Traffic Congestion Analysis and Improvement Strategies for the Amolapatty Intersection Using PTV VISSIM

Rapid urbanization and increasing vehicular demand have intensified congestion at urban intersections, resulting in longer travel times, delays, and reduced operational efficiency. This study evaluates traffic conditions and improvement strategies for the Amolapatty intersection in Dibrugarh, Assam, a junction connecting Dibrugarh University, Dibrugarh town, and commercial areas. Traffic data were collected through field observations for 30 days, with four hours of observation per day, covering traffic volume, average speed, vehicle composition and classification, queue length, delay, and signal operations. A microscopic traffic model was developed in PTV VISSIM using traffic and geometric characteristics. The model was calibrated by iteratively adjusting driving-behavior and traffic-flow parameters and comparing simulated outputs with field observations. Validation was performed using the GEH statistic, adopting GEH < 5 for at least 85% of traffic counts as the acceptance criterion. To account for stochastic variability, 10 simulation runs with different random seeds were conducted for each scenario, and mean outputs were used for evaluation. Capacity and saturation were assessed by progressively increasing demand from the observed baseline until operational deterioration occurred. Under existing conditions, the University–Dibrugarh Town approach was the most congested, with maximum queue lengths of 271.11–322.22 m, average delay of 21.96 s, density of 41.9 vehicles/km, and average speed of 20.62 km/h. The intersection reached saturation at approximately 30% above baseline demand. Three improvement scenarios—an additional lane, one-way traffic operation, and optimized signal timings—were evaluated under consistent simulation assumptions. The additional-lane scenario accommodated demand up to 79% above baseline. The one-way scenario reduced maximum queue length by 34.6% and average delay by 24.9%, while signal optimization increased capacity from 1,131 to 1,357 vehicles/h (20.0%) and improved operational indicators. These results demonstrate the potential of these measures to improve intersection performance and support congestion-management planning.