Estimating Traffic Disruption Patterns with Volunteered Geographic Information

Chico Q. Camargo, Jonathan Bright, Graham McNeill, Sridhar Raman, Scott A. Hale

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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.

Original languageEnglish
Article number1271
JournalScientific Reports
Issue number1
StatePublished - 1 Dec 2020

Bibliographical note

Publisher Copyright:
© 2020, The Author(s).


Dive into the research topics of 'Estimating Traffic Disruption Patterns with Volunteered Geographic Information'. Together they form a unique fingerprint.

Cite this