Exploratory Regression Models for Estimating Right-Turn-on-Red Volume on Exclusive Right-Turn Lanes at Signalized Intersections. Issue 3 (March 2023)

• Record Type: Journal Article
• Authors: Emtenan, A. M. Tahsin; Haghighat, Arya; Shields, Mark; Shaw, John; Hawley, Pat; Sharma, Anuj; Day, Christopher M.
• Is Part Of: Transportation research record. Volume 2677:Issue 3(2023)
• Page Start: 596
• Page End: 608
• Publication Date: 2023-03
• Subjects: operations -- arterial -- intersection performance -- signal timing -- signalized intersection -- traffic signal; Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.1177/03611981221116370
• Languages: English
• ISSNs: 0361-1981

Abstract

The Highway Capacity Manual (HCM) 2016 recommends using right-turn-on-red (RTOR) volume for capacity and level of service analyses but provides very limited guidance for predicting the volume in the absence of field data. The signalized intersection methodology in the HCM computes capacity using only the green portion of the cycle, ignoring the additional capacity that may result from allowing RTOR. This leads to an overestimation of delay, and consequently a determination of level of service that is likely to appear worse than the actual performance. Therefore, it is essential to develop proper RTOR volume and capacity estimation models for correct estimation of the level of service at signalized intersections. In this study, regression models for RTOR volume were developed for intersections where the subject RTOR movement occurs from an exclusive right-turn lane. Data from 128 intersections across the United States was used for the analysis. Four different categories of regression model with three types of model under each category were developed and their performances were compared using a testing data set. Tobit regression performed better than other types of regression in predicting the RTOR volume. The model will be useful in predicting RTOR volumes in the absence of field data for accurate estimation of delay and level of service at a signalized intersection.