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Title:

Estimating Right-Turn-on-Red Capacity for Dual Right-Turn Lanes at Signalized Intersections

Accession Number:

01366523

Record Type:

Component

Availability:

Transportation Research Board Business Office

500 Fifth Street, NW
Washington, DC 20001 United States
Order URL: http://www.trb.org/Main/Blurbs/167999.aspx

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Order URL: http://worldcat.org/isbn/9780309223263

Abstract:

Dual right-turn lanes are increasingly used as a design alternative at urban intersections, primarily to accommodate high right-turn demand. For dual right-turn lanes, an accurate estimate of right-turn-on-red (RTOR) capacity can contribute to better decisions about whether RTOR should be allowed or prohibited and may lead to refined delay estimation and improved signal timing. A gap-acceptance model was formulated for predicting lane-specific RTOR capacities at dual right-turn lanes. The proposed model can represent the unequal effects of conflicting traffic streams from different cross-street lanes on RTOR capacities of dual right-turn lanes. Existing probabilistic methods were adapted to adjust RTOR capacity for shared through and right-turn lanes. Microsimulation models were developed, calibrated based on field data, and used as benchmarks to validate the proposed model. Numerical experiments indicated that the proposed model exhibits a significantly improved ability to predict RTOR capacities for dual right-turn lanes compared with the classical Harders model.

Monograph Accession #:

01450371

Report/Paper Numbers:

12-0284

Language:

English

Authors:

Chen, Xiaoming
Qi, Yi
Li, Da

Pagination:

pp 29–38

Publication Date:

2012

Serial:

Transportation Research Record: Journal of the Transportation Research Board

Issue Number: 2286
Publisher: Transportation Research Board
ISSN: 0361-1981

ISBN:

9780309223263

Media Type:

Print

Features:

Figures; References; Tables

Uncontrolled Terms:

Subject Areas:

Highways; Operations and Traffic Management; I73: Traffic Control

Files:

TRIS, TRB, ATRI

Created Date:

Feb 8 2012 4:53PM

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