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

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, t...

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Bibliographic Details
Published in:Transport reviews 2015-01, Vol.35 (1), p.4-32
Main Authors: Oh, Simon, Byon, Young-Ji, Jang, Kitae, Yeo, Hwasoo
Format: Article
Language:English
Subjects:
Artificial intelligence
data-driven approach
Estimating techniques
Expert systems
Forecasting
highway travel-time prediction
Intelligent transportation systems
pattern searching
Predictions
Regression
Roads & highways
Robustness
statistical modelling
Strategy
Time series
traffic forecasting
Transportation
Travel
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Summary: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.
ISSN:0144-1647
1464-5327
DOI:10.1080/01441647.2014.992496