Travel time estimation based on piecewise truncated quadratic speed trajectory

Lu Sun*, Jun Yang, Hani Mahmassani

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

53 Scopus citations

Abstract

A piecewise truncated quadratic speed trajectory is proposed to mimic the unknown speed trajectory between point detectors. The basis functions of the new method consist of quadratic and constant functions of time. The constant functions, corresponding to upper and lower speed bounds, are determined using the maximum likelihood estimates of highest and lowest speeds that have been historically observed within a time interval. The purpose of setting a lower (upper) speed bound for simulating vehicle speed trajectory is to mimic a low (high) average speed during transition flow and congestion, and to restrict a quadratic speed trajectory to be within a realistic speed range, respectively. It was found that travel time estimation using different approaches is similar during free-flow conditions but significantly different during transition flow and congestion conditions. Using the actual travel time obtained from field experiment, the new method yields more accurate travel time estimation than other trajectory-based methods. Compared to travel time estimation using speed and density information, the new method only needs speed measurements, and therefore, it is more robust and easier to implement in practice than density-based methods. Computational implementation of the new trajectory method is tractable and can be done very efficiently, making it suitable for on-line real time travel time estimation.

Original languageEnglish (US)
Pages (from-to)173-186
Number of pages14
JournalTransportation Research Part A: Policy and Practice
Volume42
Issue number1
DOIs
StatePublished - Jan 2008

Keywords

  • Algebraic equation
  • Nonlinear optimization
  • Piecewise truncated quadratic interpolation
  • Point detector
  • Speed trajectory
  • Travel time estimation

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Transportation
  • Management Science and Operations Research

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