decrease in the queue sizes, in comparison with the current techniques. Moreover, the model has quicker convergence and constant green phase assignment and optimized throughput across intersections. These findings underscore the prospects of QIS-ATC as a scalable and smart control system to smart city infrastructures where adaptive and sustainable traffic control is critical to enhancing mobility in the city and congestion alleviation.
Keywords
Quantum-Inspired Optimization, Swarm Intelligence, Adaptive Traffic Control, Real-Time Signal Optimization, Intelligent Transportation Systems, Congestion Management, Urban Mobility, Smart Cities.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Anandakumar Haldorai; Methodology: Bui Hong Quang; Software: Bui Hong Quang; Data Curation: Bui Hong Quang; Writing-Original Draft Preparation: Bui Hong Quang and Anandakumar Haldorai; Visualization: Anandakumar Haldorai; Investigation: Anandakumar Haldorai; Supervision: Anandakumar Haldorai; Validation: Bui Hong Quang and Anandakumar Haldorai; Writing-Reviewing and Editing: Bui Hong Quang and Anandakumar Haldorai; All authors reviewed the results and approved the final version of the manuscript.
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Cite this Article
Bui Hong Quang and Anandakumar Haldorai, “Quantum Inspired Swarm Intelligence for Real Time Adaptive Traffic Signal Control in Urban Transportation Networks”, Elaris Computing Nexus, pp. 219-229, 2025, doi: 10.65148/ECN/2025020.
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© 2025 Bui Hong Quang and Anandakumar Haldorai. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.