Analysis on context change and repetitive travel mode choices based on a dynamic, computational model

Research on individual decision-making process is fundamentally critical to explore the macroscopic behavioral rules for travel mode choice. In this paper, a behavioral experiment under different contexts was designed by a process-tracing method to obtain data regarding repetitive travel mode choices. Based on the Decision Field Theory, a stochastic, dynamic model was proved to be reliable and used to reproduce and analyze the repeated decision-making process. It is concluded that in a stable context, travelers would gradually establish and use some new decision rules to make a travel mode choice during the repetitive decision-making process. When travelers have developed a travel mode habit, environmental cues become the key factors that trigger travelers to make travel mode choices. Context change and traffic policies can make travelers consider, weigh and compare the relevant information again and interrupt their previous habitual choice behavior, enhancing the use of Park and Ride. Meanwhile, travelers with a faster learning speed and better memory develop a travel mode habit in a stable context and change the existing car use habit in a new context more quickly. These results would help to enrich the existing theoretical study of travel behavior and provide an interesting starting point for the development of practical strategies to promote the use of public transport instead of a private car. Traffic management techniques such as congestion pricing, along with behavior intervention and guidance strategies for different groups can strengthen this effect.