The focus of policymaking in Korea has changed from vehicle-centric road environments to people-centric environments. As the importance of walking has increased, the construction of pedestrian paths and interest in pedestrian environments have also increased. However, problem recognition and resolution require considerable time in the event of a problem in a pedestrian path. People with reduced mobility tend to resist changes in roads that they use. Thus, damaged pedestrian paths and obstacles pose a considerable risk and economic loss to transportation. In this study, we aimed to minimize the time and cost required for the evaluation of pedestrian paths by developing an automatic system for determining damage using integrated You Only Look Once (YOLO) and convolutional neural network (CNN) image deep learning algorithms. We constructed a model using image deep learning by dividing the steps into walkway breakage detection and score evaluation according to the degree of breakage. The accuracy of the model was determined to be 92%. In the future, the evaluation of pedestrian path damage is expected to be automated using images and videos, thereby reducing the time required for the detection and restoration of damage.

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.

Energy efficiency is key to achieving the Sustainable Development Goals globally. Energy consumption in the transport sector is constantly increasing, and governments are implementing policies to reduce car use by shifting the focus from roads to walking. With the rise of pedestrianization policies, autonomous mobile robots (AMRs) are becoming increasingly useful. Autonomous robotic services are being used in various fields such as traffic management, logistics, and personal mobility assistance. However, AMRs research has focused on technology development, route planning, and cost reduction, with relatively little research on how to make robots more energy efficient. As these autonomous robotic services become more popular, there is a need to discuss how to efficiently use energy. This study analyses the characteristics of the hardware required for AMRs to operate. In particular, the density of obstacles in the surrounding environment is defined as saturation for the use of Lidar, and the effectiveness of the proximal policy optimization reinforcement learning algorithm is analysed to propose an energy efficiency plan for the saturation density. In the future, a large number of robots are expected to be used, and efficient energy use of such hardware will contribute to building sustainable cities.