Systematic Review of the Literature on Big Data in the Transportation Domain

Concepts and Applications

Abstract

Research in Big Data and analytics offers tremendous opportunities to conscientiously utilize evidence in making decisions in many application domains. To what extent can the paradigms of Big Data and analytics be utilized in the domain of transport? This article reports on an outcome of a systematic analysis of published articles in the last five years that discuss Big Data concepts and applications in the transportation domain. The goal was to explore and understand opportunities and challenges relating to the utilization of Big Data and analytics in transportation reported in the literature. The review revealed that Big Data and analytics can help garner insights through the analysis of various forms of data generated by sensors, cameras, GPS, smartphones, and connected vehicles. The literature also indicated that data obtained from traffic monitoring systems that generate data in the form of images, real-time video feeds, and posts on social media can be utilized to reveal insights to improve transportation systems. We identified a number of platforms or architectures that are commonly used by Big Data in the transport domain, along with a wide array of storage, processing, and analytical techniques. A number of challenges associated with the implementation of Big Data and analytics are also identified. The research presented in this article broadly contributes to the various ways in which cities can utilize Big Data in transportation to guide the creation of sustainable and safer traffic systems.

Discipline: Computer Sciences

Faculty Mentor: Dr. Indratmo