Abstract: To reveal the key influencing factors and rules of intercity travel decisions under abnormal circumstances, 16 kinds of travel scenarios were designed for stated preference (SP) survey according to the level of blocking events, the amount of time of bypassing the route, and the type of travel (elasticity trip and rigidity trip). The questionnaire included 11 factors affecting residents' travel decisions and 4 decision options. Based on the multinomial logistic regression, travel decision models were built by using the SP Data of 16 abnormal scenes in intercity travel. Results show that the significant factors affecting residents' decision-making are different from the extent of the interruption of the events, the distance of the bypass, and different purposes of the trip. Moreover, there is a difference in the degree of influence for the same factor in different scenes. Among them, the travel time consumption and residents' preference for a certain travel mode are the most significant factors followed by the trip frequency and occupation under different travel scenarios. Nevertheless, driving age, age and gender have significant effects under individual scenarios. These results can be used to predict the travel behavior of the residents under the road blocking event in the integrated transportation network. It can also provide support for personalized and refined travel information service to different travel individuals in abnormal circumstances.