Using Non-cooperative Game Theory for Taxi-Sharing Recommendation Systems

This paper presents a recommendation mechanism for taxi-sharing. The first aim of our model is to respectively recommend taxis and passengers for picking up passengers quickly and finding taxis easily. The second purpose is providing taxi-sharing service for passengers who want to save the payment. In our method, we analyze the historical global positioning system trajectories generated by 10,357 taxis during 110 days and present the service region with time-dependent R-Tree. We formulate the problem of choosing the paths among the taxis in the same region by using non-cooperative game theory, and find out the solution of this game which is known as Nash equilibrium. The simulation of SUMO, MOVE, and TraCI are adopted to fit our model to verify the proposed recommendation mechanism. The results show that our method can find taxis and passengers efficiently. In addition, applying our method can reduce the payment of passengers and increase the taxi revenue by taxi-sharing.