Empirical and experimental study on the growth pattern of traffic oscillations upstream of fixed bottleneck and model test

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Shi-Teng Zheng - , Beijing Jiaotong University (Author)
  • Rui Jiang - , Beijing Jiaotong University (Author)
  • Junfang Tian - , Tianjin University (Author)
  • Xiaopeng Liu - , University of South Florida (Author)
  • Martin Treiber - , Chair of Econometrics and Statistics, esp. in the Transport Sector (Author)
  • Zhen-Hua Li - , Ministry of Transport of the People's Republic of China (Author)
  • Lan-Da Gao - , Ministry of Transport of the People's Republic of China (Author)
  • Bin Jia - , Beijing Jiaotong University (Author)

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.

Details

Original languageEnglish
Article number103729
JournalTransportation Research Part C: Emerging Technologies
Volume140
Publication statusPublished - Jul 2022
Peer-reviewedYes

External IDs

Scopus 85130700114

Keywords

Library keywords