Detecting weak public transport connections from cellphone and public transport data
Many modern and growing cities are facing declines in public transport usage, with few efficient methods to explain why. In this article, we show that urban mobility patterns and transport mode choices can be derived from cellphone call detail records coupled with public transport data recorded from...
| Main Authors: | , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Association for Computing Machinery (ACM)
2016
|
| Online Access: | http://hdl.handle.net/1721.1/101682 https://orcid.org/0000-0003-2026-5631 https://orcid.org/0000-0002-8585-6566 |
| _version_ | 1826203952695214080 |
|---|---|
| author | Holleczek, Thomas Yu, Liang Lee, Joseph Kang Senn, Oliver Ratti, Carlo Jaillet, Patrick |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Holleczek, Thomas Yu, Liang Lee, Joseph Kang Senn, Oliver Ratti, Carlo Jaillet, Patrick |
| author_sort | Holleczek, Thomas |
| collection | MIT |
| description | Many modern and growing cities are facing declines in public transport usage, with few efficient methods to explain why. In this article, we show that urban mobility patterns and transport mode choices can be derived from cellphone call detail records coupled with public transport data recorded from smart cards. Specifically, we present new data mining approaches to determine the spatial and temporal variability of public and private transportation usage and transport mode preferences across Singapore. Our results, which were validated by Singapore's quadriennial Household Interview Travel Survey (HITS), revealed that there are 3.5 million public and 4.3 million private inter-district trips (HITS: 3.5 million and 4.4 million, respectively). Along with classifying which transportation connections are weak, the analysis shows that the mode share of public transport use increases from 38% in the morning to 44% around mid-day and 52% in the evening. |
| first_indexed | 2024-09-23T12:46:49Z |
| format | Article |
| id | mit-1721.1/101682 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:46:49Z |
| publishDate | 2016 |
| publisher | Association for Computing Machinery (ACM) |
| record_format | dspace |
| spelling | mit-1721.1/1016822022-10-01T11:03:34Z Detecting weak public transport connections from cellphone and public transport data Holleczek, Thomas Yu, Liang Lee, Joseph Kang Senn, Oliver Ratti, Carlo Jaillet, Patrick Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Department of Urban Studies and Planning Ratti, Carlo Jaillet, Patrick Many modern and growing cities are facing declines in public transport usage, with few efficient methods to explain why. In this article, we show that urban mobility patterns and transport mode choices can be derived from cellphone call detail records coupled with public transport data recorded from smart cards. Specifically, we present new data mining approaches to determine the spatial and temporal variability of public and private transportation usage and transport mode preferences across Singapore. Our results, which were validated by Singapore's quadriennial Household Interview Travel Survey (HITS), revealed that there are 3.5 million public and 4.3 million private inter-district trips (HITS: 3.5 million and 4.4 million, respectively). Along with classifying which transportation connections are weak, the analysis shows that the mode share of public transport use increases from 38% in the morning to 44% around mid-day and 52% in the evening. 2016-03-11T15:23:34Z 2016-03-11T15:23:34Z 2014-08 Article http://purl.org/eprint/type/ConferencePaper 9781450328913 http://hdl.handle.net/1721.1/101682 Thomas Holleczek, Liang Yu, Joseph Kang Lee, Oliver Senn, Carlo Ratti, and Patrick Jaillet. 2014. Detecting weak public transport connections from cellphone and public transport data. In Proceedings of the 2014 International Conference on Big Data Science and Computing (BigDataScience '14). ACM, New York, NY, USA, 8 pages. https://orcid.org/0000-0003-2026-5631 https://orcid.org/0000-0002-8585-6566 en_US http://dx.doi.org/10.1145/2640087.2644164 Proceedings of the 2014 International Conference on Big Data Science and Computing (BigDataScience '14) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Association for Computing Machinery (ACM) MIT web domain |
| spellingShingle | Holleczek, Thomas Yu, Liang Lee, Joseph Kang Senn, Oliver Ratti, Carlo Jaillet, Patrick Detecting weak public transport connections from cellphone and public transport data |
| title | Detecting weak public transport connections from cellphone and public transport data |
| title_full | Detecting weak public transport connections from cellphone and public transport data |
| title_fullStr | Detecting weak public transport connections from cellphone and public transport data |
| title_full_unstemmed | Detecting weak public transport connections from cellphone and public transport data |
| title_short | Detecting weak public transport connections from cellphone and public transport data |
| title_sort | detecting weak public transport connections from cellphone and public transport data |
| url | http://hdl.handle.net/1721.1/101682 https://orcid.org/0000-0003-2026-5631 https://orcid.org/0000-0002-8585-6566 |
| work_keys_str_mv | AT holleczekthomas detectingweakpublictransportconnectionsfromcellphoneandpublictransportdata AT yuliang detectingweakpublictransportconnectionsfromcellphoneandpublictransportdata AT leejosephkang detectingweakpublictransportconnectionsfromcellphoneandpublictransportdata AT sennoliver detectingweakpublictransportconnectionsfromcellphoneandpublictransportdata AT ratticarlo detectingweakpublictransportconnectionsfromcellphoneandpublictransportdata AT jailletpatrick detectingweakpublictransportconnectionsfromcellphoneandpublictransportdata |