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Nonlinear Autoregressive Conditional Duration Models for Traffic Congestion Estimation

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  • Eleni I. Vlahogianni
  • Matthew G. Karlaftis
  • Konstantinos Kepaptsoglou

Abstract

The considerable impact of congestion on transportation networks is reflected by the vast amount of research papers dedicated to congestion identification, modeling, and alleviation. Despite this, the statistical characteristics of congestion, and particularly of its duration, have not been systematically studied, regardless of the fact that they can offer significant insights on its formation, effects and alleviation. We extend previous research by proposing the autoregressive conditional duration (ACD) approach for modeling congestion duration in urban signalized arterials. Results based on data from a signalized arterial indicate that a multiregime nonlinear ACD model best describes the observed congestion duration data while when it lasts longer than 18 minutes, traffic exhibits persistence and slow recovery rate.

Suggested Citation

  • Eleni I. Vlahogianni & Matthew G. Karlaftis & Konstantinos Kepaptsoglou, 2011. "Nonlinear Autoregressive Conditional Duration Models for Traffic Congestion Estimation," Journal of Probability and Statistics, Hindawi, vol. 2011, pages 1-13, August.
    • Handle: RePEc:hin:jnljps:798953
    DOI: 10.1155/2011/798953
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    Cited by:

    1. Natalia Kyriakopoulou & Yorgos N. Photis & Pavlos Kanaroglou, 2016. "Mathematical characterization of spatiotemporal congested traffic patterns: mixed speed data analysis in the greater Toronto and Hamilton area, Canada," Transportation Planning and Technology, Taylor & Francis Journals, vol. 39(3), pages 318-328, April.
    2. Xu Sun & Kun Lin & Pengpeng Jiao & Huapu Lu, 2020. "The Dynamical Decision Model of Intersection Congestion Based on Risk Identification," Sustainability, MDPI, vol. 12(15), pages 1-16, July.

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