In Korea, data on pavement conditions, such as cracks, rutting depth, and the international roughness index, are obtained using automatic pavement condition investigation equipment, such as ARAN and KRISS, for the same sections of national highways annually to manage their pavement conditions. This study predicts the deterioration of road pavement by using monitoring data from the Korean National Highway Pavement Management System and a recurrent neural network algorithm. The constructed algorithm predicts the pavement condition index for each section of the road network for one year by learning from the time series data for the preceding 10 years. Because pavement type, traffic load, and environmental characteristics differed by section, the sequence lengths (SQL) necessary to optimize each section were also different. The results of minimizing the root-mean-square error, according to the SQL by section and pavement condition index, showed that the error was reduced by 58.3–68.2% with a SQL value of 1, while pavement deterioration in each section could be predicted with a high coefficient of determination of 0.71–0.87. The accurate prediction of maintenance timing for pavement in this study will help optimize the life cycle of road pavement by increasing its life expectancy and reducing its maintenance budget.

As urbanization advances, the number of people living in high-rise buildings is gradually increasing. However, few studies on evacuation planning during emergencies (or simulations) simultaneously considering high-rise buildings and urban areas have been carried out. This study targeted Sejong city in Korea to analyze evacuation behavior in high-rise apartments and the effect of evacuation information. For the disaster situation, this study assumed a worst-case situation in which apartment residents need to quickly evacuate through apartment stairwells and pedestrian roads owing to a major disaster and unexpected accident occurrence. Analysis scenarios were divided by considering the evacuation information level: 1) starting evacuation within 15 min, 2) starting evacuation within 30 min, and 3) starting evacuation within 60 min. Also, scenario 4 is for the case that information for a new optimal shelter location was informed to evacuees, whereas scenario 5 is for the case that information for an existing optimal shelter location was informed to evacuees. The analysis results indicate the evacuation completion time is greatly reduced if high-level information is provided. The results of this study offer guidance on emergency situations using micro-simulations and the Social Force model.

Recently, with the increased frequency of disasters, the demand for measures to secure the golden hour after disasters has been increasing. Therefore, it is necessary to plan and select road infrastructures for effective disaster response. The purpose of this study was to determine emergency road networks for rapid rescue, paramedical activity, and resource transfer in the event of an earthquake in Seoul (including nearby areas). Decisions were made to select a suitable emergency road network in Seoul based on the collection and management of earthquake-related data, grid-based quantitative evaluation of factors regarding demands during disasters and provision of response resources, link-based importance evaluation and grouping analysis, and results of grid and link evaluations. Analysis was first conducted on 16 types of disaster demands, including building, facility, demographic, and response resource-provision data. An expert survey was conducted, and each factor was weighted and integrated into the grid structure for grid-based analysis. Roads and bridges that could play critical roles in an earthquake were selected and grouped in the road network for link-based analysis. The final emergency road network was chosen based on the quantitative and qualitative results from the second and third stages.