Abstract Cycling is associated with health, environmental and societal benefits. Urban infrastructure design catering to cyclists’ safety can potentially reduce cyclist crashes and therefore, injury and/or mortality. This research uses publicly available big data such as maps and satellite images to capture information of the environment of cyclist crashes. Deep learning methods, such as generative adversarial networks (GANs), learn from these datasets and explore factors associated with cyclist crashes. This assumes existing environmental patterns for roads at locations with and without cyclist crashes, and suggests a deep learning method is able to learn the hidden features from map and satellite images and model the road environments using GANs. Experiments validated the method by identifying factors associated with cyclist crashes that show agreement with existing literature. Additionally, it revealed the potential of this method to identify implicit factors that have not been previously identified in the existing literature. These results provide visual indications about what streetscapes are safer for cyclist and suggestions on how city streetscapes should be planned or reconstructed to improve it.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Unsupervised Deep Learning to Explore Streetscape Factors Associated with Urban Cyclist Safety




    Publication date :

    2019-01-01


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Cyclist Conspicuity and Safety

    mcGonigle, D. / SPOKES | British Library Conference Proceedings | 2001


    Urban Streetscape Design and Crash Severity

    Harvey, Chester / Aultman-Hall, Lisa | Transportation Research Record | 2019


    Infrastructure and cyclist safety

    Reid, Stuart | TIBKAT | 2010

    Free access


    Can Automated Vehicles Improve Cyclist Safety in Urban Areas?

    Pavlos Tafidis / Ali Pirdavani / Tom Brijs et al. | DOAJ | 2019

    Free access