A geometric Brownian motion car-following model: towards a better understanding of capacity drop

Kai Yuan, Jorge Lavala, Victor Knoop, Rui Jiang, Serge Hoogendoorn

Research output: Contribution to journalArticleScientificpeer-review

31 Citations (Scopus)
145 Downloads (Pure)


Traffic flow downstream of the congestion is generally lower than the pre-queue capacity. This phenomenon is called the capacity drop. Recent empirical observations show a positive relationship between the speed in congestion and the queue discharge rate. Literature indicates that variations in driver behaviors can account for the capacity drop. However, to the best of authors' knowledge, there is no solid understanding of what and how this variation in driver behaviors lead to the capacity drop, especially without lane changing. Hence, this paper fills this gap. We incorporate the empirically observed desired acceleration stochasticity into a car-following model. The extended parsimonious car-following model shows different capacity drop magnitudes in different traffic situations, consistent with empirical observations. All results indicate that the stochasticity of desired accelerations is a significant reason for the capacity drop. The new insights can be used to develop and test new measures in traffic control.
Original languageEnglish
Number of pages15
JournalTransportmetrica B: Transport Dynamics
Volume7 (2019)
Issue number1
Publication statusPublished - 2018

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Traffic flow
  • capacity drop
  • car-following model
  • intra-driver variation


Dive into the research topics of 'A geometric Brownian motion car-following model: towards a better understanding of capacity drop'. Together they form a unique fingerprint.

Cite this