A geometric Brownian motion car-following model

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

Yuan, K. (TU Delft Transport and Planning; Georgia Institute of Technology)
Lavala, Jorge (Georgia Institute of Technology)
Knoop, V.L. (TU Delft Transport and Planning)
Jiang, Rui (Beijing Jiaotong University)
Hoogendoorn, S.P. (TU Delft Transport and Planning)

Transport and Planning

2018

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.

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

http://resolver.tudelft.nl/uuid:1f5d65dc-0729-48f4-b1a3-c9ed3e25e494

https://doi.org/10.1080/21680566.2018.1518169

2019-03-21

2168-0566

Transportmetrica B: Transport Dynamics, 7 (2019) (1)

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.

Institutional Repository

journal article

© 2018 K. Yuan, Jorge Lavala, V.L. Knoop, Rui Jiang, S.P. Hoogendoorn