# Modeling Severe Traffic Accidents With Spatial And Temporal Features

## TL;DR

This paper introduces a novel approach combining spatial and temporal features with machine learning models to predict traffic accident severity at different data granularities, aiding policy and intervention strategies.

## Contribution

It presents a new method integrating spatial network complexity, temporal, and situational features using Gradient Boosting and Gaussian Processes for accident severity prediction.

## Key findings

- Spatial complexity significantly influences accident severity predictions.
- Temporal and situational features improve model accuracy.
- Models can inform targeted traffic safety interventions.

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