A Statistical Approach for Estimating Speed Threshold for Traffic Breakdown Event Identification: a Model Accounting for Data Variations

This study aims at developing a robust Bayesian statistical approach to determine the speed threshold (ST) for detecting a traffic breakdown event using traffic flow parameters. Data collected from a freeway in Jacksonville, Florida was used as a case study segment. The approach particularly is based on the change-point regression, in which two models – the Student-t and Gaussian residual distributed regressions – were developed and compared. The study found promising results in detecting the ST value when verified using the hypothesis test and simulated data. Moreover, it was found that the Student-t regression outperformed the Gaussian residual distributed regression in fitting the speed-occupancy relationship. The methodology described in the current study can be used in the procedures of analyzing the breakdown process, stochastic roadway capacity analysis, congestion duration analysis, assessing recurring traffic conditions, and clustering different traffic conditions. The results from these analyses provide useful information required in developing advanced traffic management strategies for highway operations.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee ABJ30 Standing Committee on Urban Transportation Data and Information Systems.
  • Corporate Authors:

    Transportation Research Board

  • Authors:
    • Kidando, Emmanuel
    • Moses, Ren
    • Sando, Thobias
  • Conference:
  • Date: 2019


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References; Tables;
  • Pagination: 19p

Subject/Index Terms

Filing Info

  • Accession Number: 01697522
  • Record Type: Publication
  • Report/Paper Numbers: 19-03216
  • Files: TRIS, TRB, ATRI
  • Created Date: Mar 1 2019 3:51PM