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Short-term Travel-time Prediction on Highway: A Review of the Data-driven Approach

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  • Simon Oh
  • Young-Ji Byon
  • Kitae Jang
  • Hwasoo Yeo

Abstract

Near future travel-time information is one of the most critical factors that travellers consider before making trip decisions. In efforts to provide more reliable future travel-time estimations, transportation engineers have examined various techniques developed in the last three decades. However, there have not been sufficiently systematic and through reviews so far. In order to effectively support various transportation strategies and applications including Intelligent Transportation Systems (ITS), it is necessary to apply appropriate forecasting methods for matching circumstances in a timely manner. This paper conducts a comprehensive review study focusing on literatures, including modern techniques proposed recently, related to travel time and traffic condition predictions that are based on 'data-driven' approaches. Based on the underlying mechanisms and theoretical principles, different approaches are categorized as parametric (linear regression and time series) and non-parametric approaches (artificial intelligence and pattern searching). Then, the approaches are analysed for their strengths, potential weaknesses, and performances from five main perspectives that are prediction range, accuracy, efficiency, applicability, and robustness.

Suggested Citation

  • Simon Oh & Young-Ji Byon & Kitae Jang & Hwasoo Yeo, 2015. "Short-term Travel-time Prediction on Highway: A Review of the Data-driven Approach," Transport Reviews, Taylor & Francis Journals, vol. 35(1), pages 4-32, January.
    • Handle: RePEc:taf:transr:v:35:y:2015:i:1:p:4-32
    DOI: 10.1080/01441647.2014.992496
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    Cited by:

    1. Purva Grover & Arpan Kumar Kar & Yogesh K. Dwivedi, 2022. "Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions," Annals of Operations Research, Springer, vol. 308(1), pages 177-213, January.
    2. Milne, Dave & Watling, David, 2019. "Big data and understanding change in the context of planning transport systems," Journal of Transport Geography, Elsevier, vol. 76(C), pages 235-244.
    3. Dujuan Wang & Jiacheng Zhu & Yunqiang Yin & Joshua Ignatius & Xiaowen Wei & Ajay Kumar, 2024. "Dynamic travel time prediction with spatiotemporal features: using a GNN-based deep learning method," Annals of Operations Research, Springer, vol. 340(1), pages 571-591, September.
    4. Panda, Manoj & Ngoduy, Dong & Vu, Hai L., 2019. "Multiple model stochastic filtering for traffic density estimation on urban arterials," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 280-306.
    5. Bo Qiu & Wei (David) Fan, 2021. "Machine Learning Based Short-Term Travel Time Prediction: Numerical Results and Comparative Analyses," Sustainability, MDPI, vol. 13(13), pages 1-19, July.

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