Identifying safe intersection design through unsupervised feature extraction from satellite imagery
This work addresses road safety by identifying intersection designs that reduce risky driving behaviors, potentially lowering global road trauma, though it is incremental as it applies existing methods to a new domain.
The study analyzed intersection designs across Australia using satellite imagery and deep learning to extract features like type and complexity, linking them to telematics data from 66 million kilometers of driving to identify safer designs, such as lower hard acceleration at three-way intersections and consistent low speeds on roundabouts.
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.