After empirically investigating the vehicle accident risks by age groups, various programs and policies have been imposed to reduce the old's risks in other countries. In Korea, it is little known the risk level by age groups and no policy changes has been implemented even if the number of vehicle accidents occurred by the old has been rapidly rising while the total number of vehicle accidents has been decreasing. This study empirically investigates the vehicle accident risks by age groups and the results show that the old drivers over age 65 has the highest accident risk except for the young drivers below age 25. Thus, we emphasize the necessity of reinforcing the qualifications for reissuing the drive licence and programs for educating the old drivers in Korea which is facing the most rapid population aging in the world. On the other hand, various changes are needed reflecting the old drivers such as reforming the road signs, issuing a sticker and providing them incentives such that the old drivers use the public transportation instead of self-driving.
Annual average daily traffic(AADT) serves as important basic data in the transportation sector. AADT is used as design traffic which is the basic traffic volume in transportation planning. Despite of its importance, at most locations, AADT is estimated using short term traffic counts. An accurate AADT is calculated through permanent traffic counts at limited locations. This study dealt with estimating AADT using various models considering both the spatial correlation and time series data. Kriging models which are commonly used spatial statistics methods were applied and compared with each model. Additionally the External Universal kriging model, which includes explanatory variables, was used to assure accuracy of AADT estimation. For evaluation of various kriging methods, AADT estimation error, proposed using national highway permanent traffic count data, was analyzed and their performances were compared. The result shows the accuracy enhancement of the AADT estimation.
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