Fusing Mobile Phone and Travel Survey Data to Model Urban Activity Dynamics
A key issue to understand urban system is to characterize the activity dynamics in a city—when, where, what, and how activities happen in a city. To better understand the urban activity dynamics, city-wide and multiday activity participation sequence data, namely, activity chain as well as suitable...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/5321385 |
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| author | Chao Yang Yuliang Zhang Xianyuan Zhan Satish V. Ukkusuri Yifan Chen |
| author_facet | Chao Yang Yuliang Zhang Xianyuan Zhan Satish V. Ukkusuri Yifan Chen |
| author_sort | Chao Yang |
| collection | DOAJ |
| description | A key issue to understand urban system is to characterize the activity dynamics in a city—when, where, what, and how activities happen in a city. To better understand the urban activity dynamics, city-wide and multiday activity participation sequence data, namely, activity chain as well as suitable spatiotemporal models, are needed. The commonly used household travel survey data in activity analysis suffers from limited sample size and temporal coverage. The emergence of large-scale spatiotemporal data in urban areas, such as mobile phone data, provides a new opportunity to infer urban activities and the underlying dynamics. However, the challenge is the absence of labeled activity information in mobile phone data. Consequently, how to fuse the useful information in household survey data and mobile phone data to build city-wide, multiday, and all-time activity chains becomes an important research question. Moreover, the multidimension structure of the activity data (e.g., location, start time, duration, type) makes the extraction of spatiotemporal activity patterns another difficult problem. In this study, the authors first introduce an activity chain inference model based on tensor decomposition to infer the missing activity labels in large-scale and multiday activity data, and then develop a spatiotemporal event clustering model based on DBSCAN, called STE-DBSCAN, to identify the spatiotemporal activity patterns. The proposed approaches achieved good accuracy and produced patterns with a high level of interpretability. |
| format | Article |
| id | doaj-art-c65d21b23bf3459ebccdeaffe8035738 |
| institution | OA Journals |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-c65d21b23bf3459ebccdeaffe80357382025-08-20T02:19:15ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/53213855321385Fusing Mobile Phone and Travel Survey Data to Model Urban Activity DynamicsChao Yang0Yuliang Zhang1Xianyuan Zhan2Satish V. Ukkusuri3Yifan Chen4The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai, ChinaThe Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai, ChinaJD Intelligent City Research, Beijing, ChinaLyles School of Civil Engineering, Purdue University, West Lafayette, IN, USAInstitute of Transport Studies, Department of Civil Engineering, Monash University, Melbourne, AustraliaA key issue to understand urban system is to characterize the activity dynamics in a city—when, where, what, and how activities happen in a city. To better understand the urban activity dynamics, city-wide and multiday activity participation sequence data, namely, activity chain as well as suitable spatiotemporal models, are needed. The commonly used household travel survey data in activity analysis suffers from limited sample size and temporal coverage. The emergence of large-scale spatiotemporal data in urban areas, such as mobile phone data, provides a new opportunity to infer urban activities and the underlying dynamics. However, the challenge is the absence of labeled activity information in mobile phone data. Consequently, how to fuse the useful information in household survey data and mobile phone data to build city-wide, multiday, and all-time activity chains becomes an important research question. Moreover, the multidimension structure of the activity data (e.g., location, start time, duration, type) makes the extraction of spatiotemporal activity patterns another difficult problem. In this study, the authors first introduce an activity chain inference model based on tensor decomposition to infer the missing activity labels in large-scale and multiday activity data, and then develop a spatiotemporal event clustering model based on DBSCAN, called STE-DBSCAN, to identify the spatiotemporal activity patterns. The proposed approaches achieved good accuracy and produced patterns with a high level of interpretability.http://dx.doi.org/10.1155/2020/5321385 |
| spellingShingle | Chao Yang Yuliang Zhang Xianyuan Zhan Satish V. Ukkusuri Yifan Chen Fusing Mobile Phone and Travel Survey Data to Model Urban Activity Dynamics Journal of Advanced Transportation |
| title | Fusing Mobile Phone and Travel Survey Data to Model Urban Activity Dynamics |
| title_full | Fusing Mobile Phone and Travel Survey Data to Model Urban Activity Dynamics |
| title_fullStr | Fusing Mobile Phone and Travel Survey Data to Model Urban Activity Dynamics |
| title_full_unstemmed | Fusing Mobile Phone and Travel Survey Data to Model Urban Activity Dynamics |
| title_short | Fusing Mobile Phone and Travel Survey Data to Model Urban Activity Dynamics |
| title_sort | fusing mobile phone and travel survey data to model urban activity dynamics |
| url | http://dx.doi.org/10.1155/2020/5321385 |
| work_keys_str_mv | AT chaoyang fusingmobilephoneandtravelsurveydatatomodelurbanactivitydynamics AT yuliangzhang fusingmobilephoneandtravelsurveydatatomodelurbanactivitydynamics AT xianyuanzhan fusingmobilephoneandtravelsurveydatatomodelurbanactivitydynamics AT satishvukkusuri fusingmobilephoneandtravelsurveydatatomodelurbanactivitydynamics AT yifanchen fusingmobilephoneandtravelsurveydatatomodelurbanactivitydynamics |