Spatiotemporal variation in travel regularity through transit user profiling
Open accessAbstract
New smart card datasets are providing new opportunities to explore travel behaviour in much greater depth than anything accomplished hitherto. Part of this quest involves measuring the great array of regular patterns within such data and explaining these relative to less regular patterns which have often been treated in the past as noise. Here we use a simple method called DBSCAN to identify clusters of travel events associated with particular individuals whose behaviour over space and time is captured by smart card data. Our dataset is a sequence of three months of data recording when and where individual travellers start and end rail and bus travel in Greater London. This dataset contains some 640 million transactions during the period of analysis we have chosen and it enables us to begin a search for regularities at the most basic level. We first define measures of regularity in terms of the proportions of events associated with temporal, modal (rail and bus), and service regularity clusters, revealing that the frequency distributions of these clusters follow skewed distributions with different means and variances. The analysis then continues to examine how regularity relative to irregular travel across space, demonstrating high regularities in the origins of trips in the suburbs contrasted with high regularities in the destinations in central London. This analysis sets the agenda for future research into how we capture and measure the differences between regular and irregular travel which we discuss by way of conclusion.
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ML is particularly relevant for deducing information from novel data, such as learning about public transit users from smart card data.
“ML is particularly relevant for deducing information from novel data-for example, learning about the behavior of public transit users from smart card data [280,515].”
From Tackling climate change with machine learning (2023) · cites this paper as [515]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Manley, E., Zhong, C., & Batty, M.
- Affiliations
- University College London (GB)
- Corresponding author
- Manley, Ed
- Venue
- Transportation
- Published
- 2016-11-10
- Volume
- 45
- Issue
- 3
- Pages
- 703-732
- ISSN
- 0049-4488 (ISSN-L)
- 1572-9435
- PMC
- PMC6560821
- OpenAlex
- W2551071090
- Cited by
- 82
- References
- 43
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 6 · 2023: 8 · 2022: 15 · 2021: 12 · 2020: 8 · 2019: 9 · 2018: 6 · 2017: 3 · 2016: 1
- FWCI
- 10.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Transportation (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Portal (King's College London) (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- European Research Council (249393)
- Topics
- Social Sciences › Transportation › Transportation Planning and Optimization
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Social Sciences › Transportation › Urban Transport and Accessibility
- Keywords
- smart card data, DBSCAN, travel behavior, service regularity, origin-destination trips, spatiotemporal variation
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