Towards Data Empowered Intelligent Transportation Systems: Use Cases and Future Opportunities

Intelligent Transportation Systems (ITS) is an evolving technology that has many applications to improve traffic safety and management. The utilization of advanced sensors and wireless communications is instrumental in implementing the applications of ITS. Vehicles can regularly transmit their position information to other vehicles on the road and also to the infrastructure units, thus forming a vehicular network. Data shared by vehicles is thus used to improve safety-related decisions, manage the flow of traffic, and reduce carbon emissions. It is thus critical to collect the traffic data efficiently and transmit data to the servers and other vehicles reliably, use intelligent learning models to analyze the data, and quickly compute the data analysis-related tasks. In this paper, an overview of the use of big data in ITS is provided. A brief overview of the ITS and its applications is presented. A review of the recent work in ITS is also explained. A case study of data computation using k-means clustering is added to highlight the use of big data for improving the performance of ITS. In the end, the future opportunities and open challenges related to ITS are presented.

I. Introduction

Intelligent Transportation Systems (ITS) is the technology of the future that will revolutionize the way traffic moves on the road. The current challenges faced by the traffic can be addressed by the ITS technology by providing an updated view of the road situation and vehicle movement, thus making intelligent and informed decisions about the traffic flow [1]. The applications of ITS range from the guidance of drivers to make safe decisions, route guidance for reduced traffic congestion, managing traffic such that carbon emissions are minimized, etc. The car industry is fast moving towards a fully autonomous system where vehicles will make self-decisions based on the accurate and real-time data collection of the vehicles on the roads [2] – [4].