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

Simon Oh, Young Ji Byon, Kitae Jang, Hwasoo Yeo

Research output: Contribution to journalArticlepeer-review

107 Scopus citations

Abstract

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.

Original languageBritish English
Pages (from-to)4-32
Number of pages29
JournalTransport Reviews
Volume35
Issue number1
DOIs
StatePublished - 2 Jan 2015

Keywords

  • artificial Intelligence
  • data-driven approach
  • highway travel-time prediction
  • pattern searching
  • statistical modelling
  • traffic forecasting

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