  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.
