A Scalable Multi-Agent Routing Paradigm for Adaptive Traffic Control Networks

International Journal of Computer Science and Technology Vol. 1, No. 11, November 1999 | pp. 87–94

DOI: 10.46882/1999/IJCST/000011

Research Article

Title: A Scalable Multi-Agent Routing Paradigm for Adaptive Traffic Control Networks

Names of Authors: Li Wei-Min¹, Carlos Silva²

Authors’ Affiliations: ¹School of Information Engineering, Zhejiang University, Hangzhou, China; ²Department of Systems Engineering, University of Lisbon, Lisbon, Portugal.

Abstract:
Urban traffic congestion presents severe economic and logistical bottlenecks, demanding intelligent systems capable of adapting to real-time flow variations. Traditional static signal timers fail to mitigate congestion during random peak bursts or sudden lane obstructions. This paper presents an optimized, decentralized multi-agent routing paradigm developed for adaptive signal control across dense metropolitan grids. The framework treats each intersection as an autonomous agent that communicates queue lengths and velocity profiles with adjacent nodes using a localized consensus protocol. We simulated the multi-agent framework across an artificial network modeling 200 highly active transit intersections under fluctuating vehicular stress. The experimental metrics reveal a 22.4% reduction in average vehicle wait time and an 18.5% improvement in peak throughput efficiency compared to conventional fixed-time and actuated signal algorithms. Furthermore, the communication overhead between adjacent intersections remained low, scaling linearly with the grid size without oversaturating the local network channels. The system demonstrates excellent fault tolerance, isolating individual node dropouts without destabilizing the broader regional synchronization matrix. This approach provides a practical framework for municipal traffic management systems seeking to enforce stringent efficiency protocols.

Keywords: Intelligent Transportation, Multi-Agent Systems, Decentralized Control, Traffic Signal Optimization, Network Scaling, Flow Simulation.

Manuscript Timeline: Received: August 14, 1999; Revised: September 21, 1999; Accepted: October 10, 1999; Published: November 24, 1999.

Citation: Li, W. M., & Silva, C. (1999). A Scalable Multi-Agent Routing Paradigm for Adaptive Traffic Control Networks. International Journal of Computer Science and Technology, 1(11), 87–94.

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