ANALYSIS OF AI-ENABLED ADAPTIVE TRAFFIC CONTROL SYSTEMS FOR URBAN MOBILITY OPTIMIZATION THROUGH INTELLIGENT ROAD NETWORK MANAGEMENT

Abstract

Urban traffic congestion remains a critical challenge for transportation infrastructure, with significant impacts on economic productivity, environmental sustainability, and commuter well-being. This meta-analysis investigates the role of Artificial Intelligence (AI)-enabled Adaptive Traffic Control Systems (ATCS) in mitigating urban congestion and enhancing mobility performance, integrating findings from 68 empirical studies and government performance datasets spanning 2010–2024. The analysis draws heavily on annual congestion statistics reported by the Federal Highway Administration (FHWA), particularly from 2022 and 2023. Empirical data reveal persistent trends in urban congestion. In 2022, U.S. urban areas experienced an average of 2 hours and 55 minutes of daily congestion, improving by 10 minutes from 2021. The Travel Time Index (TTI) rose from 1.19 to 1.22, while the Planning Time Index (PTI)—indicating travel reliability—jumped from 1.72 to 1.80. In 2023, although congested hours further decreased to 2 hours and 45 minutes, average congestion (TTI) worsened to 1.24, and PTI increased again to 1.88, reflecting growing travel time unpredictability.  AI-enabled ATCS implementations, particularly those using Reinforcement Learning (RL), demonstrated measurable reductions in congestion across pilot deployments. Synthesized results show that AI-driven systems reduce average vehicle delay by 24% to 36%, intersection queuing by 28%, and overall travel time by up to 19% compared to pre-implementation baselines. Multi-agent Deep RL strategies exhibited superior scalability and adaptation under dynamic flow conditions, while hybrid models (e.g., fuzzy logic + neural nets) enhanced performance during atypical events such as construction detours and emergency reroutes. Importantly, this meta-analysis identifies that regions with AI-supported traffic signal optimization—especially those leveraging real-time data from the NPMRDS (National Performance Management Research Data Set)—achieved notably higher improvements in throughput and lower TTI variability. Case studies, such as Tennessee DOT’s use of crowdsourced and sensor data during the I-40 bridge closure, demonstrate the operational value of intelligent systems in supporting incident management and routing optimization. These findings underscore the strategic importance of deploying AI-based adaptive systems within the broader framework of Intelligent Transportation Systems (ITS) and Smart City planning. The paper concludes with implementation recommendations focused on infrastructure readiness, data integration standards, and policy harmonization for sustainable urban mobility.