Systematic review of the literature on big data in the transportation domain: concepts and applications

Author
Neilson, Alex
Indratmo, Indratmo
Daniel, Ben
Tjandra, Stevanus
Faculty Advisor
Date
2019
Keywords
Big Data , smart city , intelligent transportation system , connected vehicle , road traffic safety , Vision Zero
Abstract (summary)
Research in Big Data and analytics offers tremendous opportunities to utilize evidence in making decisions in many application domains. To what extent can the paradigms of Big Data and analytics be used in the domain of transport? This article reports on an outcome of a systematic review of published articles in the last five years that discuss Big Data concepts and applications in the transportation domain. The goal is to explore and understand the current research, opportunities, and challenges relating to the utilization of Big Data and analytics in transportation. The review shows the potential of Big Data and analytics to garner insights and improve transportation systems through the analysis of various forms of data obtained from traffic monitoring systems, connected vehicles, crowdsourcing, and social media. We discuss some platforms and software architecture for the transport domain, along with a wide array of storage, processing, and analytical techniques, and describe challenges associated with the implementation of Big Data and analytics. This review contributes broadly to the various ways in which cities can utilize Big Data in transportation to guide the creation of sustainable and safer traffic systems. Since research in Big Data and transportation is, by and large, at infancy, this article does not prescribe recommendations to the various challenges identified, which also constitutes the limitation of the article.
Publication Information
Nielson, A., Indratmo, Daniel, B., & Tjandra, S. (2019). Systematic review of the literature on big data in the transportation domain: concepts and applications. Big Data Research. Advance online publication. doi:10.1016/j.bdr.2019.03.001
DOI
Notes
Item Type
Article
Language
English
Rights
Attribution-NonCommercial-NoDerivs (CC BY-NC-ND)