A Comprehensive Analysis on Traffic Prediction Methods for Real-World Deployment: Challenges, Cause and Scope

This study provides a comprehensive overview of modern traffic analysis and congestion prediction methodologies, identifying some limitations inherent in current approaches, spanning data adequacy to algorithmic performance. It examines environmental factors and public transit disruptions as contributors to traffic congestion, emphasizing the complexity of urban traffic management. By addressing these challenges, the study aims to enhance the efficiency and adaptability of traffic management systems. Proposed solutions include predictive traffic management with AI, dynamic traffic signal control, and optimization strategies utilizing deep learning algorithms. It underscores the significance of integrating advanced technologies such as machine learning and AI into transportation infrastructure for improved accuracy and reliability. Overall, the findings offer valuable insights for future research and development in this critical field, shedding light on the current state of traffic analysis and congestion prediction.