Journal article
Estimating traffic disruption patterns with volunteered geographic information
- Abstract:
- Accurate understanding and forecasting of traffic is a key contemporary problem for policymakers. Road networks are increasingly congested, yet traffic data is often expensive to obtain, making informed policy-making harder. This paper explores the extent to which traffic disruption can be estimated using features from the volunteered geographic information site OpenStreetMap (OSM). We use OSM features as predictors for linear regressions of counts of traffic disruptions and traffic volume at 6,500 points in the road network within 112 regions of Oxfordshire, UK. We show that more than half the variation in traffic volume and disruptions can be explained with OSM features alone, and use cross-validation and recursive feature elimination to evaluate the predictive power and importance of different land use categories. Finally, we show that using OSM’s granular point of interest data allows for better predictions than the broader categories typically used in studies of transportation and land use.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 2.3MB, Terms of use)
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- Publisher copy:
- 10.1038/s41598-020-57882-2
Authors
- Publisher:
- Springer Nature
- Journal:
- Scientific Reports More from this journal
- Volume:
- 10
- Article number:
- 1271
- Publication date:
- 2020-01-27
- Acceptance date:
- 2019-12-24
- DOI:
- EISSN:
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2045-2322
- ISSN:
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2045-2322
- Pmid:
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31988334
- Language:
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English
- Keywords:
- Pubs id:
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1046233
- Local pid:
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pubs:1046233
- Deposit date:
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2020-03-09
- ARK identifier:
Terms of use
- Copyright holder:
- Camargo et al.
- Copyright date:
- 2020
- Rights statement:
- © The Author(s) 2020. Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
- Notes:
- A previous version of this paper was presented at CARMA 2018: 2nd International Conference on Advanced Research Methods and Analytics.
- Licence:
- CC Attribution (CC BY)
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