Detecting weak public transport connections from cellphone and public transport data
Author(s)Holleczek, Thomas; Yu, Liang; Lee, Joseph Kang; Senn, Oliver; Ratti, Carlo; Jaillet, Patrick; ... Show more Show less
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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.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Department of Urban Studies and Planning
Proceedings of the 2014 International Conference on Big Data Science and Computing (BigDataScience '14)
Association for Computing Machinery (ACM)
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.
Author's final manuscript