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Journal of Transportation Engineering / Volume 135 / Issue 9 / TECHNICAL NOTES

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Estimating Highway Capacity Considering Two-Regime Models

J. Transp. Eng. 135, 670 (2009); http://dx.doi.org/10.1061/(ASCE)0733-947X(2009)135:9(670) (7 pages)

Jun Yao1, Hesham Rakha2, Hualiang (Harry) Teng3, Valerian Kwigizile4, and Mohamed Kaseko5

1Transportation Planner I, PB Americas, Inc., 3340 Peachtree Rd., Suite 2400, Tower Place 100, Atlanta, GA 30326. E-mail: junyao@gmail.com
2Professor, Charles E. Via Jr. Dept. of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA 24060. E-mail: hrakha@vt.edu
3Assistant Professor, Dept. of Civil and Environmental Engineering, Univ. of Nevada, Las Vegas, 4505 Maryland Pkwy, Box 454015, Las Vegas, NV 89154-4015 (corresponding author). E-mail: hualiang.teng@unlv.edu
4Assistant Professor, Dept. of Civil Engineering, Institute of Technology, West Virginia Univ., 405 Fayette Pike, Montgomery, WV 25136. E-mail: Valerian.Kwigizile@mail.wvu.edu
5Associate Professor, Dept. of Civil and Environmental Engineering, Univ. of Nevada, Las Vegas, 4505 Maryland Pkwy, Box 454015, Las Vegas, NV 89154-4015. E-mail: kaseko@ce.unlv.edu

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(Submitted 2 March 2007; accepted 7 April 2009; published online 14 August 2009)

Capacity can be derived by fitting a relationship between the three fundamental traffic variables: flow, speed, and density. The study uses a dual-regime model with the consideration of all three variables. Different forms of functions were specified for congested and uncongested conditions. An optimization based estimation procedure was developed to minimize the distance between the theoretical functions and the traffic stream variable measurements. The optimal procedure was coded into a computer program and applied to three data sets representing different roadway classifications. The results indicate that the proposed method can produce good estimates of the key traffic stream parameters including the roadway capacity.

© 2009 ASCE

Article Outline

  1. Introduction
  2. Model Formulation
    1. Point Estimation
  3. Parameter Optimization
  4. Model Estimation
  5. Conclusions and Recommendations

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ARTICLE DATA

Digital Object Identifier
http://dx.doi.org/10.1061/(ASCE)0733-947X(2009)135:9(670)

PUBLICATION DATA

ISSN

0733-947X (print)  
1943-5436 (online)

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ASCE

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