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Article
Predicting Freeway Work Zone Capacity Distribution Based on Logistic Speed-Density Models
Journal of Advanced Transportation
  • Chaoru Lu, Norwegian University of Science and Technology
  • Jing Dong, Iowa State University
  • Anuj Sharma, Iowa State University
  • Tingting Huang, Iowa State University
  • Skylar Knickerbocker, Iowa State University
Document Type
Article
Publication Version
Published Version
Publication Date
11-1-2018
DOI
10.1155/2018/9614501
Abstract

Speed-volume-density relationship and capacity are key elements in modelling traffic operations, designing roadways, and evaluating facility performance. This paper uses a modified five-parameter logistic model to describe the speed-density relationship. The calibrated speed-density models show that the stop-and-go speed (V-b ) and shape parameters (theta(1) and theta(2) ) are similar for work zones and the nonwork zone site. Accordingly, an operational capacity prediction method is proposed. To demonstrate the effectiveness of the proposed method, the predicted operational capacities are compared with the field data, Highway Capacity Manual method, the output of WorkZoneQ software, and the ensemble tree approach under different work zone scenarios. Furthermore, a lifetime distribution prediction framework for stochastic capacity of work zones is proposed. The predicted lifetime distribution can well capture the tendency of the observed work zone capacities.

Comments

This article is published as Lu, Chaoru, Jing Dong, Anuj Sharma, Tingting Huang, and Skylar Knickerbocker. "Predicting Freeway Work Zone Capacity Distribution Based on Logistic Speed-Density Models." Journal of Advanced Transportation 2018 (2018): 9614501. DOI: 10.1155/2018/9614501. Posted with permission.

Creative Commons License
Creative Commons Attribution 4.0 International
Copyright Owner
Chaoru Lu et al.
Language
en
File Format
application/pdf
Citation Information
Chaoru Lu, Jing Dong, Anuj Sharma, Tingting Huang, et al.. "Predicting Freeway Work Zone Capacity Distribution Based on Logistic Speed-Density Models" Journal of Advanced Transportation Vol. 2018 (2018) p. 9614501
Available at: http://works.bepress.com/anuj_sharma1/85/