Modeling Severe Traffic Accidents with Spatial and Temporal Features

Abstract

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 other features in prediction which may be helpful in framing policies and targeting interventions for preventing severe traffic related accidents and injuries.