Predicting Road Accident Risk Using Google Maps Images and A Convolutional Neural Network

Abstract

Location specific characteristics of a road segment such as road geometry as well as surrounding road features can contribute significantly to road accident risk. A Google Maps image of a road segment provides a comprehensive visual of its complex geometry and the surrounding features. This paper proposes a novel machine learning approach using Convolutional Neural Networks (CNN) to accident risk prediction by unlocking the precise interaction of these many small road features that work in combination to contribute to a greater accident risk. The model has worldwide applicability and a very low cost/time effort to implement for a new city since Google Maps are available in most places across the globe. It also significantly contributes to existing research on accident prevention by allowing for the inclusion of highly detailed road geometry to weigh in on the prediction as well as the new locationbased attributes like proximity to schools and businesses.