CLEAR: congestion-level estimation and adaptive routing for Indian urban traffic

Abstract

Urban traffic congestion in India poses significant economic and environmental challenges, necessitating early detection for effective management. This study presents a novel approach to real-time congestion prediction, addressing the need for a mechanism that can adapt to India's diverse traffic patterns while minimizing computational complexity. Unlike previous studies that focused on long-term forecasts, this research proposes a lightweight, resource-efficient model capable of predicting congestion levels at 15-minute intervals. The model utilizes a real-time dataset collected from the Palakkad region in southern India. To improve prediction accuracy, the method employs ensemble methods. Experiments have shown that the random forest algorithm is 98.6% accurate at predicting traffic jams, which is better than previous research in this area. Additionally, the study develops a best-route recommendation system based on the experimental results. This research contributes to the field by offering a practical approach to mitigating urban traffic congestion in India, with applications in other cities characterized by diverse traffic patterns. The proposed model's ability to provide accurate short-term predictions while maintaining computational efficiency represents a significant advancement in traffic management strategies for developing urban ecosystems. Furthermore, the study underscores the efficacy of data-driven decision support systems in optimizing urban mobility and reducing vehicular emissions.