Artificial intelligence, machine learning, and deep learning in intelligent transportation systems: A review for smart cities and sustainable mobility
Abstract
With high urbanization rates, traffic congestion, road accidents, generation of carbon release, and transportation inefficiency, the problem of smart mobility growing through intelligent and sustainable transportation and designing is increasing in urban smart cities. Conventional transport systems are often incapable of responding to real-time conditions, dynamic traffic, as well as multimodal portable needs, which has seen a research gap in applying Artificial Intelligence, Machine Learning, and Deep Learning to Intelligent Transportation Systems. The purpose of this review is to critically assess the recent trends, applications, challenges and future opportunities of intelligent transportation technologies in smart cities and sustainable mobility. The review was oriented towards emerging themes such as traffic prediction and traffic flow optimization, intelligent traffic signal control, connected vehicles, autonomous vehicles, vehicle-to-everything communication, smart parking, accident prediction, driver behavior analysis, public transportation optimization, intelligent logistics and carbon emission reduction. In addition, the incorporation of the Internet of Things, Edge Computing, Big Data Analytics, Digital Twin technologies, Explainable Artificial Intelligence, Federated Learning, and Generative Artificial Intelligence is broadening the horizons of smart transportation and sustainable urban development. The review ends by stating that Intelligent Transport Systems that are empowered by Artificial Intelligence can be instrumental to the realization of resilient, efficient, low-carbon, and data-driven urban mobility ecosystems. Nevertheless, issues of cybersecurity, personal data privacy, explainability of the model, infrastructure shortages, and integration of policies still pose significant obstacles to large-scale use in the future.
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