NDLI: On mining lifestyles from user trip data

Content Provider	IEEE Xplore Digital Library
Author	Chiang, Meng-Fen Lim, Ee-Peng Low, Jia-Wei
Copyright Year	2015
Description	Author affiliation: Living Analytics Research Center, Singapore Management University, 80 Stamford Road, Singapore (Chiang, Meng-Fen; Lim, Ee-Peng; Low, Jia-Wei)
Abstract	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.
Starting Page	145
Ending Page	152
File Size	473323
Page Count	8
File Format	PDF
ISBN	9781450338547
DOI	10.1145/2808797.2808906
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Association for Computing Machinery, Inc. (ACM)
Subject Keyword	Legged locomotion Semantics Sociology Hidden Markov models Cities and towns Statistics Public transportation
Content Type	Text
Resource Type	Article

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