Impacts of preceding information on travelers' departure time behavior

Travelers can use the information to help them make decision reasonably. In this paper, bottleneck model is used to study the effect of information on travelers' departure time choice. We use Bayesian learning mechanism to simulate travelers' daily decision-making behavior. Four typical cases of travelers are considered: all the information, only information by oneself, preceding information and mixed way (all the information and preceding information). The numerical results indicate that preceding information has positive impacts on traffic system at initial stage, because it can avoid excessive fluctuations; mixed way can achieve almost as much travel time as all the information.