Analysing Capacity and Delay Parameters for Signalised Arterials with Freight Delivery for the Ratanpole Corridor of Ahmedabad City through a Microsimulation Model

Abstract

Freight deliveries on signalized city roads frequently result in lane blockages, exacerbating urban traffic congestion. This issue has gained prominence as traffic engineers and city planners strive for sustainable solutions amidst constrained road capacities. This study's core aim is to evaluate a model tailored to assess the impact of freight deliveries on both capacity and delay along signalized city roads in Ahmedabad. Modeled after the methodology delineated in the Highway Capacity Manual (HCM2010), this research endeavors to shed light on the application of analytical tools in urban freight delivery policies. The investigation delves into estimating delay and vehicle capacity, factoring in variables like delivery locations, durations, and their differential effects across lanes. Leveraging machine learning techniques, particularly Support Vector Machines and Artificial Neural Networks, this study predicts vehicle capacity and estimates delays. The findings illustrate a strong concurrence between our predictions and actual observations, signifying the efficacy of the employed methodologies in assessing and managing freight-related traffic impacts in urban settings.