On mining lifestyles from user trip data

Large cities today are facing major challenges in planning and policy formulation to keep their growth sustainable. In this paper, we aim to gain useful insights about people living in a city by developing novel models to mine user lifestyles represented by the users' activity centers. Two models, namely ACMM and ACHMM, have been developed to learn the activity centers of each user using a large dataset of bus and subway train trips performed by passengers in Singapore. We show that ACHMM and ACMM yield similar accuracies in location prediction task. We also propose methods to automatically predict "home", "work" and "others" labels of locations visited by each user. Through validating with human-labeled home and work locations, we show that the accuracy of location label assignment is surprisingly very good even using an unsupervised method. With the location labels assigned, we further derive interesting insights of urban lifestyles at both individual and population levels.