Analyzing Tourist Departure Time Decision-Making Behaviors Using Cusp Catastrophe Model

抄録

Predicting traffic flow through AI analysis has become popular in the development of traffic control; however, if the potential behaviors of road users, such as adjusting their departure times after being provided with forecast data, are overlooked, the effect of big data on traffic flow predictions is weakened. The third-party APP can provide an interface for road users that adjust departure times based on traffic conditions and provide feedback regarding adjustments. This study constructed a cusp catastrophe model of choice behavior with attitude, switching barriers, and behavioral intention as variables. Scenario simulation revealed that if the travel time forecasting of the app were 95% accurate and a highway toll waiver was offered, 44.1% of study participants would accept the new departure time recommended by the app.