Coordination behavior in travel mode choice: an experiment on autonomous vehicle services

With rapid advancements in automation, autonomous vehicles (AVs) will soon coexist with human-driven vehicles (HVs) in mixed traffic. This study investigates the coordination behaviour in mode choice between AVs and HVs with positive and negative externalities, respectively. Participants play a mode choice game repeatedly under partial information (PI) or full information (FI) feedback on payoffs of the alternatives. The descriptive analysis indicates that choices under PI are close to the outcome at the Nash equilibrium; however, choices under FI are consistently below this equilibrium outcome. This discrepancy leads to a decline in social welfare, despite the provision of additional information. To test the factors influencing choice behaviour, we built a series of mixed multinomial logit (MMNL) models. The first model reveals substantial heterogeneity in individual preferences for AVs and indicates that full information feedback weakens the influence of inherent preferences while reinforcing the role of dynamic experience learning. In the second model, the memory effect diminishes further when payoff fairness is introduced. Fairness considerations significantly shape mode choices: while some participants prioritise equitable payoffs, others pursue individual payoff maximisation, producing aggregate outcomes between the distributions induced by the payoff equity and by the Nash equilibrium. By understanding the factors influencing choice behaviour and payoff preferences, policymakers and transport planners can develop targeted interventions to promote AV adoption, ultimately facilitating a smoother transition towards autonomous vehicle usage.