Mode choice between autonomous vehicles and manually-driven vehicles: An experimental study of information and reward

Abstract

An increase in autonomous vehicles (AVs) would result in a decline in traffic congestion; however, the travel cost associated with AVs is always higher than that of manually-driven vehicles (MV). This situation is interpreted as a so-called multi-player social dilemma. This study designed an economic experiment to investigate the effect of AVs on mode choice in mixed traffic flows. Participants were informed about the cost function in both modes and were asked to choose the travel mode for more than 60 rounds. In full information (FI) treatment, participants received information about the travel costs of the AV and MV modes at the end of each round. In the partial information (PI) treatment, participants received information only about the travel cost of the mode they chose. We found that participants were sensitive to cost differences in the FI treatments. Based on inequality aversion models, we proposed a perceived cost that could better explain the experimental equilibrium. A monetary reward was provided to encourage participants to take AVs and solve social dilemmas. The results demonstrated that the reward mechanism reduces traffic congestion and increases social benefits, especially in the FI treatment. Finally, a learning model that considers inertia and perceived cost is proposed to explain the decision-making process of the participants during the experiment. The findings have implications for traffic forecasting in the mixed flow of MVs and AVs and provide insights and policy suggestions for AV management.