Framework for AI-Driven Traffic Management in Kathmandu

Abstract

Traffic congestion was always a critical issue in Kathmandu, particularly in some of the densely populated areas, where traditional traffic management approaches have been inefficient. This research proposes a comprehensive framework for AI based traffic management designed for the unique traffic patterns and infrastructure limitations of Kathmandu. The objective is to leverage Artificial Intelligence (AI), including machine learning algorithms and computer vision technologies, to reduce congestion, enhance traffic flow, and improve commute for the locals. The study used a simulation model to evaluate AI’s potential impact on Kathmandu’s traffic system. Using the Simulation of Urban Mobility (SUMO) tool, traffic scenarios were created to assess how AI-driven systems respond to varying traffic volumes, roads, and peak hours in Kathmandu. A case study analysis of AI-optimized traffic systems in advanced cities and countries like Los Angeles, Beijing and Singapore was conducted to benchmark the potential improvements. Results show that AI systems perform significantly better than traditional methods by reducing congestion by up to 25%, shortening travel times, and improving fuel efficiency. These findings demonstrate the effectiveness of an AI-driven approach to alleviate Kathmandu’s traffic problems and provide a pathway for practical implementation.