Role of Technology in Sustainable Transportation System

Machine learning (ML) is a key topic that addresses fundamental problems concerning the design of computer systems and the underlying statistical principles driving learning processes in the current digital competition scene. In order to improve the efficiency, sustainability, safety, and accessibility of transportation networks, this study investigates the nexus between machine learning and smart transportation. Predictive analytics, route planning, autonomous vehicle development, and traffic management are made possible by machine learning (ML) by utilizing data and algorithms. However, there are issues with data quality, real-time processing, the accuracy of predictive analytics, and policy execution when integrating ML into smart transportation. Notwithstanding these difficulties, machine learning (ML) has enormous potential to transform mobility networks, boost user experiences, optimize infrastructure, and improve safety in the future of smart transportation. In order to guarantee the responsible and equitable implementation of machine learning technologies in smart transportation, interdisciplinary cooperation, ethical considerations, and user-centric design are essential. This study highlights the significance of more research and innovation in this domain by providing insights into the existing landscape, problems, and future directions of machine learning in smart transportation through a thorough assessment and analysis.