First order multi-lane traffic flow model – an incentive based macroscopic model to represent lane change dynamics

First order multi-lane traffic flow model – an incentive based macroscopic model to represent lane change dynamics

Nagalur Subraveti, H.H.S. (TU Delft Transport and Planning)
Knoop, V.L. (TU Delft Transport and Planning)
van Arem, B. (TU Delft Transport and Planning)

2019

Unbalanced lane usage on motorways might lead to the reduction in capacity
of the motorway. Lane-level traffic management present new opportunities
to balance the lane-flow distribution and help reduce congestion.
In order to come up with efficient traffic management strategies on a
lane-level, there is a need for accurate lane-specific traffic state estimation
models. This paper presents a first-order lane-level traffic flow model. The
proposed model differs from the existing models in the following areas: (i)
incentive-based motivation for lane changes and consideration of downstream
conditions (ii) transfer of lateral flows among cells. The model is
tested against real-world data. It is observed that the model is able to capture
the lane-level dynamics in terms of the lane flow distribution. The
model results are compared to a linear regression model and results show
that the developed model performs better than the regression model on
the test sections.

cell transmission model
incentives
lane flow distribution
Lane-level
macroscopic traffic flow model

http://resolver.tudelft.nl/uuid:648e782a-1e5b-4d27-86cc-065590d2a603

https://doi.org/10.1080/21680566.2019.1700846

2020-06-12

2168-0566

Transportmetrica B: Transport Dynamics, 7 (1), 1758-1779

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

© 2019 H.H.S. Nagalur Subraveti, V.L. Knoop, B. van Arem