  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 from
static 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 static
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 aggregate categories typically used in studies of
transportation and land use.
