Description of the Cover Picture

The cover picture is taken from the relevant content of the article"Trip Purposes of Automobile Users Inference Using Multi-day Traffic Monitoring Data". In this paper,the travel characteristics of automobile users in urban areas are investigated,and their most likely trip purpose is explored. In order to avoid the multi-day behavior variability and unobservable heterogeneity of individual characteristics ignored in traditional traffic questionnaire,traffic monitoring data collected in Northern district of Qingdao City are employed to estimate the trip purpose of automobile users by K-means clustering method. Then,Adaptive Boosting and Random Forest methods are used to classify and predict trip purposes. According to the result of this study,the purpose of automobile users can be mainly divided into four clusters,which include Commuting trips,Flexible life demand travel in daytime,Evening entertainment and leisure shopping,and Taxi-based trips for the first three types of purposes,respectively. Meanwhile,the Random Forest method performs significantly better than AdaBoost in trip purpose prediction for higher accuracy.