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First published online January 1, 2017

Estimation of Saturation Headway in Work Zones on Urban Streets

Abstract

Work zones and lane closures on urban arterials can cause significant disruptions to the traveling public, and methods are increasingly needed to estimate the reductions to saturation flow rates that result from work zones at signalized intersections. A set of statistical models that estimate saturation headways as a function of the presence and configuration of the work zone on signalized arterial streets is presented. More than 10,000 individual vehicular headway observations were collected from video observations in and after work zones at six study sites in North Carolina. Conventional multiple-regression and path-based-regression models (structural equation model) were used to develop the saturation headway models. Three models are provided at different aggregation levels of the collected data with identical work zone configurations. The models developed at cycle-length, 15-min, and full aggregation produced adjusted R-squared values of .3259, .7209, and .895, respectively. The proposed model incorporates the effects of lane configuration, pavement condition, turning percentage from shared lanes, work intensity, and number of closed exclusive turning lanes. Based on path analysis, the structural equation model satisfies all the rule-of-thumb criteria for goodness-of-fit indices. The model uses Highway Capacity Manual default values for turning-vehicle headway effect as its intercept coefficient value.

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Article first published online: January 1, 2017
Issue published: January 2017

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© 2017 National Academy of Sciences.
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Authors

Affiliations

Ali Hajbabaie
Civil and Environmental Engineering Department, Washington State University, Pullman, WA 99163
SangKey Kim
Local Investment Management Center, Korea Research Institute for Local Administration, 12-6 Banpo-Daero 30-Gil, Seocho-Gu, Seoul 06647, South Korea
Bastian J. Schroeder
Kittelson & Associates, Inc., Suite 810, 300 M Street, SE, Washington, DC 20003
Seyedbehzad Aghdashi
Institute for Transportation Research and Education, Research 4, 909 Capability Drive, Raleigh, NC 27606
Nagui M. Rouphail
Institute for Transportation Research and Education, Research 4, 909 Capability Drive, Raleigh, NC 27606
Kambiz Tabrizi
Institute for Transportation Research and Education, Research 4, 909 Capability Drive, Raleigh, NC 27606

Notes

A. Hajbabaie, [email protected].

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