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

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  • Kaffash, Sepideh
  • Nguyen, An Truong
  • Zhu, Joe

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.

Suggested Citation

  • Kaffash, Sepideh & Nguyen, An Truong & Zhu, Joe, 2021. "Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:proeco:v:231:y:2021:i:c:s0925527320302279
    DOI: 10.1016/j.ijpe.2020.107868
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    References listed on IDEAS

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    Cited by:

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    14. Qi, Quansong & Xu, Zhiyong & Rani, Pratibha, 2023. "Big data analytics challenges to implementing the intelligent Industrial Internet of Things (IIoT) systems in sustainable manufacturing operations," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    15. Miguel F. Arevalo-Castiblanco & Jaime Pachon & Duvan Tellez-Castro & Eduardo Mojica-Nava, 2023. "Cooperative Cruise Control for Intelligent Connected Vehicles: A Bargaining Game Approach," Sustainability, MDPI, vol. 15(15), pages 1-21, August.
    16. M. Azizur Rahman & Al-Amin Hossain & Binoy Debnath & Zinnat Mahmud Zefat & Mohammad Sarwar Morshed & Ziaul Haq Adnan, 2021. "Intelligent Vehicle Scheduling and Routing for a Chain of Retail Stores: A Case Study of Dhaka, Bangladesh," Logistics, MDPI, vol. 5(3), pages 1-21, September.
    17. Okkie Putriani & Sigit Priyanto & Imam Muthohar & Mukhammad Rizka Fahmi Amrozi, 2022. "Millimetre Wave and Sub-6 5G Readiness of Mobile Network Big Data for Public Transport Planning," Sustainability, MDPI, vol. 15(1), pages 1-19, December.
    18. Yang He & Lisheng Jin & Huanhuan Wang & Zhen Huo & Guangqi Wang & Xinyu Sun, 2022. "Automatic ROI Setting Method Based on LSC for a Traffic Congestion Area," Sustainability, MDPI, vol. 14(23), pages 1-19, December.
    19. P. V. Thayyib & Rajesh Mamilla & Mohsin Khan & Humaira Fatima & Mohd Asim & Imran Anwar & M. K. Shamsudheen & Mohd Asif Khan, 2023. "State-of-the-Art of Artificial Intelligence and Big Data Analytics Reviews in Five Different Domains: A Bibliometric Summary," Sustainability, MDPI, vol. 15(5), pages 1-38, February.

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