Empirical and experimental study on the growth pattern of traffic oscillations upstream of fixed bottleneck and model test. (July 2022)

• Record Type: Journal Article
• Authors: Zheng, Shi-Teng; Jiang, Rui; Tian, Junfang; Li, Xiaopeng; Treiber, Martin; Li, Zhen-Hua; Gao, Lan-Da; Jia, Bin
• Is Part Of: Transportation research. Volume 140(2022)
• Publication Date: 2022-07
• Subjects: Bottleneck -- Car following -- Concave growth pattern -- Field experiment -- Traffic oscillation; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2022.103729
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Transfer the insights on the rather special situation/observation of moving bottleneck to the ubiquitous case of fixed bottleneck. The role of stochasticity: Intra-driver stochasticity is needed to describe the phenomena. The nature of the goodness-of-fit (microscopic or macroscopic) should reflect the nature of the observations to be calibrated. The high-quality data in itself can be used to test traffic flow models and theories. Abstract: This paper aims to address a simple but fundamental question, how the traffic oscillations grow along the road. Firstly, we conduct an empirical study on the growth pattern of traffic oscillations on US-101 Freeway and German-A5 Freeway. Then we perform an experiment to study the growth pattern of traffic oscillations on a single lane with a fixed bottleneck of speed limit. Both empirical and experimental results show that traffic oscillations grow in a concave way along the road. This finding is consistent with the previous one that traffic oscillations grow concavely along the platoon following a slower leader taking on the role of a moving bottleneck, which is not to be expected a priori for a fixed bottleneck. Finally, we use the finding to test three typical car-following models. The test indicates that whereas the intelligent driver model (IDM) fails to reproduce the observed growth pattern, the 2D-IDM and the stochastic speed adaptation model could. These findings are expected to improve our understanding of the role of stochasticity in car following, and the high-quality data in itself can be used to test traffic flow models and theories.