PURPOSES : Under the Traffic Safety Act, the installation and management of transportation facilities (facilities and attachments necessary for the operation of transportation, such as roads, railways, and terminals) must take necessary measures to ensure traffic safety, such as enhancing safety facilities. Recently, railway operators have graded the congestion level inside railway stations and vehicles, addressing safety and convenience issues arising from congestion and providing this information to users. However, for bus-related transportation facilities (such as bus stops, terminals, and transfer facilities), criteria and related research for assessing traffic congestion are lacking. Therefore, this study developed a model for the congestion risk factors of four bus-related transportation facilities and proposed criteria for classifying congestion risk levels. METHODS : This study involved selecting congestion risk influence variables for each traffic facility through field surveys, calculating congestion risk index values through evacuation and pedestrian simulations, and constructing a congestion risk influence model based on the ridge model. RESULTS : The factors influencing congestion were selected to include the number of people waiting, effective sidewalk width, and number of bus stops. As a result of developing congestion risk grades, the central bus stops were determined to be in a severe stage if the Average Waiting Time (AWT) was 2.7 or above. Roadside bus stops were considered severe at 4.2, underground metropolitan transit centers at 3.7, and bus terminals at 5.9 or above. CONCLUSIONS : This study can help establish a foundation for a safety management system for congested areas in transportation facilities. When the congestion risk prediction results correspond to cautionary or severe levels, measures that can reduce congestion risk must be applied to ensure the safety of road users.

PURPOSES : Basic research to calculate the appropriate gap acceptance for autonomous vehicles at merging section. Research on whether users prefer short or long gap acceptance. METHODS : Using a driving simulator, experience autonomous driving with different gap acceptance in different weather condition, and analyze which gap acceptance is preferred using survey and biometric data. RESULTS : Regardless of the weather condition, long gap acceptance was preferred, and difference was especially clear in rainy or foggy situation. CONCLUSIONS : It was analyzed that users prefer long gap acceptance over short gap acceptance, and that they feel less frustrated due to long gap acceptance when weather condition is poor.

PURPOSES : Evaluation of the effectiveness of changing the form of yellow carpet installation as a way to reduce child pedestrian traffic accidents. METHODS : Through expert opinion, two improvement plans for yellow carpet installation (oblique type, extended type) were derived. The improvement paln was built in virtual reality, and a virtual driving experiment was performed using a driving simulator and eye-trakcing device. The improvement effects of the two alternatives were evaluated by analyzing eye-tracking data and driving behavior. RESULTS : In the case of the oblique type, it was analyzed that it was effective in improving the total gaze time and first gaze position compared to the normal type. In the case of the extended type, it was analyzed that the workload during operation can be reduced. However, neither of them had a significant effect on driving behavior. CONCLUSIONS : Although the change in the yellow carpet installation type did not affect the driver's driving behavior, it had advantages in terms of visual behavior and workload while driving, so it can be considered as an alternative among measures to improve traffic accidents involving children and pedestrians.

PURPOSES : The use of virtual driving tests to determine actual road driving behavior is increasing. However, the results indicate a gap between real and virtual driving under same road conditions road based on ergonomic factors, such as anxiety and speed. In the future, the use of virtual driving tests is expected to increase. For this reason, the purpose of this study is to analyze the gap between real and virtual driving on same road conditions and to use a calibration formula to allow for higher reliability of virtual driving tests. METHODS : An intelligent driving recorder was used to capture real driving. A driving simulator was used to record virtual driving. Additionally, a virtual driving map was made with the UC-Win/Road software. We gathered data including geometric structure information, driving information, driver information, and road operation information for real driving and virtual driving on the same road conditions. In this study we investigated a range of gaps, driving speeds, and lateral positions, and introduced a calibration formula to the virtual record to achieve the same record as the real driving situation by applying the effects of the main causes of discrepancy between the two (driving speed and lateral position) using a linear regression model. RESULTS: In the virtual driving test, driving speed and lateral position were determined to be higher and bigger than in the real Driving test, respectively. Additionally, the virtual driving test reduces the concentration, anxiety, and reality when compared to the real driving test. The formula includes four variables to produce the calibration: tangent driving speed, curve driving speed, tangent lateral position, and curve lateral position. However, the tangent lateral position was excluded because it was not statistically significant . CONCLUSIONS: The results of analyzing the formula from MPB (mean prediction bias), MAD (mean absolute deviation) is after applying the formula to the virtual driving test, similar to the real driving test so that the formula works. Because this study was conducted on a national, two-way road, the road speed limit was 80 km/h, and the lane width was 3.0-3.5 m. It works in the same condition road restrictively.