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 di

SUBMITTER: Camargo CQ 

PROVIDER: S-EPMC6985234 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature