EXPLORING AI FOR EFFECTIVE TRAFFIC PREDICTION AND MITIGATING URBAN TRAFFIC CHALLENGES - A RECONNAISSANCE

Abstract

The rapid urbanization and rising population density in cities have significantly increased traffic congestion, leading to adverse effects on economic productivity, environmental sustainability, and quality of life. Accurate traffic prediction is crucial for mitigating these challenges and ensuring efficient transportation systems. Traditional traffic forecasting models often struggle with nonlinear traffic patterns, which has propelled the adoption of advanced artificial intelligence (AI) techniques, particularly machine learning (ML) and deep learning (DL). These approaches excel in processing large, dynamic datasets and capturing intricate spatial-temporal dependencies in traffic data.

Ensemble learning methods, which combine multiple ML and DL models, have emerged as a robust solution to address the limitations of individual models. Techniques such as bagging, boosting, and stacking enhance prediction accuracy by leveraging the strengths of diverse algorithms and mitigating errors. Recent advancements, including hybrid

models like CNN-LSTMs and attention-based frameworks, demonstrate significant improvements in forecasting complex urban traffic conditions.

This study thoroughly explores the application of AI-driven traffic prediction methodologies, emphasizing the potential of ensemble learning in overcoming challenges like data sparsity, scalability, and real-time demands. The outcomes of this exploration extend beyond traffic prediction to encompass critical areas of congestion management, pollution control, and road utilization. The integration of AI enables accident detection, dynamic routing, and public transport planning, contributing to safer and more efficient urban mobility systems.

By leveraging AI for smart city integration, this research also highlights its application in emergency services and weather impact analysis, ensuring robust responses to environmental and operational challenges. Reviewing state-of-the-art models and their integration into Intelligent Transportation Systems (ITS), this study provides a framework for sustainable urban mobility. The findings aim to guide researchers and practitioners in developing reliable, efficient, and scalable traffic management solutions, advancing smart city initiatives by enhancing transportation efficiency and reducing congestion.