Adaptive and Intelligent Urban Traffic Control: An Integrated Framework Combining Swarm Reinforcement Learning, Fuzzy Logic, and Sensor-driven Rerouting for Resilient Expressway Management

Abstract

This paper presents an integrated, theoretically grounded framework for adaptive urban traffic control that synthesizes swarm reinforcement learning, fuzzy logic control, wireless sensor network inputs, and dynamic vehicle rerouting to reduce incident frequency and congestion on urban expressways. The proposed approach is motivated by empirical findings that environmental and traffic conditions significantly influence incident occurrence on expressways (Zhanga, Chen & Tu, 2013), and by converging literature showing the promise of multi-agent reinforcement learning for coordinated signal control (Tahifa, Boumhidi & Yahyaouy, 2015), and fuzzy/neuro-fuzzy strategies for isolated intersection management (Salehi, Sepahvand & Yarahmadi, 2014; Collotta, lo Bello & Pau, 2015; Royani, Haddadnia & Alipoor, 2013). We develop an architectural blueprint that leverages wireless sensor networks to provide real-time traffic and environmental state, uses a cooperative multi-agent swarm reinforcement learning model to coordinate distributed controllers, embeds fuzzy inference layers to manage uncertainty and human-centric constraints, and applies pheromone-inspired and graph-based rerouting strategies to balance network load and minimize propagation of congestion (Jiang, Zhang & Yew-Soon, 2014; Bayraktar et al., 2023). Methodologically, we articulate the model components, state and action spaces, reward shaping, exploration-exploitation trade-offs, and a layered control strategy that allows for graceful degradation and interpretability. We then provide a descriptive analysis of expected outcomes, including reduced incident frequency, improved travel time reliability, and increased resilience to environmental fluctuations and sensor noise, drawing on cross-disciplinary evidence from machine learning, intelligent transport systems, and IoT-enabled urban infrastructure (Agrawal et al., 2023; Al-Ani et al., 2023; Gohar & Nencioni, 2021). We critically examine limitations, including ethical and privacy considerations in sensor networks and crowdsensing, the risk of non-stationarity in traffic dynamics, and computational scalability. Finally, we propose a staged roadmap for field validation, a set of measurable performance metrics, and open research directions that include transfer learning across cities, safety-aware reward formulation, and hybrid symbolic–learning controllers. This article contributes an exhaustive theoretical elaboration that integrates disparate strands of traffic control research into a cohesive, implementable design for modern smart cities.