Accurate seismic vulnerability assessment requires high quality and large amounts of ground motion data. Ground motion data generated from time series contains not only the seismic waves but also the background noise. Therefore, it is crucial to determine the high-pass cut-off frequency to reduce the background noise. Traditional methods for determining the high-pass filter frequency are based on human inspection, such as comparing the noise and the signal Fourier Amplitude Spectrum (FAS), f2 trend line fitting, and inspection of the displacement curve after filtering. However, these methods are subject to human error and unsuitable for automating the process. This study used a deep learning approach to determine the high-pass filter frequency. We used the Mel-spectrogram for feature extraction and mixup technique to overcome the lack of data. We selected convolutional neural network (CNN) models such as ResNet, DenseNet, and EfficientNet for transfer learning. Additionally, we chose ViT and DeiT for transformer-based models. The results showed that ResNet had the highest performance with R2 (the coefficient of determination) at 0.977 and the lowest mean absolute error (MAE) and RMSE (root mean square error) at 0.006 and 0.074, respectively. When applied to a seismic event and compared to the traditional methods, the determination of the high-pass filter frequency through the deep learning method showed a difference of 0.1 Hz, which demonstrates that it can be used as a replacement for traditional methods. We anticipate that this study will pave the way for automating ground motion processing, which could be applied to the system to handle large amounts of data efficiently.

PURPOSES : There are generally various driving behaviors in toll collection areas, including lane changing, merging and diverging, and other behaviors. Because of these behaviors, accident risk and traffic congestion may occur. To mitigate these problems, multi-lane electronic toll collection systems have been developed with a high-speed limit of 80 km/h. This study was conducted to investigate travel speed changes and effects through multi-lane electronic toll collection systems with a high-speed limit. METHODS : Traffic simulations were conducted using VISSIM. Before conducting simulations, driving behavior around the toll collection areas was observed, and field data were collected based on drones for peak and non-peak hours. In addition, safety effect evaluations were conducted based on conflict analyses using the SSAM software. RESULTS : Through multi-lane electronic toll collection systems with a high-speed limit, the travel speed on the toll collection area was increased, and traffic operational efficiencies were identified. However, different speed variations were produced as observation locations in toll collection areas. Speed variations were mitigated at most locations except the area within the tollbooth because of the high speed limit for multi-lane electronic tollbooth. CONCLUSIONS : It was important to manage lane-changing activities, and this may influence traffic operational effects. Safety effects were also identified through conflict analyses.

PURPOSES: This investigational survey is to observe a proper spatial aggregation method for path travel time estimation using the hi-pass DSRC system. METHODS: The links which connect the nodes of section detectors location are used for path travel time estimation traditionally. It makes some problem such as increasing accumulation errors and processing times. In this background, the new links composition methods for spatial aggregation are considered by using some types of nodes as IC, JC, RSE combination. Path travel times estimated by new aggregation methods are compared with PBM travel times by MAE, MAPE and statistical hypothesis tests. RESULTS : The results of minimum sample size and missing rate for 5 minutes aggregation interval are satisfied except for JC link path travel time in Seoul TG~Kuemho JC. Thus, it was additionally observed for minimum sample size satisfaction. In 15, 30 minutes and 1 hour aggregation intervals, all conditions are satisfied by the minimum sample size criteria. For accuracy test and statistical hypothesis test, it has been proved that RSE, Conzone, IC, JC links have equivalent errors and statistical characteristics. CONCLUSIONS : There are some errors between the PBM and the LBM methods that come from dropping vehicles by rest areas. Consequently, this survey result means each of links compositions are available for the estimation of path travel time when PBM vehicles are missed.

본 연구에서는 하이패스 차로의 속도감소를 유도하기 위한 적절한 노면표시를 도출하기 위하여 감성공학적 분석방법을 활용하여 연구를 수행하였다. 현재 고속도로 영업소에서는 감속유도를 위하여 하이패스 차로에 갈매기 노면표시를 설치하여 운영하고 있다. 본 연구에서는 하이패스 차로의 감속유도를 위하여 갈매기 노면표시 및 Peripheral Transverse Bar(PT bar)에 대한 효과평가를 실시하고, 두 가지 노면표시를 조합하여 6가지 시나리오를 도출하였다. 도출된 시나리오에 대하여 이용자의 Perception 측정 및 분석을 통한 노면표시 설계 시 Human Factor를 반영할 수 있는 방법론을 제시하였다. 분석결과 빗살무늬의 PT bar 및 60˚ 각도의 갈매기 노면표시의 조합시나리오가 가장 적절한 노면표시로 선정되었다. 본 연구의 결과는 하이패스 차량들의 통과속도를 감소시켜 고속도로의 안전성을 높일 수 있을 것으로 기대된다.