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Mathematical characterization of spatiotemporal congested traffic patterns: mixed speed data analysis in the greater Toronto and Hamilton area, Canada

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  • Natalia Kyriakopoulou
  • Yorgos N. Photis
  • Pavlos Kanaroglou

Abstract

This paper formulates a comprehensive methodology for analyzing, quantifying and identifying congestion characteristics based on speed distribution. Utilizing vehicle speed data, a mathematical approach is applied, in order to characterize roadway segments, in terms of travel reliability, congestion severity and duration. We argue that the Gaussian mixture model (GMM) and its parameter combination is the appropriate tool if we are to obtain quantitative congestion measures and rank roadway performance. A significant contribution of our approach is that it is based on assumptions regarding mixed components as well as speed distribution and can be applied to large databases. We test our framework on the greater Toronto and Hamilton area in Ontario, Canada, and conclude that congestion quantification through the application of the GMM can be successfully accomplished. Results indicate that speed patterns differ significantly between counties as well as days of the week.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:transp:v:39:y:2016:i:3:p:318-328
    DOI: 10.1080/03081060.2016.1142226
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    References listed on IDEAS

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    1. Saaty, Thomas L., 1990. "How to make a decision: The analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 48(1), pages 9-26, September.
    2. Schilling M.F. & Watkins A.E. & Watkins W., 2002. "Is Human Height Bimodal?," The American Statistician, American Statistical Association, vol. 56, pages 223-229, August.
    3. 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.
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    Cited by:

    1. Seter, Hanne & Arnesen, Petter & Hjelkrem, Odd André, 2019. "The data driven transport research train is leaving the station. Consultants all aboard?," Transport Policy, Elsevier, vol. 80(C), pages 59-69.

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