Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis

Sepideh Kaffash, An Truong Nguyen and Joe Zhu

International Journal of Production Economics, 2021, vol. 231, issue C

Abstract: The volume and availability of data in the Intelligent Transportation System (ITS) result in the need for data-driven approaches. Big Data algorithms are applied to further enhance the intelligence of the applications in the transportation field. Applying Big Data algorithms has increasingly received attention in both the academic and industrial fields of ITS. Big Data algorithms in ITS have a wide range of applications including but not limited to signal recognition, object detection, traffic flow prediction, travel time planning, travel route planning and safety of vehicle and road. This survey aims to provide a bibliography, a comprehensive review of the application of ITS and a review of most recognized models with Big Data used in the context of ITS. 586 papers are reviewed over the period 1997–2019. This study provides a deep insight into applications of Big Data algorithms in ITS, revealing different areas of those applications and integrates models and applications. The result of the study identifies research gaps and direction for the future.

Keywords: Intelligent transportation system; Big data algorithms; Survey; Traffic flow prediction (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (20)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:231:y:2021:i:c:s0925527320302279

DOI: 10.1016/j.ijpe.2020.107868

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