# Estimating Traffic Disruption Patterns with Volunteered Geographic Information

## TL;DR

This study demonstrates that static features from OpenStreetMap can effectively predict traffic disruptions and volume, offering a cost-effective approach for traffic forecasting and policy planning.

## Contribution

It introduces a method to estimate traffic patterns using only static OSM features, outperforming traditional aggregate land use categories.

## Key findings

- Over 50% of traffic variation explained by static features
- Granular OSM point of interest data improves prediction accuracy
- Static features can be used for cost-effective traffic forecasting

## 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 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.