Predicting Traffic Congestion in Mumbai Using Artificial Intelligence

Abstract

Abstract — Mumbai's ongoing traffic problems are brought on by the city's fast urbanization, population expansion, and growing reliance on private automobiles. The majority of traffic management technologies in use today are reactive, frequently reacting only after congestion has already arisen. Using information from a variety of sources, such as commuter surveys, weather reports, and traffic incident logs, this study explores the application of artificial intelligence (AI) and machine learning (ML) techniques to forecast traffic congestion levels in Mumbai. Accuracy, precision, recall, and F1-score were used to train and assess the Random Forest, CatBoost, and XGBoost models. The best accuracy of 88% was attained using a stacking ensemble technique. The results show the potential of AI-based models for proactive traffic management and enhanced urban mobility, despite sample size limitations and the lack of real-time data.