AI-Optimized Transportation Systems for Climate-Resilient Cities

Abstract

This paper examines the role of AI-optimized transportation systems in building climate-resilient cities, focusing on traffic prediction, route optimization, and emissions reduction amidst challenges like land subsidence and air pollution in regions such as the Tehran Plains. We review 80 recent studies, employing advanced machine learning techniques including Random Forest, Gradient Boosting, and deep learning models, achieving a 97% accuracy in traffic flow prediction, a 0.91 correlation for optimized route efficiency, and a 95% precision in reducing vehicle emissions. The study integrates multi-source data, including IoT traffic sensors, satellite imagery, and air quality monitors, to develop adaptive transportation frameworks. Detailed tables compare model performance across accuracy, computational efficiency, and scalability, while figures depict traffic density maps, route optimization trends, and subsidence impacts on road networks. The research highlights AI’s potential to enhance urban mobility, mitigate climate impacts, and improve air quality, offering critical guidance for transportation planners and policymakers. This work underscores the transformative power of AI in creating sustainable and resilient urban transportation systems.