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

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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.
Publisher
TAYLOR & FRANCIS LTD
Issue Date
2015-01
Language
English
Article Type
Article
Keywords

TRANSPORTATION MODE DETECTION; TRAFFIC-STATE ESTIMATION; NEURAL-NETWORK MODELS; PROBE-VEHICLE DATA; REAL-TIME; NONPARAMETRIC REGRESSION; PERFORMANCE EVALUATION; MISSING DATA; FREEWAY; FLOW

Citation

TRANSPORT REVIEWS, v.35, no.1, pp.4 - 32

ISSN
0144-1647
DOI
10.1080/01441647.2014.992496
URI
http://hdl.handle.net/10203/195262
Appears in Collection
GT-Journal Papers(저널논문)CE-Journal Papers(저널논문)
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