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.

While it is impossible to estimate when a road section will collapse, the understanding of road section deterioration can help asset managers predict the condition of road sections and take appropriate actions for rehabilitations. Deterioration forecasting modeling is an essential element for an efficient pavement management system. Although the Pavement Management System (PMS) has been introduced and operated for optimal road maintenance since the late 1980s in Korea, some problems for accurate prediction of road deterioration remain due to the quality of pavement performance data and the different pavement structural, material and environmental conditions. In this paper, a methodology to estimate the Markov transition probability model is presented to forecast the deterioration process of road sections. The deterioration states of the road sections are categorized into several ranks and the deterioration processes are characterized by hazard models. The Markov transition probabilities between the deterioration states, which are defined by the non-uniform or irregular intervals between the inspection points in time, are described by the exponential hazard models. Furthermore, in order to verify the validity of the proposed method, the applicability of the estimation methodology presented in this paper is investigated by using the empirical surface data set of the national highway in Korea.

In this study, we focus on resilience as the ability of specific infrastructure systems at the regional scale to absorb the shocks of extreme events, such as earthquakes. The occurrence of a disaster such as an earthquake leads to a rapid decrease in infrastructure performance. In the case of road networks, performance might refer to the number of drivers using the road within a certain period of time. The objective of this study is to propose a quantitative evaluation method to analyze road network performance (or performance loss) when natural disasters occur. Furthermore, we use cluster analysis and consider the performance loss and asset value in an attempt to propose a method to determine the critical path that should be prioritized for maintenance. This study aimed at analyzing hazard resilience from the network aspect through a scenario analysis depending on damage recovery after disaster occurrence. This study compared the hazard resilience speed to recover existing performance according to the scenario for damage recovery targeting the selected road network. It was found that the total increase in the utility (e.g., total travel time saved) gradually diminished as the restoration cost increased.

Traffic signal violations at urban intersections have become a major safety concern in urban areas since, compared to other types of crashes, they may cause a higher percentage of injuries and fatal crashes. Therefore, the deployment of red light cameras has been concentrated on urban intersections, both to prevent traffic signal violations and to reduce vehicle crashes. Other methodologies for establishing the safety impact of red light cameras are also being researched, but accurate results are difficult to ascertain. In this study, the safety impact of red light cameras on urban intersections in Daejeon Metropolitan City, South Korea was assessed using the Empirical Bayesian (EB) approach. According to the EB analysis in this study, red light camera has increased fatal crashes in all crash types by around 2%. However, it has also increased injury crashes in all crash types by about 53%. In specific types of crashes at urban intersections, such as angle, sideswipe & rear-end, and head-on, red light cameras have reduced fatal crashes by around 23%. However, injury crashes for typical crash types at urban intersections have actually increased by around 37%. Furthermore, the EB analysis results of total crashes are similar to those of injury crashes. This may be the result of a higher than average travel speed on arterial link sections in Daejeon compared to that of other metropolitan cities like Seoul. Therefore, the higher average speed on arterial roads may be the cause of the increase of injuries resulting from sideswipe & rear-end crashes.

In this paper, a pavement management accounting system (PMAS) is presented as a method for controlling the life cycle costs of road pavement. The aim of the PMAS is to provide decision makers with planning information regarding repair strategies and accounting information on asset values, with the aim of achieving efficient asset management of road pavement. This paper represents implementation of an integrated pavement system with an accounting system. This integration makes it possible to provide justification for a specific budget request on the basis of standard cost accounting information. Moreover, it links pavement deterioration explicitly to asset depreciation, which makes it possible to combine engineering and accounting information within the same analysis. By comparing the pros and cons of the three alternative methods of accounting, the authors conclude that deferred maintenance accounting is most likely to result in the efficient asset management of road pavement. The PMAS for asset managemen...