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    Identifying safe intersection design through unsupervised feature extraction from satellite imagery

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    Author
    Wijnands, JS; Zhao, H; Nice, KA; Thompson, J; Scully, K; Guo, J; Stevenson, M
    Date
    2020-10-19
    Source Title
    Computer-Aided Civil and Infrastructure Engineering
    Publisher
    Wiley
    University of Melbourne Author/s
    Thompson, Jason; Stevenson, Mark; Wijnands, Jasper; Nice, Kerry; Zhao, Haifeng
    Affiliation
    Architecture, Building and Planning
    Metadata
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    Document Type
    Journal Article
    Citations
    Wijnands, J. S., Zhao, H., Nice, K. A., Thompson, J., Scully, K., Guo, J. & Stevenson, M. (2020). Identifying safe intersection design through unsupervised feature extraction from satellite imagery. Computer-Aided Civil and Infrastructure Engineering, 36 (3), https://doi.org/10.1111/mice.12623.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/251373
    DOI
    10.1111/mice.12623
    NHMRC Grant code
    NHMRC/1136250
    ARC Grant code
    ARC/LE170100200
    ARC/DE180101411
    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.

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