Intelligent traffic signals: Pivotal innovations for sustainable urban traffic management

Abstract

As living standards rise, there is a noticeable surge in the number of private vehicles. This increase places considerable strain on urban transportation, leading to significant congestion in metropolitan areas. This research delves into the establishment of intelligent traffic control, focusing specifically on the signal light control system to address this growing concern. The realm of intelligent transportation seeks to bolster the efficiency, safety, and environmental sustainability of transit systems. To discern variations and identify potential bottlenecks in traffic flow, the background difference method compares traffic data over distinct time frames. This paper melds the background difference method with the SORT tracking algorithm to meticulously recognize and monitor vehicles. Two timing algorithms, Webster and ARRB, are explored in this study. Both are commonly employed to optimize traffic signal control and amplify the fluidity of traffic movement. Presently, the domain of intelligent transport systems is transitioning to embrace digital advancements, aiming to offer astute, efficient, and user-friendly traffic management solutions.