Rademics Research Institute

Abstract

The increasing complexity of urban transportation systems necessitates the adoption of intelligent, adaptive, and decentralized control mechanisms to manage dynamic traffic conditions and optimize route planning. This chapter explores the integration of Multi-Agent Reinforcement Learning (MARL) models into Intelligent Transportation Systems (ITS) for urban traffic signal control and real-time route optimization. Unlike traditional centralized frameworks, MARL enables scalable and distributed decision-making through cooperative agents capable of learning from local interactions and partial observations. The chapter provides a detailed analysis of the underlying principles of MARL, examines the architectural shift from centralized to decentralized learning, and discusses strategies for agent coordination, convergence, and policy optimization. It also highlights the challenges of scalability, dynamic congestion avoidance, and validation in complex traffic networks. The significance of simulation platforms and real-world datasets is addressed, with emphasis on reproducibility and benchmark standardization. By synthesizing state-of-the-art developments, practical considerations, and future research directions, this work positions MARL as a transformative approach for achieving efficient, adaptive, and resilient traffic management in next-generation urban mobility systems.

Introduction

The rapid expansion of urban populations and vehicle ownership has led to significant challenges in the management of traffic systems, particularly in densely populated metropolitan regions [1]. Traditional transportation control methods, often built upon fixed-timing traffic signals and static route assignment models, lack the flexibility and responsiveness required to adapt to real-time variations in traffic demand [2]. These systems are typically reactive rather than predictive, making them inefficient in addressing dynamic congestion, unexpected incidents, or fluctuating travel patterns [3]. As cities strive toward the vision of sustainable, smart urban mobility, there is an increasing demand for advanced technologies that can offer intelligent, data-driven solutions to optimize traffic flow and reduce congestion [4]. The evolution of Intelligent Transportation Systems (ITS) reflects this paradigm shift, as it aims to integrate modern computing, sensing, and communication technologies with transportation infrastructure to create adaptive and efficient urban mobility networks [5].

Artificial Intelligence (AI), and more specifically Reinforcement Learning (RL), has emerged as a promising approach in ITS due to its ability to model complex decision-making tasks through interaction with the environment [6]. Reinforcement Learning enables traffic control agents to learn optimal actions by maximizing long-term rewards based on system feedback, rather than relying on pre-defined rules [7]. In recent years, Multi-Agent Reinforcement Learning (MARL) has gained significant traction for its ability to distribute intelligence across a network of agents, each responsible for managing a localized segment of the transportation system [8]. These agents can interact, share observations, and coordinate actions, leading to more scalable, robust, and context-aware traffic management solutions [9]. Unlike centralized learning frameworks that depend on complete global knowledge and intensive computation, MARL facilitates decentralized control, enhancing real-time responsiveness and operational resilience in large-scale urban environments [10].