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Data fusion algorithm improves travel time predictions

Data fusion algorithm improves travel time predictions

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  • Author(s): S. Lim 1  and  C. Lee 2
    • View affiliations
    • Affiliations: 1: Advanced Transport Research Division, Korea Institute of Construction Technology, Korea
      2: Department of Transportation Engineering, University of Seoul, Korea
  • Source: Volume 5, Issue 4, December 2011, p. 302 – 309
    DOI:  10.1049/iet-its.2011.0014 , Print ISSN 1751-956X, Online ISSN 1751-9578
© The Institution of Engineering and Technology
Published

Travel time is considered more useful to users than other travel-related information such as speed. It is mainly estimated by point or interval detection systems. In this study, the authors investigate the deficiency of these systems in estimating travel times when they are used in isolation, and proposed a fusion algorithm that simultaneously utilises data from both point and interval detection systems. The fusion algorithm is based on the traffic flow and k-nearest neighbourhood (k-NN) models. Specifically, the authors precisely define the so-called the time lag issue in interval detection systems. To overcome this problem, they analysed the travel time variation because of variation in traffic states using fused data from point and interval detection systems. The authors show that the travel time obtained from interval detection systems is renewed by considering the travel time variation and their results show that the proposed algorithm satisfactorily predicts the travel time with the mean absolute percentage errors (MAPE).

Inspec keywords: delays; traffic information systems; travel industry; sensor fusion

Other keywords: data fusion algorithm; travel-related information; traffic flow; interval detection system; k-nearest neighbourhood model; time lag issue; travel time prediction; mean absolute percentage error

Subjects: Administration of other service industries; Traffic engineering computing; Data handling techniques

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