Inferring individual daily activities from mobile phone traces: A Boston example
Understanding individual daily activity patterns is essential for travel demand management and urban planning. This research introduces a new method to infer individuals’ activities from their mobile phone traces. Using Metro Boston as an example, we develop an activity detection model with travel d...
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| Format: | Article |
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SAGE Publications
2019
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| Online Access: | http://hdl.handle.net/1721.1/120100 https://orcid.org/0000-0003-2026-5631 https://orcid.org/0000-0003-0600-3803 |
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| author | Ratti, Carlo Zhu, Yi Ferreira Jr, Joseph Diao, Mi, Ph. D. Massachusetts Institute of Technology |
| author2 | Massachusetts Institute of Technology. Department of Urban Studies and Planning |
| author_facet | Massachusetts Institute of Technology. Department of Urban Studies and Planning Ratti, Carlo Zhu, Yi Ferreira Jr, Joseph Diao, Mi, Ph. D. Massachusetts Institute of Technology |
| author_sort | Ratti, Carlo |
| collection | MIT |
| description | Understanding individual daily activity patterns is essential for travel demand management and urban planning. This research introduces a new method to infer individuals’ activities from their mobile phone traces. Using Metro Boston as an example, we develop an activity detection model with travel diary surveys to reveal the common laws governing individuals’ activity participation, and apply the modeling results to mobile phone traces to extract the embedded activity information. The proposed approach enables us to spatially and temporally quantify, visualize, and examine urban activity landscapes in a metropolitan area and provides real-time decision support for the city. This study also demonstrates the potential value of combining new “big data” such as mobile phone traces and traditional travel surveys to improve transportation planning and urban planning and management. Keywords: Individual activity detection; urban sensing; mobile phone traces; travel survey |
| first_indexed | 2024-09-23T07:54:37Z |
| format | Article |
| id | mit-1721.1/120100 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T07:54:37Z |
| publishDate | 2019 |
| publisher | SAGE Publications |
| record_format | dspace |
| spelling | mit-1721.1/1201002022-09-23T09:34:54Z Inferring individual daily activities from mobile phone traces: A Boston example Ratti, Carlo Zhu, Yi Ferreira Jr, Joseph Diao, Mi, Ph. D. Massachusetts Institute of Technology Massachusetts Institute of Technology. Department of Urban Studies and Planning Massachusetts Institute of Technology. Media Laboratory Massachusetts Institute of Technology. SENSEable City Laboratory Ratti, Carlo Zhu, Yi Ferreira Jr, Joseph Understanding individual daily activity patterns is essential for travel demand management and urban planning. This research introduces a new method to infer individuals’ activities from their mobile phone traces. Using Metro Boston as an example, we develop an activity detection model with travel diary surveys to reveal the common laws governing individuals’ activity participation, and apply the modeling results to mobile phone traces to extract the embedded activity information. The proposed approach enables us to spatially and temporally quantify, visualize, and examine urban activity landscapes in a metropolitan area and provides real-time decision support for the city. This study also demonstrates the potential value of combining new “big data” such as mobile phone traces and traditional travel surveys to improve transportation planning and urban planning and management. Keywords: Individual activity detection; urban sensing; mobile phone traces; travel survey 2019-01-18T14:59:35Z 2019-01-18T14:59:35Z 2016-07 2015-09 2019-01-17T19:09:55Z Article http://purl.org/eprint/type/JournalArticle 0265-8135 1472-3417 http://hdl.handle.net/1721.1/120100 Diao, Mi et al. “Inferring Individual Daily Activities from Mobile Phone Traces: A Boston Example.” Environment and Planning B: Planning and Design 43, 5 (July 2016): 920–940 © 2015 The Author(s) https://orcid.org/0000-0003-2026-5631 https://orcid.org/0000-0003-0600-3803 http://dx.doi.org/10.1177/0265813515600896 Environment and Planning B: Planning and Design Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf SAGE Publications Other repository |
| spellingShingle | Ratti, Carlo Zhu, Yi Ferreira Jr, Joseph Diao, Mi, Ph. D. Massachusetts Institute of Technology Inferring individual daily activities from mobile phone traces: A Boston example |
| title | Inferring individual daily activities from mobile phone traces: A Boston example |
| title_full | Inferring individual daily activities from mobile phone traces: A Boston example |
| title_fullStr | Inferring individual daily activities from mobile phone traces: A Boston example |
| title_full_unstemmed | Inferring individual daily activities from mobile phone traces: A Boston example |
| title_short | Inferring individual daily activities from mobile phone traces: A Boston example |
| title_sort | inferring individual daily activities from mobile phone traces a boston example |
| url | http://hdl.handle.net/1721.1/120100 https://orcid.org/0000-0003-2026-5631 https://orcid.org/0000-0003-0600-3803 |
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