A Game Theoretic Macroscopic Model of Bypassing at Traffic Diverges with Applications to Mixed Autonomy Networks

TL;DR

This paper introduces a macroscopic game-theoretic model for vehicle bypassing at traffic diverges, capturing selfish lane choices via Wardrop equilibrium, and demonstrates its effectiveness through validation and potential applications in mixed autonomy networks.

Contribution

It develops a novel macroscopic model based on Wardrop equilibrium for predicting bypassing behavior at traffic diverges, including existence, uniqueness, and calibration methods.

Findings

Model accurately predicts lane change maneuvers in simulations.

Equilibrium is proven to exist and be unique under mild conditions.

Model can be used to optimize and control traffic flow with autonomous vehicles.

Abstract

Vehicle bypassing is known to negatively affect 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, we present a macroscopic model for predicting the number of vehicles that bypass at a traffic diverge. We take into account the selfishness of vehicles in selecting their lanes; every vehicle selects lanes such that its own cost is minimized. We discuss how we model the costs experienced by the vehicles. Then, taking into account the selfish behavior of the vehicles, we model the lane choice of vehicles at a traffic diverge as a Wardrop equilibrium. We state and prove the properties of Wardrop equilibrium in our model. We show that there always exists an equilibrium for our model. Moreover, unlike most nonlinear asymmetrical routing games, we prove that the equilibrium is unique under mild assumptions. We discuss how our model can be easily calibrated by running a simple optimization problem. Using our calibrated model, we validate it through simulation studies and demonstrate that our model successfully predicts the aggregate lane change maneuvers that are performed by vehicles for bypassing at a traffic diverge. We further discuss how our model can be employed to obtain the optimal lane choice behavior of the vehicles, where the social or total cost of vehicles is minimized. Finally, we demonstrate how our model can be utilized in scenarios where a central authority can dictate the lane choice and trajectory of certain vehicles 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. We show how certain decisions of the central authority can affect the total delays in such scenarios via an example.