Optimizing Highway Efficiency in Real-Time Freeway Design Capacity

The estimation of capacity as a parameter to assess traffic flow performance on freeway facilities has received considerable attention in the literature. Despite the general acceptance of the stochastic notion of capacity, limited research has been conducted on how to select a single representative design value from a capacity distribution function. This study reports the results of an empirical comparison between conventional capacity estimates and those obtained by maximizing the Sustained Flow Index (SFI) for 19 U.S. freeway sections. The SFI is defined as the product of the traffic volume and the probability of survival at this volume. The capacity of each cross section was estimated by analyzing the speed-flow relationship and applying methods for stochastic capacity analysis. The results show that the optimum volumes obtained by maximizing the SFI estimated in 5-minute intervals correspond well to the 15 percent probability of breakdown proposed in the Highway Capacity Manual (HCM) 6th edition to estimate the capacity from field data. However, for 15-minute intervals, the optimum volumes obtained in 15-minute intervals correspond to 4 percent probability of breakdown. From these results, it was concluded that maximizing the SFI can be considered a preferred approach to estimate a single, representative value of freeway capacity.

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  • Supplemental Notes:
    • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
  • Corporate Authors:

    Louisiana State University, Baton Rouge

    Department of Civil and Environmental Engineering
    Baton Rouge, LA  United States  70803

    National Transportation Center at Maryland

    1124 Glenn Martin Hall
    University of Maryland
    College Park, MD  United States  20742

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Authors:
    • Wolshon, Brian
    • Shojaat, Siavash
    • Geistefeldt, Justin
    • Parr, Scott A
    • Escobar, Luis
  • Publication Date: 2017-12


  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Figures; References; Tables;
  • Pagination: 26p

Subject/Index Terms

Filing Info

  • Accession Number: 01666279
  • Record Type: Publication
  • Report/Paper Numbers: NTC2016-SU-R-12
  • Created Date: Mar 13 2018 10:34AM