SUMO-Based Optimization of Intersection Performance under Heterogeneous Traffic
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Abstract
Traffic congestion is one of the most critical challenges in metropolitan cities, where intersections often experience long queues, high delays, and increased emissions due to mixed traffic and reliance on manual police control. This study examined how geometric improvements and signal timing optimization can improve the performance of a busy four-arm intersection in Uttara, Dhaka. Traffic data were collected through field surveys during peak hours and used to build a detailed microsimulation model in SUMO, calibrated with local driving behavior. Three scenarios were tested: the existing manual police control, channelization of turning lanes with current timings, and channelization with optimized fixed-time signals using Webster’s method. Results showed that under current conditions, delays and queues were extremely high, corresponding to the lowest service level. Channelization alone reduced delays by up to 40% and improved overall flow, while the combination of channelization and optimized signals achieved the best outcomes, cutting delays to less than 40 seconds per vehicle, reducing queues by more than 70%, and improving the level of service to stable and acceptable conditions. Environmental analysis confirmed that the optimized scenario lowered fuel consumption and carbon emissions by 25–35% compared to current operations. These findings demonstrate that integrating physical improvements with evidence-based signal control can substantially enhance traffic efficiency, reduce energy use, and improve air quality in rapidly growing cities. The study provides practical insights for policymakers and planners seeking sustainable, low-cost solutions to congestion in Dhaka and other megacities facing similar challenges.
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