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Calibrating Steady-State Traffic Stream and Car-Following Models Using Loop Detector Data

Author

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  • Hesham Rakha

    (Charles E. Via Jr. Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia 24061)

  • Mazen Arafeh

    (Department of Industrial Engineering, Faculty of Engineering & Technology, The University of Jordan, Amman 11942, Jordan)

Abstract

The research reported in this paper develops a heuristic automated tool (SPD_CAL) for calibrating steady-state traffic stream and car-following models using loop detector data. The performance of the automated procedure is then compared to off-the-shelf optimization software parameter estimates, including the MINOS and Branch and Reduce Optimization Navigator (BARON) solvers. The model structure and optimization procedure is shown to fit data from different roadway types and traffic regimes (uncongested and congested conditions) with a high quality of fit (within 1% of the optimum objective function). Furthermore, the selected functional form is consistent with multiregime models, without the need to deal with the complexities associated with the selection of regime breakpoints. The heuristic SPD_CAL solver, which is available for free, is demonstrated to perform better than the MINOS and BARON solvers in terms of execution time (at least 10 times faster), computational efficiency (better match to field data), and algorithm robustness (always produces a valid and reasonable solution).

Suggested Citation

  • Hesham Rakha & Mazen Arafeh, 2010. "Calibrating Steady-State Traffic Stream and Car-Following Models Using Loop Detector Data," Transportation Science, INFORMS, vol. 44(2), pages 151-168, May.
    • Handle: RePEc:inm:ortrsc:v:44:y:2010:i:2:p:151-168
    DOI: 10.1287/trsc.1090.0297
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    References listed on IDEAS

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    Cited by:

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    5. Mustafa Attallah & Jalil Kianfar & Yadong Wang, 2022. "Impact of High Resolution Radar-Obtained Weather Data on Spatio-Temporal Prediction of Freeway Speed," Sustainability, MDPI, vol. 14(22), pages 1-17, November.

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