Review and evaluation of methods for estimating delay at priority junctions

ABSTRACT

The Level of Service (LOS) for a priority junction is identified by the delay calculated for each movement. Because of the complexity of the traffic operations, various methods have been studied to evaluate more precise delay. This paper reviews benchmarks of these methods developed over past 55 years from Tanner’s method, 1962, until now. The precision of these techniques is examined using real data from two priority junctions, i.e. three-legged formed, with multilane on the major and minor road. About 9-h data were gathered for each junction from morning until evening using video camera technique. This, in total, provided 18 hdata recording on normal working days. Then, observed delays incurred on minor road vehicles are compared with approximated delays from reviewed techniques. The study found that Brilon’s and Troutbeck’s methods that are the most popular methods provide consistent and closest calculated delay at priority junction. Additionally, Highway Capacity Manual (HCM) is an appropriate method only during low saturation where volume over capacity is less than 0.9.

KEYWORDS:

• Delay
• priority junction
• video camera
• observed delays

Acknowledgments

The authors declare that there is no conflict of interest regarding the publication of this article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Mohammad Ali Sahraei

Mohammad Ali Sahraei received Master (2012) and Ph.D. degree (2018) in field of traffic ans transportation engineering at Universiti Teknologi Malaysia (UTM). He is postdoctoral researcher at Erzurum Technical University (ETU). His research interests include traffic and transportation modeling, simulation, traffic safety, traffic management and ITS.

Elnaz Akbari

Elnaz Akbari received Ph.D. degree in sensors based nanomaterials from the UTM in 2014. She was recently Postdoctoral Researcher at the University of California-Merced. Her research interests include machine learning and sensors.