(untitled) (CAHIERS MAGELLANES-NS, 2024)
Freight deliveries on urban roads with traffic signals frequently cause lane blockages, which exacerbates traffic congestion in cities. This issue has gained prominence as traffic engineers and city planners seek sustainable solutions within the constraints of limited road capacities. The focus of this study is to evaluate a model that assesses the impact of freight deliveries on road capacity and delays at signalized intersections in Ahmedabad. Adhering to the guidelines in the Highway Capacity Manual (HCM2010), the research aims to apply analytical tools to urban freight delivery strategies. It investigates delays and vehicle capacity by considering delivery locations, durations, and their varied effects on different lanes. By employing machine learning techniques, such as Support Vector Machines and Artificial Neural Networks, in conjunction with a microsimulation model, the study forecasts vehicle capacity and delays. The findings reveal a strong correlation between predicted and actual outcomes, underscoring the effectiveness of these methods in addressing and managing freight-related traffic challenges in urban settings.