Moving towards smart transportation with machine learning and Internet of Things (IoT): a review

Internet of Things (IoT) is a new paradigm that is providing enormous services for the innovative technical advances by the researchers. There are so many IoT applications that integrate the physical objects using internet and transfer the collected data over the network without human assistance. Further the collected data by the embedded devices in the objects is analyzed to take important decisions which help the society to use these applications in the different types of services. However, still there is lot of scope to deal with the various problems and apply IoT in the different fields to maximize automation. Transportation is one of such area that determines the safety and quality of life of the people in the society. Due to population growth and development of technology, the demand for the vehicles in the transportation has been increasing continuously. This has attracted the researchers to contribute from different aspects by developing relevant applications and business models to make the transportation smart. The usage of IoT devices has increased the volume of the data and there is need to make this quantitative data as qualitative by using it to make the applications intelligent. So, by applying machine learning (ML) techniques, these applications are becoming intelligent and capable of producing better results by addressing the issues in traffic management and providing safety to the residents. Many researchers have shown keen interest in such techniques and their approaches have helped to find the solution for relevant issues. This paper provides the readers an overview of the IoT technology and application of the ML algorithms proposed by various authors. The focus is that the future researchers can identify the open issues and challenges and adopt various means to work beyond that. In this paper there is an introduction of development of new applications that have used the data from IoT devices thus providing meaningful outcome and make the things intelligent via ML algorithms.