A game theory approach for smart traffic management

Abstract

The rapid increase in population and transportation resources presents numerous challenges, including traffic congestion and accidents. This paper proposes a smart traffic management (STM) framework that combines the Internet of Vehicles (IoV) and game theory to manage traffic loads at road intersections. The intersection is considered a non-cooperative game, where traffic flow for each route is determined by the Nash Equilibrium (NE) to ensure that no individual can improve their performance by changing their strategy. In severe congestion, many players/vehicles significantly affect the strategy selection process. To address this, we adopt an agent-representative approach, using spectral clustering to group players with the same strategies and payoff. The proposed STM is compared with other schemes, i.e., the conventional approach without NE and SmartRoute. Our STM significantly outperformed existing protocols, reducing intersection traffic intensity by 30% and average waiting time by 40%, demonstrating its effectiveness in managing traffic loads at uncontrolled intersections.