A game theoretic macroscopic model of lane choices at traffic diverges with applications to mixed–autonomy networks

Vehicle bypassing is known to increase delays at traffic diverges. However, due to the complexities of this phenomenon, accurate and yet simple models of such lane change maneuvers are hard to develop. In this work, the authors present a macroscopic model for predicting the number of vehicles that perform a bypass at a traffic diverge when taking an exit link. The authors interpret the bypassing maneuver of vehicles at a traffic diverge as drivers acting selfishly; every vehicle selects lanes such that its own cost of travel is minimized. The authors discuss how they model the costs that are incurred by the vehicles. Then, taking into account the selfish behavior of vehicles, the authors model the lane choice of vehicles at a traffic diverge as a Wardrop equilibrium. The authors state and prove the properties of the equilibrium in their model. They show that there always exists an equilibrium for their model. Moreover, although their model is an instance of nonlinear asymmetrical routing games which in general have multiple equilibria, the authors prove that the equilibrium of their model is unique under certain assumptions that they observed to hold in all their case studies. The authors discuss how their model can be calibrated by running a simple optimization problem. Then, using their calibrated model, The authors validate it through simulation studies and demonstrate that their model successfully predicts the aggregate lane change maneuvers that are performed by vehicles at a traffic diverge. Having shown the predictive power of their model, the authors discuss how their model can be employed to obtain the optimal lane choice behavior of vehicles, where the social or the overall cost of all vehicles is minimized. Finally, the authors demonstrate how their model can be utilized in scenarios where a central authority can dictate the lane choice and trajectory of certain vehicles, for example autonomous vehicles directed by a central authority, so as to increase the overall vehicle mobility at a traffic diverge. Examples of such scenarios include the case when both human driven and autonomous vehicles coexist in the network.

• Record URL:
  • https://doi.org/10.1016/j.trb.2020.11.004
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0191261520304276
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01912615
• Supplemental Notes:
  • © 2020 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Mehr, Negar
  • Li, Ruolin
  • Horowitz, Roberto
• Publication Date: 2021-2

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: pp 45-59
• Serial:
  • Transportation Research Part B: Methodological
  • Volume: 144
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0191-2615
  • Serial URL: http://www.sciencedirect.com/science/journal/01912615

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Diverging traffic; Game theory; Human beings; Lane changing; Macroscopic traffic flow; Motor vehicles; Simulation; Vehicle mix
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01764771
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 11 2021 3:36PM