Conference Proceeding Article
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.
Computer Sciences | Databases and Information Systems | Transportation
Data Management and Analytics
ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015: August 25-28, Paris
City or Country
CHIANG, Meng-Fen and Ee-peng LIM.
On Mining Lifestyles from User Trip Data. (2015). ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015: August 25-28, Paris. 145-152. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3079