This study proposes a data-driven framework for analyzing freeway driving behavior using multiple real-world trajectory datasets, and applies it consistently to mainline and ramp sections. The four large-scale datasets—namely highD, exiD, NGSIM I-80, and NGSIM US- 101—were processed through a unified preprocessing pipeline that converted all variables to International System Units(SI), resampled trajectories to 10 Hz, applied Savitzky-Golay smoothing to speed, and removed physically implausible and statistical outliers based on joint physical-statistical criteria. For each vehicle, 24 summary features were constructed from six longitudinal indicators–speed, acceleration, deceleration, time headway (THW), distance headway (DHW), and time-to-collision (TTC)–using their minimum, maximum, mean, and standard deviation. Indicator distributions by road type were compared using relative frequency histograms with common binning; then, principal component analysis (PCA) and K-means clustering were applied independently to each dataset. The leading principal components revealed interpretable axes related to longitudinal driving intensity (speed and acceleration level), safety margin (THW/DHW/TTC), and onramp sections; responsiveness was characterized by acceleration-deceleration variability, as observed within the analyzed datasets. Cluster interpretation yielded four relative driving behavior categories–aggressive, responsive, stable, and defensive–defined within each dataset based on indicator levels and variability rather than absolute thresholds.

PURPOSES : Because a driving simulator typically focuses on analyzing a driver’s driving behavior, it is difficult to analyze the effect on the overall traffic flow. In contrast, traffic simulation can analyze traffic flow, that is, the interaction between vehicles; however, it has limitations in describing a driver’s driving behavior. Therefore, a method for integrating the simulator and traffic simulation was proposed. Information that could be controlled through driving experiments was used, and only the lane-change distance was considered so that a more natural driving behavior could be described in the traffic flow. METHODS : The simulated connection method proposed in this study was implemented under the assumption of specific traffic conditions. The driver’s lane-changing behavior (lane-changing distance, deceleration, and steering wheel) due to the occurrence of road debris was collected through a driving study. The lane-change distance was input as a parameter for the traffic simulation. Driving behavior and safety were compared between the basic traffic simulation setting, in which the driver's driving behavior information was not reflected, and the situation in which the driving simulator and traffic simulation were integrated. RESULTS : The number of conflicts between the traffic simulation default settings (Case 1) and the situation in which the driving simulator and traffic simulation were integrated (Case 2) was determined and compared for each analysis. The analysis revealed that the number of conflicts varied based on the level of service and road alignment of the analysis section. In addition, a statistical analysis was performed to verify the differences between the scenarios. There was a significant difference in the number of conflicts based on the level of service and road alignment. When analyzing a traffic simulation, it is necessary to replicate the driving behavior of the actual driver. CONCLUSIONS : We proposed an integration plan between the driving simulator and traffic simulation. This information can be used as fundamental data for the advancement of simulation integration methods.

목적 : 본 연구는 다양한 운전상황에서의어려움 정도를 평가하는 안전운전행동 체크리스트를 개발할 때 수집된 데이터를 라쉬분석을 활용하여 부적절한 문항을 확인하고 타당도를 검증하고자 하였다. 연구방법 : 안전운전행동체크리스트는 37문항으로 개발되었고 이 연구 참여한 대상자는 60세 이상 노인 운전자 의명21의1 원데이터를 활용하였다. 안전운전행동 체크리스트(자가보고식 노인 안전운전 측정도구)의 타당도 검증을 위해 라쉬분석을 사용하였고 Winsteps 프로그램을 통해 문항 적합성, 문항의 난이도, 평정척도의적합성을 검증하였다. 결과 : 탐색적 요인분석 결과 운전상황, 외부환경과 날씨, 주차, 일반적 운전상황 4개 요인으로 추출되었다. 라쉬 분석을 통해 1문항이 삭제되어 36문항이 적절하다고 분석되었다. 또한 문항의 난이도를 파악할 수 있었는데 문항 난이도 분석결과 가장 어려운 문항은 “눈길이나 빙판길에서 운전하기’, 가장 쉬운 문항은 “신호등 지키기”로 문항이었다. 결론 : 본 연구에서는 라쉬분석과 요인분석을 이용하여 안전운전행동 체크리스트의 타당도를 입증하였고 운전요인으로 4가지 요인을 확인하였다. 또 최종 문항의 난이도를 파악할 수 있었다. 향후 국내 상황에 적합한 운전 수행능력을 검증할 수 있는 다양한 평가와 중재가 개발되어 작업치료 임상현장에서 운전재활 기초자료로 활용되기를 기대한다.

운전자의 인적요인분석을 위한 기초자료를 제공하는 디지털 운행기록계에서는 GPS 속도, 방위각, RPM정보 등 극히 제한된 운행정보만을 기록하여 실제 운전자의 운전행태를 분석하는 데는 많은 한계가 있다. 또한, 현재 상업화가 활발히 이루어지고 있는 차량용 블랙박스는 운전자의 운전행동보다는 차량에 대한 위험사항을 기록하고 있는 방식으로서 운전자의 실제 운전행태를 분석하기에는 많은 문제점을 보이고 있다. 따라서 기존의 교통안전관련 연구들을 살펴보면 인적요인분석에 필요한 운전자의 위험운전분석이 극히 제한적으로 이루어져 있는 현실이다. 이에 본 연구에서는 사업용자동차의 위험운전유형과 각 운전유형을 결정하는 임계치를 이용하여 버스운전자의 운전행태를 분석하였다. 또한, 버스운전자에게 위험운전에 대해 단말기를 통해 실시간으로 경고정보를 자동 제공하여 실시간경고정보에 따른 효과분석을 실시하였다. 운전자들의 행태분석에 대한 해석은 차량의 가속도 센서와 회전 각속도 센서의 종방향 가속도(Ax), 횡방향 가속도(Ay), 회전 각속도(Yaw rate) 등에 대한 분석을 통해 이루어졌다.

Using driving simulator, we analyzed the driving behavior of an older driver on intersection and measured the pychological load to HRV. As a results, older drivers started to enter the more complex intersection on a great distance and on low velocity for