Study on the Multi-Class User’s Mode Choice Behavior Based on Travel Time Budget

In real life, the travelers’ route travel time and the parking space seeking time are always uncertain. In this paper, it is assumed that the route travel time and parking space seeking time are random variables. Based on the travel time budget (TTB), a nested logit (NL) model for the multi-mode and multi-class user is proposed. A nested method of the successive average algorithm was designed to solve the model. Numerical results indicate that the expected probability of arriving on time produces a great influence on travelers’ mode choice behavior. Travelers with a higher expected probability of arriving on time are more likely to choose the subway. Among the other travelers, those with a lower expected probability of arriving on time will choose to drive in the whole journey. Accordingly, those with a higher expected probability of arriving on time are more likely to choose park-and-ride.