A Review and Analysis of IoT Enabled Smart Transportation Using Machine Learning Techniques

Abstract

This study represents the complex terrain of smart transport applications, focusing on the synergistic potential that emerges from the strategic confluence of Machine Learning (ML) and Internet of Things (IoT) methodologies. This review provides insight into how the dynamic nature and large volume of data created by IoT systems make them an excellent environment for the integration of ML approaches by exploring the interplay between these areas. Notably, a wide range of ML algorithms have been reviewed and suggested in the context of smart transportation, with a focus on critical areas such as route optimization, parking management, and accident detection/prevention. A crucial finding from this investigation is the noticeable gap in ML coverage throughout the range of smart lighting systems and parking applications. This highlights the need to refocus on these topics from an ML standpoint, opening the path for future investigation and innovation. This research tackles important topics including sustainability, cost-effectiveness, safety, and time efficiency, highlighting the fascinating possibilities of fusing IoT, ML, and smart mobility. Proactively preventing accidents, expedited parking reservations, cutting-edge street lighting, and accurate route suggestions are just a few benefits of the integration of these technologies. The study does, however, highlight the need for more research, particularly in unexplored areas like parking applications and smart lighting. By bridging these gaps and improving ML and IoT cooperation, smart transportation will be greatly improved and creative solutions for improved urban mobility will be offered.