The main objective of this study is to validate a performance forecasting model for road surface temperatures ranging from 3 to 24 hours in the future using real-time weather forecasts as provided by the Korea Meteorological Administration (KMA). The model used here has been developed by researchers at the Korea Institute of Construction and Technology. The core concept of the model is based on heat transfer between road surfaces and the atmosphere. Preliminary tests were performed through comparing historically observed road surface temperatures obtained from road sensors with predicted temperatures from model runs at a specific site on the CheongWon-SangJoo Expressway. After testing, the model was partially run on the CheongWon-SangJoo Expressway at the Bo-Eun Branch of the Korea Highway Corporation to predict road surface temperatures 24 hours in advance. Test results showed that predicted road surface temperatures matched well with the observed temperatures. In addition, the average (either hourly or daily) difference between the two temperatures lies within ±2°C, indicating that the model performance is reasonable. For a real-time model run, road surface temperatures were predicted only at night as there were no instruments to measure solar radiation in real time along the roads. Nonetheless, a 12 hour forecast performed well, showing the average temperature differences between observed and predicted temperatures of about ±0.5°C.

Road surface conditions have a direct effect on the quality of driving, which in turn affects overall traffic flow. Many studies have been conducted to accurately identify road surface conditions using diverse technologies. However, these previously proposed methods may still be insufficient to estimate actual risks along the roads because the exact road risk levels cannot be determined from only road surface damage data. The actual risk level of the road must be derived by considering both the road surface damage data as well as other factors such as speed. In this study, the road hazard index is proposed using smartphone-obtained pothole and traffic data to represent the level of risk due to road surface conditions. The relevant algorithm and its operating system are developed to produce the estimated index values that are classified into four levels of road risk. This road hazard index can assist road agencies in establishing road maintenance plans and budgets and will allow drivers to minimize the risk of accidents by adjusting their driving speeds in advance of dangerous road conditions. To demonstrate the proposed risk hazard assessment methodology, road hazards were assessed along specific test road sections based on observed pothole and historical travel speed data. It was found that the proposed methodology provides a rational method for improving traffic safety.

This paper reports on current freight forecasting models used inside and outside of the U.S. Our study first classifies available models into seven classes, and describes important modeling concepts and case studies applying these models. Our study summarizes primary independent variables (inputs) and outputs for these freight models in order to help understand the current status of freight forecast modeling. In addition, we found that a common characteristic of the case studies shows that freight modeling is not always done as a single project at one point in time nor do the most successful studies rely on a single class of the model. Due to resource constraints or evolving freight needs, public agencies can opt to stage their freight model development or combine and remove portions of certain classes as they see fit. Our paper concludes by discussing three types of hybrid models involving commodity supply chains, urban logistics tours, and economic tours which are considered for the future modeling trends.