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.
First order multi-lane traffic flow model – an incentive based macroscopic model to represent lane change dynamics
Transportmetrica B: Transport Dynamics ; 7 , 1 ; 1758-1779
2019-12-23
22 pages
Article (Journal)
Electronic Resource
English
A Macroscopic Multi-Lane Traffic Flow Model for ACC/CACC Traffic Dynamics
Transportation Research Record | 2018
|British Library Conference Proceedings | 2020
|A macroscopic first-order lane-group-based signalized node model
TIBKAT | 2022
|