• Journal of ILASS-Korea
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• v.25 no.1
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• pp.27-33
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• 2020

NOx, PN and CO emissions from diesel trucks make up a significant portion of domestic air pollutant emissions. Therefore, test vehicles with various emission standards and driving modes were selected to evaluate the emission characteristics of regulated pollutants (NOx, PN, CO) in medium-duty trucks. As a result of test, all test vehicles were satisfied with Euro 5 or 6 regulation. NOx emissions of Euro 6 vehicles with after-treatment of LNT + DPF were lower than those of Euro 5 vehicles with DPF. In WLTC mode, all vehicles have high NOx emissions at section of extra high speeds, which are determined by increased fuel consumption and high combustion temperatures. CO and PN emissions from all vehicles were found to be low at section of low speeds. Also, The NO₂/NOx ratio was analyzed at 7-23% in each mode, and the NO₂/NOx ratio increased as the average vehicle speed increased. In NIER 9 mode, the CO, HC, and PN emissions were higher under cold conditions of engine than hot conditions of engine. In addition, vehicles with after-treatment system of LNT have similar NOx emissions level in this study.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.1
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• pp.136-146
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• 2013

Truck demand analysis based on TCS data has limitation in that TCS data can not provide truck O/D data for each type of truck vehicle. This study conducted video survey for classifying truck vehicle types. By using TCS data and vehicle ratio by region/cities type, truck O/D data on expressway were estimated. It was found that average travel distances of small truck, medium truck and large truck were 52km/veh, 56km/veh and 97km/veh, respectively by analysing truck O/D data estimated in this study. The reliability analysis showed that check points where error rate is lower than 30% comprise of 87.3%. It is considered that estimated O/D data by truck vehicle types would be useful for the analysis of truck demand of expressway.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.562-565
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. However, this irregular weight distribution is not possible to be recognized with the current weight measurement system for vehicles on roads. To address this limitation, we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles from the CCTV, black box, and hand-held camera point of view. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data. From the result, we believe that public big data can be utilized more efficiently and applied to the development of an object detection-based overloaded vehicle detection model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.469-472
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• 2022

With the recent development of intelligent transportation systems, various technologies applying deep learning technology are being used. To crackdown on illegal vehicles and criminal vehicles driving on the road, a vehicle type classification system capable of accurately determining the type of vehicle is required. This study proposes a vehicle type classification system optimized for mobile traffic control systems using YOLO(You Only Look Once). The system uses a one-stage object detection algorithm YOLOv5 to detect vehicles into six classes: passenger cars, subcompact, compact, and midsize vans, full-size vans, trucks, motorcycles, special vehicles, and construction machinery. About 5,000 pieces of domestic vehicle image data built by the Korea Institute of Science and Technology for the development of artificial intelligence technology were used as learning data. It proposes a lane designation control system that applies a vehicle type classification algorithm capable of recognizing both front and side angles with one camera.