Equilibrium modeling of mixed autonomy traffic flow based on game theory

Jia Li, Di Chen and Michael Zhang

Transportation Research Part B: Methodological, 2022, vol. 166, issue C, 110-127

Abstract: While much attention was paid to the interactions of human-driven and automated vehicles at the microscopic level in recent years, the understanding of the macroscopic properties of mixed autonomy traffic flow still remains limited. In this paper, we present an equilibrium model of traffic flow with mixed autonomy based on the theory of two-player games. We consider self-interested traffic agents (i.e. human-driven and automated vehicles) endowed with different speed functions and interacting with each other simultaneously in both longitudinal and lateral dimensions. We propose a two-player game model to encapsulate their interactions and characterize the equilibria the agents may reach. We show that the model admits two types of Nash equilibria, one of which is always Pareto efficient. Based on this equilibrium structure, we propose a speed policy that guarantees the realized equilibria are Pareto efficient in all traffic regimes. We present two examples to illustrate the applications of this model. In one example, we construct flux functions for mixed autonomy traffic based on behavior characteristics of agents. In the other example, we consider a lane policy and show that mixed autonomy traffic may exhibit counterintuitive behaviors even though all the agents are rational.

Keywords: Mixed autonomy traffic; Nash equilibrium; Automated vehicle behavior design (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.trb.2022.10.001

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