In order to quantitatively analyze the influence of highway dynamic congestion traffic information caused by traffic accidents and traveler heterogeneity on travel choice behavior, a large-scale SP/RP survey data that covering 2500 highway users in west Japan is used for this study. Latent Class Analysis (LCA) is adopted for extracting different preference features on traffic information, followed by a multilevel model which sets the results of LCA as explanatory variable. The research results show that: according to investigation information,travelers can be divided into three groups：high-dependence group, low-dependence group and non-dependence group accounting for 38.8%, 36.1% and 25.1% respectively; the model considering data hierarchical structure is more accurate than the general discrete choice model; and travelers with different heterogeneity of traffic information have obvious differences in travel behavior under dynamic traffic information.