  We present an approach to estimate the severity of traffic related accidents
in aggregated (area-level) and disaggregated (point level) data. Exploring
spatial features, we measure complexity of road networks using several area
level variables. Also using temporal and other situational features from open
data for New York City, we use Gradient Boosting models for inference and
measuring feature importance along with Gaussian Processes to model spatial
dependencies in the data. The results show significant importance of complexity
in aggregated model as well as as other features in prediction which may be
helpful in framing policies and targeting interventions for preventing severe
traffic related accidents and injuries.
