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

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 i..