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 mode...

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Main Authors: CHIANG, Meng-Fen, Ee-peng LIM
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2015
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Online Access:https://ink.library.smu.edu.sg/sis_research/3079
https://ink.library.smu.edu.sg/context/sis_research/article/4079/viewcontent/136._On_Mining_Lifestyles_from_User_Trip_Data__ASONAM2015_.pdf
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spelling sg-smu-ink.sis_research-40792018-07-13T04:40:52Z On mining lifestyles from user trip data CHIANG, Meng-Fen Ee-peng LIM, 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. 2015-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3079 info:doi/10.1145/2808797.2808906 https://ink.library.smu.edu.sg/context/sis_research/article/4079/viewcontent/136._On_Mining_Lifestyles_from_User_Trip_Data__ASONAM2015_.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Computer Sciences Databases and Information Systems Transportation
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Computer Sciences
Databases and Information Systems
Transportation
spellingShingle Computer Sciences
Databases and Information Systems
Transportation
CHIANG, Meng-Fen
Ee-peng LIM,
On mining lifestyles from user trip data
description 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.
format text
author CHIANG, Meng-Fen
Ee-peng LIM,
author_facet CHIANG, Meng-Fen
Ee-peng LIM,
author_sort CHIANG, Meng-Fen
title On mining lifestyles from user trip data
title_short On mining lifestyles from user trip data
title_full On mining lifestyles from user trip data
title_fullStr On mining lifestyles from user trip data
title_full_unstemmed On mining lifestyles from user trip data
title_sort on mining lifestyles from user trip data
publisher Institutional Knowledge at Singapore Management University
publishDate 2015
url https://ink.library.smu.edu.sg/sis_research/3079
https://ink.library.smu.edu.sg/context/sis_research/article/4079/viewcontent/136._On_Mining_Lifestyles_from_User_Trip_Data__ASONAM2015_.pdf
_version_ 1770572802717384704