Developing RTI IMS Software to Autonomously Manage Road Surface Quality, Adapting to Environmental Impacts

In the context of increasing negative environmental impacts driven by alarming climate change, extreme weather conditions will necessitate new ways to plan and manage the maintenance of road networks. This promotes the need to shift from reactive and predetermined maintenance methods to predictive and proactive maintenance. Predicting road damage has become a challenge and there is a need to build a more modern pavement quality assessment system in the future. Aimed at enhancing the resilience and sustainability of the road system against significant environmental impacts, this study develops software integrating advanced technology to intelligently manage road infrastructure quality. The software in this study applies machine learning technologies like Yolo, GPS positioning, and online Internet connectivity to automatically detect and locate road damage on the traffic network map. The study successfully developed software capable of automatically identifying road damage or signs of pavement quality degradation before they become more severe. The results of this study help support transportation management agencies by providing an overview of the health of each road within a road network and aiding in the efficient allocation of road maintenance funds. The software in this research not only achieves the goal of modernizing road patrol and maintenance activities but also opens avenues for exploring Industry 4.0 technologies in the development of intelligent traffic management systems.