Identifying safe intersection design through unsupervised feature extraction from satellite imagery

TL;DR

This study uses satellite imagery and deep learning to analyze intersection designs across Australia, linking infrastructure features to driving behaviors and safety outcomes, aiming to identify safer intersection configurations.

Contribution

It introduces a novel approach combining satellite imagery and unsupervised feature extraction to systematically analyze intersection safety and design patterns.

Findings

Four-way intersections have more hard acceleration events.

T-intersections show fewer hard deceleration events.

Roundabouts maintain lower average speeds.

Abstract

The World Health Organization has listed the design of safer intersections as a key intervention to reduce global road trauma. This article presents the first study to systematically analyze the design of all intersections in a large country, based on aerial imagery and deep learning. Approximately 900,000 satellite images were downloaded for all intersections in Australia and customized computer vision techniques emphasized the road infrastructure. A deep autoencoder extracted high-level features, including the intersection's type, size, shape, lane markings, and complexity, which were used to cluster similar designs. An Australian telematics data set linked infrastructure design to driving behaviors captured during 66 million kilometers of driving. This showed more frequent hard acceleration events (per vehicle) at four- than three-way intersections, relatively low hard deceleration frequencies at T-intersections, and consistently low average speeds on roundabouts. Overall, domain-specific feature extraction enabled the identification of infrastructure improvements that could result in safer driving behaviors, potentially reducing road trauma.