Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore
In this study, with Singapore as an example, we demonstrate how we can use mobile phone call detail record (CDR) data, which contains millions of anonymous users, to extract individual mobility networks comparable to the activity-based approach. Such an approach is widely used in the transportation...
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| Format: | Article |
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Institute of Electrical and Electronics Engineers (IEEE)
2019
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| Online Access: | http://hdl.handle.net/1721.1/120769 https://orcid.org/0000-0002-3483-5132 https://orcid.org/0000-0003-0600-3803 https://orcid.org/0000-0002-8482-0318 |
| _version_ | 1826210132629913600 |
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| author | Jiang, Shan Ferreira Jr, Joseph Gonzalez, Marta C. |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Jiang, Shan Ferreira Jr, Joseph Gonzalez, Marta C. |
| author_sort | Jiang, Shan |
| collection | MIT |
| description | In this study, with Singapore as an example, we demonstrate how we can use mobile phone call detail record (CDR) data, which contains millions of anonymous users, to extract individual mobility networks comparable to the activity-based approach. Such an approach is widely used in the transportation planning practice to develop urban micro simulations of individual daily activities and travel; yet it depends highly on detailed travel survey data to capture individual activity-based behavior. We provide an innovative data mining framework that synthesizes the state-of-the-art techniques in extracting mobility patterns from raw mobile phone CDR data, and design a pipeline that can translate the massive and passive mobile phone records to meaningful spatial human mobility patterns readily interpretable for urban and transportation planning purposes. With growing ubiquitous mobile sensing, and shrinking labor and fiscal resources in the public sector globally, the method presented in this research can be used as a low-cost alternative for transportation and planning agencies to understand the human activity patterns in cities, and provide targeted plans for future sustainable development. |
| first_indexed | 2024-09-23T14:44:26Z |
| format | Article |
| id | mit-1721.1/120769 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T14:44:26Z |
| publishDate | 2019 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1207692022-09-29T10:15:23Z Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore Jiang, Shan Ferreira Jr, Joseph Gonzalez, Marta C. Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Massachusetts Institute of Technology. Department of Urban Studies and Planning Massachusetts Institute of Technology. Institute for Data, Systems, and Society Jiang, Shan Ferreira Jr, Joseph Gonzalez, Marta C. In this study, with Singapore as an example, we demonstrate how we can use mobile phone call detail record (CDR) data, which contains millions of anonymous users, to extract individual mobility networks comparable to the activity-based approach. Such an approach is widely used in the transportation planning practice to develop urban micro simulations of individual daily activities and travel; yet it depends highly on detailed travel survey data to capture individual activity-based behavior. We provide an innovative data mining framework that synthesizes the state-of-the-art techniques in extracting mobility patterns from raw mobile phone CDR data, and design a pipeline that can translate the massive and passive mobile phone records to meaningful spatial human mobility patterns readily interpretable for urban and transportation planning purposes. With growing ubiquitous mobile sensing, and shrinking labor and fiscal resources in the public sector globally, the method presented in this research can be used as a low-cost alternative for transportation and planning agencies to understand the human activity patterns in cities, and provide targeted plans for future sustainable development. Singapore. National Research Foundation (through the Singapore-MIT Alliance for Research and Technology (SMART) Center for Future Urban Mobility (FM)) Center for Complex Engineering Systems at MIT and KACST 2019-03-07T12:28:07Z 2019-03-07T12:28:07Z 2017-06 2016-07 2019-01-18T15:58:40Z Article http://purl.org/eprint/type/JournalArticle 2332-7790 http://hdl.handle.net/1721.1/120769 Jiang, Shan, Joseph Ferreira, and Marta C. Gonzalez. “Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore.” IEEE Transactions on Big Data 3, no. 2 (June 1, 2017): 208–219. https://orcid.org/0000-0002-3483-5132 https://orcid.org/0000-0003-0600-3803 https://orcid.org/0000-0002-8482-0318 http://dx.doi.org/10.1109/TBDATA.2016.2631141 IEEE Transactions on Big Data Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) Other repository |
| spellingShingle | Jiang, Shan Ferreira Jr, Joseph Gonzalez, Marta C. Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore |
| title | Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore |
| title_full | Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore |
| title_fullStr | Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore |
| title_full_unstemmed | Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore |
| title_short | Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore |
| title_sort | activity based human mobility patterns inferred from mobile phone data a case study of singapore |
| url | http://hdl.handle.net/1721.1/120769 https://orcid.org/0000-0002-3483-5132 https://orcid.org/0000-0003-0600-3803 https://orcid.org/0000-0002-8482-0318 |
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