PURPOSES : This study aims to perform a quantitative analysis of Forward Collision Warning and crash frequency using heavy vehicle driving data collected in expressway driving environments, and to classify the driving environments where Forward Collision Warnings of heavy vehicles occur for accident-prone areas and analyze their occurrence characteristics. METHODS : A bivariate Gaussian mixture model based on inter-vehicle distance gap and speed-acceleration parameters is used to classify the environment in which Forward Collision Warning occurs for heavy vehicles driving on expressways. For this analysis, Probe Vehicle Data of 80 large trucks collected by C-ITS devices of Korea Expressway Corporation from May to June 2022. Combined with accident information from the past five years, a detailed analysis of the classified driving environments is conducted. RESULTS : The results of the clustering analysis categorizes Forward Collision Warning environments into three groups: Group I (highdensity, high-speed), Group II (high-density, low-speed), and Group III (low-density, high-speed). It reveals a positive correlation between Forward Collision Warning frequency and accident rates at these points, with Group I prevailing. Road characteristics at sites with different accident incidences showed that on-ramps and toll gates had high occurrences of both accidents and warnings. Furthermore, acceleration deviation at high-accident sites was significant across all groups, with variable speed deviations noted for each warning group. CONCLUSIONS : The Forward Collision Warning of heavy vehicles on expressways is classified into three types depending on the driving environment, and the results of these environmental classifications can be used as a basis for building a road environment that reduces the risk of crashes for heavy vehicles.

PURPOSES: The density in uninterrupted traffic flow facilities plays an important role in representing the current status of traffic flow. For example, the density is used for the primary measures of effectiveness in the capacity analysis for freeway facilities. Therefore, the estimation of density has been a long and tough task for traffic engineers for a long time. This study was initiated to evaluate the performance of density values that were estimated using VDS data and two traditional methods, including a method using traffic flow theory and another method using occupancy by comparing the density values estimated using vehicular trajectory data generated from a radar detector. METHODS: In this study, a radar detector which can generate very accurate vehicular trajectory within the range of 250 m on the Joongbu expressway near to Dongseoul tollgate, where two VDS were already installed. The first task was to estimate densities using different data and methods. Thus, the density values were estimated using two traditional methods and the VDS data on the Joongbu expressway. The density values were compared with those estimated using the vehicular trajectory data in order to evaluate the quality of density estimation. Then, the relationship between the space mean speed and density were drawn using two sets of densities and speeds based on the VDS data and one set of those using the radar detector data. CONCLUSIONS: As a result, the three sets of density showed minor differences when the density values were under 20 vehicles per km per lane. However, as the density values become greater than 20 vehicles per km per lane, the three methods showed a significant difference among on another. The density using the vehicular trajectory data showed the lowest values in general. Based on the in-depth study, it was found out that the space mean speed plays a critical role in the calculation of density. The speed estimated from the VDS data was higher than that from the radar detector. In order to validate the difference in the speed data, the traffic flow models using the relationships between the space mean speed and the density were carefully examined in this study. Conclusively, the traffic flow models generated using the radar data seems to be more realistic.

고속도로 휴게소는 고속도로 이용자의 휴식을 위한 공간으로 현재 졸음쉼터, 간이 휴게소, 일반 휴게 소, 화물차 휴게소 등 다양한 유형으로 구분할 수 있다. 국내 휴게소의 경우 일부 휴게소에서 도로 이용 시 휴식을 제공하는 기본적인 기능에 국한되지 않고 즐길거리, 볼거리, 살거리 등을 제공하는 등의 다양 한 변화를 시도하고는 있다. 휴게소 분야의 선진사례인 일본의 경우 고속도로 휴게소와 연계시설인 하이웨이 오아시스, 휴게소의 기능과 지역커뮤니티의 기능을 보유한 국도 휴게소인 미치노에키 등이 활발히 운영 중에 있으며 일본의 다양성과 전문성, 규모를 벤치마킹해야 할 필요가 있다. 이러한 배경에서 본 연구는 본 연구에서는 국내 휴게소의 발전방안을 제시하고 향후 휴게소 발전방안 에 대한 의사결정시 참고할 수 있는 현실성 있는 세부모델을 제시하는 것을 목적으로 한다. 현황 및 사례 분석을 위하여 국내 테마휴게소에 대한 현장조사를 통해 휴게소 운영현황, 시설, 이용객 의 이용패턴 등을 인터뷰 및 현장조사하였고 휴게소 운영상의 문제점을 도출하였다. 또한, 일본 휴게소에 대한 사례조사를 통해 고속도로 정규휴게소, 간이휴게소, 하이웨이 오아시스와 국도 휴게소 미치노에키의 운영 특징 및 현황을 파악하였다. 일본 및 국내 휴게소의 비교분석을 통해 국내 휴게소가 가지는 한계점 과 발전방안 수립을 위한 시사점을 도출하였다. 이와 함께, 기존 휴게소 관련 선행연구를 검토하여 발전 방안 수립시 참고자료로 활용하였다. 현황 분석 및 시사점 도출을 통해 국내 휴게소 발전방안을 크게 ⅰ)테마 휴게소 강화 ⅱ)IT 기술을 적 용한 휴게소 서비스의 고도화 및 관리 과학화 ⅲ)휴게소 인프라를 통한 지역협력을 발전방안으로 제시 하였다. 첫 번째 발전방안인 테마휴게소의 강화방안을 위해 휴게소의 특성화를 추진하기 위한 개선모델인 애니 메이션 캐릭터 휴게소를 개선모델로 제시하였다. 두 번째 발전방안인 IT 기술을 적용한 휴게소 서비스의 고도화 및 관리 과학화 방안의 세부 모델로는 ICT, 빅데이터 기술을 적용한 스마트 휴게소의 도입 모델을 제시하였다. 마지막으로 휴게소 인프라를 통한 지역협력 발전방안으로는 휴게소 시설의 개방을 현실적인 대안으로 제시하였다.

PURPOSES : The aim of this study was to compare the performance of the QUEENSOD method and the gravity model in estimating Origin-Destination (O/D) tables for a large-sized microscopic traffic simulation network. METHODS : In this study, an expressway network was simulated using the microscopic traffic simulation model, VISSIM. The gravity model and QUEENSOD method were used to estimate the O/D pairs between internal and between external zones. RESULTS: After obtaining estimations of the O/D table by using both the gravity model and the QUEENSOD method, the value of the root mean square error (RMSE) for O/D pairs between internal zones were compared. For the gravity model and the QUEENSOD method, the RMSE obtained were 386.0 and 241.2, respectively. The O/D tables estimated using both methods were then entered into the VISSIM networks and calibrated with measured travel time. The resulting estimated travel times were then compared. For the gravity model and the QUEENSOD method, the estimated travel times showed 1.16% and 0.45% deviation from the surveyed travel time, respectively. CONCLUSIONS : In building a large-sized microscopic traffic simulation network, an O/D matrix is essential in order to produce reliable analysis results. When link counts from diverse ITS facilities are available, the QUEENSOD method outperforms the gravity model.

PURPOSES : To operate more efficient traffic management system, it is utmost important to detect the change in congestion level on a freeway segment rapidly and reliably. This study aims to develop classification method of congestion change type. METHODS: This research proposes two classification methods to capture the change of the congestion level on freeway segments using the dedicated short range communication (DSRC) data and the vehicle detection system (VDS) data. For developing the classification methods, the decision tree models were employed in which the independent variable is the change in congestion level and the covariates are the DSRC and VDS data collected from the freeway segments in Korea. RESULTS : The comparison results show that the decision tree model with DSRC data are better than the decision tree model with VDS data. Specifically, the decision tree model using DSRC data with better fits show approximately 95% accuracies. CONCLUSIONS : It is expected that the congestion change type classified using the decision tree models could play an important role in future freeway traffic management strategy.

본 연구에서는 차량검지기의 속도측정 성능평가방법을 개발하였다. 개발된 성능평가방법에서는 오차요인들을 기준속도에 반영하며 측정불확도의 개념을 적용하였다. 기존연구, 통계적 처리기법, 기존교통단속장비 및 차량검지시스템의 속도측정 성능평가방법 등에 대한 고찰을 통해 기존평가방법의 문제점을 도출하고 개선된 성능평가방법을 개발하였다, 실제 현장에 설치된 차량검지기에 대해서 기존평가방법과 개발방법을 적용해본 결과 기존평가방법은 평가기준에 적합하나 개발방법은 평가기준을 만족시키지 못하고 있다. 이러한 결과는 기존성능평가방법이 측정 시의 오차요인들을 충분히 고려하지 못해서 평가대상장비의 성능을 고평가할 가능성이 있음을 의미하며, 반면에 개발모형은 측정 시의 변동요인인 오차를 고려하므로 기존평가방법 보다 정확함을 나타낸다.