PURPOSES : The reliability of traffic volume estimates based on location intelligence data (LID) is evaluated using various statistical techniques. There are several methods for determining statistical significance or relationships between different database sets. We propose a method that best represents the statistical difference between actual LID-based traffic volume estimates and the VDS values (i.e., true values) for the same road segment. METHODS : A total of 2,496 datasets aggregated for 1-h LID and VDS data were subjected to various statistical analyses to evaluate the consistency of the two datasets. The VDS data were defined as the true values for comparison. Four different statistical techniques (procrutes, 2-sample t-test, paired-sample t-test, and model performance rating scale) were applied. RESULTS : In cases where there is a specific pattern (e.g., traffic volume distribution considering peak and off-peak times), distribution tests such as Procrustes or Kolmogorov-Smirnov are useful because not only the prediction accuracy but also the similarity of the data distribution shape is important. CONCLUSIONS : The findings of this study provide important insight into the reliability of LID-based traffic volume estimation. To evaluate the reliability between the two groups, a paired-sample t-test was considered more appropriate than the performance evaluation measure of the machine-learning model. However, it is important to set the acceptance criteria necessary to statistically determine whether the difference between the two groups in the paired-sample t-test varies according to the given problem.

도시의 확장 및 광역화로 지상에서의 물리적인 도로 공급 및 확대는 현실적으로 어려운 상황에 직면하였으며, 이를 해결하기 위한 정책의 일환으로 경부고속도로 지하화 사업으로 대표되는 고속도로 지하화 사업이 추진되고 있다. 지하공간을 이용한 도로의 입체적 확장은 새로운 도로 공급용지의 공간적 확대뿐 아니라 지상도로의 교통량 분산으로 교통정체 완화, 차량으로 인한 소음 및 대기오염 문제 완화 등 도로교통체계의 효율성 및 문제점을 개선할 것으로 판단된다. 그러나 현재까지는 지하도로의 설계 및 시공 관련 기술을 위주로 개발 및 적용되고 있어, 운영 및 안전관리에 필수적인 지하고속도로 교통류 관리에 관한 연구는 이론적 수준에 머물고 있는 한계가 상존한 상황에 있다. 이에 본 연구는 지하고속도로 안전 향상을 위한 사고 예방 및 대응 기술 개발의 전단계로 유고 상황에 대응하기 위한 교통관리 개념을 유고 상황에 따라 변화되는 혼잡 지속시간을 추정할 수 있는 교통류 진단과 지하고속도로 내 유고 발 생 후 교통류 혼잡 회복 및 상태 안전화로 나누어 제안하였다. 이를 기반으로 향후 추진될 기술 개발의 단계를 교통류 변동 지표 개 발, 교통류 진단 알고리즘 개발, 교통류 해석ㆍ추정 알고리즘 개발, 교통관리 현장 적용 및 검증의 4단계로 기술 개발 로드맵을 제시 하였다. 본 연구의 결과는 향후 추진되는 K-지하고속도로 안전 확보를 위한 사고 예방 및 대응 기술 개발에 대한 실효성 및 신뢰성을 높이는데 기여할 것으로 사료된다.

본 연구는 국내 육성 품종 ‘선플’과 ‘감황’ 과실 생산에 적합 한 수분수를 선발하기 위해 ‘SKK2’, ‘델리웅’, ‘보화’, ‘Chieftain’ 각각의 꽃가루로 인공수분하여 과실 품질과 종자의 발육을 조 사하였다. 착과율은 두 품종이 모든 처리구에서 96% 이상이 었다. ‘선플’은 ‘델리웅’ 처리에서 과중이 가장 높았고 2021년 은 ‘보화’ 처리에서 2022년은 ‘Chieftain’ 처리에서 가장 낮았 다. ‘감황’은 2021년에 ‘델리웅’ 처리에서 2022년은 ‘Chieftain’ 처리에서 과중이 가장 높아 연차 간 차이를 보였다. ‘선플’의 과실 품질 조사 결과, 2022년에 건물률은 ‘SKK2’처리에서 가장 높고 가용성 고형물 함량은 ‘Chieftain’에서 가장 높았으 며 2021년에는 처리 간 유의차가 없었다. ‘선플’의 총 종자 수 와 미숙 종자 수는 6배체 수분수 처리구에서 가장 많았고 천립 중은 4배체 수분수 처리구에서 무거웠다. ‘감황’의 과실 품질 조사 결과, 건물률과 산 함량은 2년 모두 ‘SKK2’ 처리에서 가 장 높았고 가용성 고형물 함량은 연차 간 차이를 나타냈다. ‘감 황’의 총 종자 수와 성숙 종자 수는 ‘델리웅’과 ‘Chieftain’ 처 리에서 가장 많았고 미숙 종자 수는 ‘SKK2’ 처리에서 가장 적 었다.

PURPOSES : We propose a framework to evaluate the reliability of integrating homogeneous or heterogeneous mobility data to produce the various data required for greenhouse gas emission estimation. METHODS : The mobility data used in the framework were collected at a fixed time from a specific point and were based on raster data. In general, the traffic volume for all traffic measurement points over 24 h can be considered raster data. In the future, the proposed framework can be applied to specific road points or road sections, depending on the presence or absence of raster data. RESULTS : The activity data required to calculate greenhouse gas emissions were derived from the mobility data analysis. With recent developments in information, communication, and artificial intelligence technologies, mobility data collected from different sources with the same collection purpose can be integrated to increase the reliability and accuracy of previously unknown or inaccurate information. CONCLUSIONS : This study will help assess the reliability of mobility data fusion as it is collected on the road, and will ultimately lead to more accurate estimates of greenhouse gas emissions.

PURPOSES : This study aimed to identify factors affecting the duration of traffic incidents in tunnel sections, as accidents in tunnels tend to cause more congestion than those on main roads. Survival analysis and a Cox proportional hazards model were used to analyze the determinants of incident clearance times. METHODS : Tunnel traffic accidents were categorized into tunnel access sections versus inner tunnel sections according to the point of occurrence. The factors affecting duration were compared between main road and tunnel locations. The Cox model was applied to quantify the effects of various factors on incident duration time by location. RESULTS : Key factors influencing mainline incident duration included collision type, driver behavior and gender, number of vehicles involved, number of accidents, and post-collision vehicle status. In tunnels, the primary factors identified were collision type, driver behavior, single vs multi-vehicle involvement, and vehicles stopping in the tunnel after collisions. Incidents lasted longest when vehicles stopped at tunnel entrances and exits. In addition, we hypothesize that incident duration in tunnels is longer than in main roads due to the reduced space for vehicle handling. CONCLUSIONS : These results can inform the development of future incident management strategies and congestion mitigation for tunnels and underpasses. The Cox model provided new insights into the determinants of incident duration times in constrained tunnel environments compared to open main roads.

본 연구에서는 중공사형 이산화탄소 분리막 모듈을 사용하여 수소개질기 배가스로부터 이산화탄소 포집을 목적 으로 한 분리막 공정 최적화 연구를 진행하였다. 랩스케일의 소형 분리막 모듈을 사용하여 혼합기체를 대상으로 이산화탄소 순도 90% 및 회수율 90%을 달성하는 2단 공정 조건을 도출하였다. 막 면적이 정해진 모듈의 분리막 공정에서는 스테이지-컷, 주입부 및 투과부 압력에 따라서 포집 순도 및 회수율이 모두 다르게 나타나기 때문에 운전 조건에 대한 최적화가 필수적이 다. 본 연구에서는 다양한 운전 조건에서 1단 분리막에서 보이는 공정 포집 효율의 한계를 확인하고, 높은 순도와 회수율을 동시에 달성하기 위한 2단 회수 공정을 최적화하였다.

This study examines the characteristics of berries from secondary bearing shoots of ‘Scintilla’ southern highbush blueberry, grown hydroponically in the Jinju, Gimhae, and Uiryeong regions of Gyeongnam Province. Typically, ‘Scintilla’ forms flower buds at the tips of regular bearing shoots during the previous season, yielding berries in the current season. However, under heated cultivation, we observed a proliferation of secondary bearing shoots that produce berries in the same growing season. Flowering and harvesting on secondary bearing shoots were delayed by 52 and 36 days, respectively, compared to regular bearing shoots. However, these shoots exhibited a 54% increase in diameter and a 10% increase in length. We found no significant difference in berry size and soluble solid content between the two types of shoots. Notably, berries from the secondary bearing shoots had higher potassium and lower calcium and magnesium concentrations. We conclude that berries from secondary bearing shoots could be marketable, provided the bushes are healthy. These findings provide valuable insights for optimizing cultural practices to improve the yield and quality of blueberries under specific environmental conditions.

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.

본 연구는 무대재배 복숭아 ‘미황’을 대상으로 성숙기간 중 RGB 영상을 취득한 후 다양한 품질 지표를 측정하고 이를 딥 러닝 기술에 적용하여 복숭아 과실 숙도 분류의 가능성을 탐 색하고자 실시하였다. 취득 영상 730개의 데이터를 training 과 validation에 사용하였고, 170개는 최종 테스트 이미지로 사용하였다. 본 연구에서는 딥러닝을 활용한 성숙도 자동 분 류를 위하여 조사된 품질 지표 중 경도, Hue 값, a*값을 최종 선 발하여 이미지를 수동으로 미성숙(immature), 성숙(mature), 과숙(over mature)으로 분류하였다. 이미지 자동 분류는 CNN (Convolutional Neural Networks, 컨볼루션 신경망) 모델 중 에서 이미지 분류 및 탐지에서 우수한 성능을 보이고 있는 VGG16, GoogLeNet의 InceptionV3 두 종류의 모델을 사용 하여 복숭아 품질 지표 값의 분류 이미지별 성능을 측정하였 다. 딥러닝을 통한 성숙도 이미지 분석 결과, VGG16과 InceptionV3 모델에서 Hue_left 특성이 각각 87.1%, 83.6% 의 성능(F1 기준)을 나타냈고, 그에 비해 Firmness 특성이 각각 72.2%, 76.9%를 나타냈고, Loss율이 각각 54.3%, 62.1% 로 Firmness를 기준으로 한 성숙도 분류는 적용성이 낮음을 확인하였다. 추후에 더 많은 종류의 이미지와 다양한 품질 지 표를 가지고 학습이 진행된다면 이전 연구보다 향상된 정확도 와 세밀한 성숙도 판별이 가능할 것으로 판단되었다.

PURPOSES : In this study, the factors affecting the severity of traffic accidents in highway tunnel sections were analyzed. The main lines of the highway and tunnel sections were compared, and factors affecting the severity of accidents were derived for each tunnel section, such as the tunnel access zone and tunnel inner zone. METHODS : An ordered probit model (OPM) was employed to estimate the factors affecting accident severity. The accident grade, which indicates the severity of highway traffic accidents, was set as the dependent variable. In addition, human, environmental, road condition, accident, and tunnel factors were collected and set as independent variables of the model. Marginal effects were examined to analyze how the derived influential factors affected the severity of each accident. RESULTS : As a result of the OPM analysis, accident factors were found to be influential in increasing the seriousness of the accident in all sections. Environmental factors, road conditions, and accident factors were identified as the main influential factors in the tunnel access zone. In contrast, accident and tunnel factors in the tunnel inner zone were found to be the influencing factors. In particular, it was found that serious accidents (A, B) occurred in all sections when a rollover accident occurred. CONCLUSIONS : This study confirmed that the influencing factors and the probability of accident occurrence differed between the tunnel access zone and inner zone. Most importantly, when the vehicle was overturned after the accident occurred, the results of the influencing factors were different. Therefore, the results can be used as a reference for establishing safety management strategies for tunnels or underground roads.

PURPOSES : It is necessary to implement traffic-control strategies for underground roads. In this study, the application criteria for traffic control were developed to minimize actual traffic congestion on underground roads before it occurs. In particular, the traffic congestion judgement criteria and procedure (TJCAP) were developed. They can specifically classify the possibility of traffic congestion underground. METHODS : A microscopic traffic simulation model was used to analyze different scenarios. With the scenario simulation results, a hierarchical clustering analysis was applied to produce quantitative values from the TJCAP for each experimental network case. RESULTS : For network case (a), it was concluded that the possibility of traffic congestion on underground roads increases when the speed of the ground road connected to the main underground road and the connected ground road after the outflow of the ramp section is low. When the connected road is an interrupted facility after entering the underground roads, the red time is long, and when the section travel speed is 15 km/h, the possibility of traffic congestion underground is highest. A cluster analysis based on these results was performed using two techniques (elbow and silhouette) to verify the final classification. CONCLUSIONS : The TJCAP were designed to operate traffic flow with stricter criteria than traffic congestion management on ground roads. This reflects the difference in the driving environment between underground and above-ground roadways.

PURPOSES : In this study, model-agnostic methods are applied for interpreting machine learning models, such as the feature global effect, the importance of a feature, the joint effects of features, and explaining individual predictions. METHODS : Model-agnostic global interpretation techniques, such as partial dependence plot (PDP), accumulated local effect (ALE), feature interaction (H-statistics), and permutation feature importance, were applied to describe the average behavior of a machine learning model. Moreover, local model-agnostic interpretation methods, individual conditional expectation curves (ICE), local surrogate models (LIME), and Shapley values were used to explain individual predictions. RESULTS : As global interpretations, PDP and ALE-Plot demonstrated the relationship between a feature and the prediction of a machine learning model, where the feature interaction estimated whether one feature depended on the other feature, and the permutation feature importance measured the importance of a feature. For local interpretations, ICE exhibited how changing a feature changes the interested instance’s prediction, LIME explained the relationship between a feature and the instance’s prediction by replacing the machine model with a locally interpretable model, and Shapley values presented how to fairly contribute to the instance’s prediction among the features. CONCLUSIONS : Model-agnostic methods contribute to understanding the general relationship between features and a prediction or debut a model from the global and/or local perspective, securing the reliability of the learning model.

PURPOSES : This study prioritizes the potential technology for establishing an efficient traffic control in the ramp junction of urban deep underground tunnels in the future. We considered most of the applicable technologies that ensure traffic safety at the on-off ramp junction. METHODS : This study proposes a methodology to prioritize the applicable technology for establishing efficient traffic control in the ramp junction of an urban deep underground tunnel using an analytical hierarchy process (AHP). First, an AHP structure was developed. Second, an individual survey was conducted to collect the opinions of road and transportation experts. Based on the survey results, weights were estimated depending on the relevant criteria of the developed structure. The estimated weights were verified using the consistency index (CI) and consistency ratio (CR). In addition, a sensitivity analysis was performed to confirm the reliability of the estimated weights. Finally, the potential technology for an efficient traffic control in the ramp junction of an urban deep underground tunnel was prioritized. RESULTS : In the first level of hierarchy, traffic demand control had the highest priority, and ramp metering, section speed control, and shoulder lane control were selected in the second level of hierarchy. CONCLUSIONS : These results implied that prioritizing would be useful in establishing traffic operation strategies for traffic safety when constructing and opening deep underground tunnels in urban areas in the future.

본 시험은 국내에 가장 많이 보급되어있는 북부하이부쉬 블 루베리 ‘듀크’ 품종에 대한 시설하우스 양액재배 가능성을 평가하기 위해 실시하였다. 블루베리 용기재배에 보편적으로 활용되는 피트모스(130L)와 펄라이트(40L) 배지를 180L 플라스틱 용기에 혼합하여 1년생 묘목을 심은 후 8년간 양액을 지속적으로 공급하는 양액처리구와 지하수만 공급하는 무처리구를 비교하였다. 양액은 NO3-N 4.6, NH4-N 3.4, PO4-P 3.3, K 3, Ca 4.6, Mg 2.2mmol-1를 EC 1.5로 조절하여 공급하였다. 양액처리구는 처리 후 8년차까지 수체 생장이 양호하였으며, 무처리구보다 주당 신초수가 18% 많고 주당 총신초장은 24%가 길었다. 양액처리구는 무처리구에 비해 뿌리 발달이 양호하였으며 주당 총건물중은 1.4배로 컸다. 식물체의 잎, 신초, 묵은 가지의 무기원소나 유기화합물이 양액처리구에서 대체로 높게 나타나 양액을 통한 양분흡수가 원활하였음을 확인되었다. 수량은 양액처리구에서 4년생 때부터 성과기에 달하여 시험이 종료될 때까지 높게 유지되었다. 이와 같은 결과 로 양액재배기술은 블루베리 재배에 유용하게 활용 가능할 것으로 평가되었다.