PURPOSES : This study aims to calculate the estimation of travel time saving benefits from smart expressway construction by considering the willingness to pay for automated vehicles. METHODS : In this study, data were collected from 809 individual drivers through a stated preference survey. A multinomial logit model was constructed to analyze the choice behavior between arterial roads, expressways, and smart expressways. Through this, the values of time and benefits were estimated. RESULTS : The value of time was calculated at 19,379 won per vehicle per hour for arterial roads and expressways and 23,061 won per vehicle per hour for smart expressways. Applying these values to the Jungbu Naeryuk expressway, we evaluated the demand change and benefits resulting from the improvement to the smart expressways. The results show that the traffic volume on the Jungbu Naeryuk expressway is expected to increase by 4.7% to 20.7% depending on the changes in capacity. CONCLUSIONS : The travel time saving benefits are estimated as positive, resulting from the construction of smart expressways. The benefits resulting from the construction of new smart expressways are expected to be enhanced due to the anticipation of more significant time-saving effects.

PURPOSES : As road pavement design in an apartment complex varies from one site to another, it is practically difficult to calculate and estimate the traffic volume of construction vehicles. Therefore, this study introduces a methodology to estimate the number of construction vehicles and use it as an indicator to evaluate the conditions of road pavement in an apartment complex. METHODS: Through a literature review and site survey, the operational status of the construction vehicles passing through the site was identified, and the factors affecting the number of construction vehicles were analyzed. The methodologies used to estimate the number of construction vehicles were verified by calculating the Cumulative Load Prediction Index (CLPI), which is a predictive index of the cumulative load on each path. By using this index, the traffic volume of construction vehicles can be estimated based on the number of households in an apartment complex. To prove this definition, we examined the surface and core conditions, and compared the results against the predicted values. RESULTS : By comparing the Cumulative Load Prediction Index with the crack rate on the pavement surface, we obtained a correlation coefficient of 0.92. Furthermore, the analysis indicated that the core condition rate would decrease as the Cumulative Load Prediction Index increased. This correlation between the Cumulative Load Prediction Index, and the pavement surface and core status demonstrates that the traffic volume can be estimated by considering the number of households. CONCLUSIONS: The Cumulative Load Prediction Index presented in this study is a suitable indicator for estimating the conditions of the road pavement in an apartment complex by considering the number of households in the complex, even if the construction processes and characteristics vary.

As environmental concerns including climate change drive the strong regulations for car exhaust emissions, electric vehicles attract the public eye. The purpose of this study is to identify rural areas vulnerable for charging infrastructures based on the spatial distributions of the current gas stations and provide the target dissemination rates for promoting electric cars. In addition, we develop various scenarios for finding optimal way to expand the charging infrastructures through the administrative districts data including 11,677 gas stations, the number of whole national gas stations. Gas stations for charging infrastructures are randomly selected using the Monte Carlo Simulation (MCS) method. Evaluation criteria for vulnerability assessment include five considering the characteristic of rural areas. The optimal penetration rate is determined to 21% in rural areas considering dissemination efficiency. To reduce the vulnerability, the charging systems should be strategically installed in rural areas considering geographical characteristics and regional EV demands.