Impact of Real-Time Traffic Data on Urban Congestion Mitigation: A Case Study from Los Angeles

Abstract

Urban congestion remains a pressing challenge for cities worldwide, significantly contributing to increased travel times, emissions, and economic inefficiencies. This study investigates the impact of real-time traffic data on urban traffic management strategies in Los Angeles, employing a mixed-methods approach that combines quantitative analysis and qualitative interviews with key stakeholders. Utilizing advanced simulation models and real-time data from the Los Angeles Department of Transportation (LADOT), our findings reveal a 25% reduction in traffic congestion during peak hours when real-time data is integrated into traffic signal systems. Moreover, qualitative insights highlight the importance of stakeholder engagement in optimizing data utilization. This research contributes to the understanding of intelligent transportation systems (ITS) and their potential to enhance urban mobility. The results underscore the necessity for cities to adopt data-driven traffic management solutions to alleviate congestion.