Assessment of the resilience of pedestrian roads based on image deep learning models

Currently, the evaluation of pedestrian paths is very time consuming. Additionally, disabled pedestrians do not tend to change their routes, even if pedestrian conditions are poor, resulting in reduced convenience and safety. Therefore, it is important to identify and act on the statuses of pedestrian paths quickly. Therefore, this study aimed to identify and process the conditions of pedestrian paths quickly to achieve high resilience. A resilience triangle was calculated according to the discrimination automation to analyse the corresponding values. Pedestrian path discrimination automation applies convolutional neural networks and 'you only look once' analysis to identify the road surface conditions of walkways and the presence of obstacles. Quantitative analyses for the safety and economic problems associated with transportation vulnerabilities through discrimination algorithms using deep image learning were carried out. As a result of the analyses, it was possible to determine the extent of damage with 94% accuracy if only damaged sidewalk photographs are captured. When this result was applied in Seoul, the benefits of improving pedestrian paths were quantitatively calculated to be South Korean Won (KRW) 41.2 billion (1 KRW=US$0.00085). This study may secure pedestrian resilience and improve convenience in the current scenario of a rapidly ageing population.