Adaptive Traffic Signals: A Mitigative Approach to Solving Urban Intersection Congestion

Abstract

Traffic congestion is a pervasive issue that transcends geographical boundaries, affecting both developed and developing nations. While developed countries have been actively pursuing effective solutions, developing economies face distinct challenges owing to infrastructure constraints and improper road utilization. Inadequate infrastructure and inefficient traffic management not only exacerbate congestion but also result in unnecessary delays, wasted fuel, and lost productivity, ultimately impeding societal progress. This study recognizes the importance of addressing traffic congestion, particularly at critical junctions, through the implementation of intelligent traffic signalization systems leveraging artificial intelligence (AI) and optimized capacity utilization. By harnessing the power of AI and coding techniques, the proposed approach aims to mitigate congestion at intersections, thereby enhancing traffic flow, reducing travel times, and minimizing adverse socio-economic impacts. Moreover, the paper explores the role of traffic engineer in facilitating effective traffic management solutions. Ultimately, this research endeavors to contribute to the development of sustainable and efficient urban mobility systems, benefiting both commuters and society at large.