The run-off road crash (RORC) is a representative type of lethal crash. The severity of RORC has increased owing to a combination of factors, such as roadside geometry, traffic conditions, and weather/climatic conditions. In this study, a model for estimating the RORC severity was developed based on various factors, including fixed objects, roadway geometry, traffic conditions, and road traffic environment. To develop the model, the accident data of crashes with roadside fixed objects on highways, as well as information on fixed object-related variables and roadway geometry-related variables, were collected. To improve the model in terms of implementing a close reflection of the real world, a learning method with tree augmented naïve Bayes (TAN), which takes into account the causal links between variables, was applied. The results of the analysis showed that the severity of crashes with roadside fixed objects increased sharply when the vertical slope was ≥4%, the radius of the curve was ≥250 m, the distance between the fixed object and the roadway was less than 3 m, or the density of fixed objects installation was greater than 2 for every 10 m. The proposed model allows for an analysis of sections with a high RORC severity on the roadways in operation and provides improvement measures to reduce the severity of RORC.

The smart intersection (SI) systems, as they are named in the Republic of Korea, are part of the ITS services implemented under local government projects with financial support from the central government. They collect real-time traffic data available at signalized intersections with advanced detection systems for surveillance purposes only. A traffic signal method utilizing such valuable data has been desirable but unavailable as yet in practice. This paper proposes a new approach to designing traffic signal timings, reflecting the demand changing in real time, by utilizing SI surveillance data. The proposed artificial neural network model generates suitable traffic signal timings trained to be near optimum based on surveillance data for each directional movement.

The carpet-Type materials painted yellow in pedestrian waiting areas are safety devices used to prevent collisions with vehicles and children. They are primarily recommended to be placed on both the pavement and the wall sides, but local governments in the Republic of Korea place them only on the pavement side, excluding the wall part to reduce the deployment costs and increase the number of deployment sites. The difference in effectiveness between these Types may be different, but this has not yet been proven. This study examines the difference between these Types found in the field test. The test employed two different measures: (1) the level of driver visibility and (2) the vehicle deceleration rate. As a result of the analysis, the yellow carpet installed on both the pavement and the wall sides was comparatively more effective. The test results suggest that this yellow carpet Type should be installed on both pavement and wall sides, particularly for school zone areas, to maximize its safety effects.