A Comprehensive Review on Real-Time Traffic Signal Coordination Using Machine Learning: Optimizing Urban Traffic Flow for Efficiency and Sustainability

Abstract

Abstract: Cities globally grapple with escalating traffic congestion stemming from urban expansion, aginginfrastructure, suboptimal traffic signal coordination, and a deficiency in real-time data. INRIX quantified the resulting toll at $305 billion for U.S. commuters in 2020, underscoring the pressing need for cities to adopt innovative strategies within the constraints of building new roads. This challenge is further complicated by disparate data collection times at various junctions, creating a mosaic of information from different periods. Some junctions provide limited or sporadic data, necessitating careful consideration for future projections. To address these challenges, cities must embrace cutting-edge approaches that integrate real-time data, efficient traffic management systems, and forward-thinking projections, enabling them to navigate the intricacies of contemporary urban mobility and pioneer sustainable solutions for the future.