• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.7-14
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• 2018

The purpose of this study is to analyze the effect of commuter's departure time on transportation system in future traffic system operated autonomous vehicle using agent based model. Various scenarios have been set up, such as when all passenger choose a similar departure time, or if the passenger chooses a different departure time. Also, this study tried to analyze the effect of road capacity. It was found that although many of the scenarios had been completed in a stable manner, many commuters were significantly coordinated at the desired departure time. In particular, in the case of a reduction in road capacity or in certain scenarios, it has been shown that, despite excessive schedule adjustments, many passengers are unable to commute before 9 o'clock. As a result, it is suggested that traffic management and pricing policies are different from current ones in the era of autonomous car operation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.70-85
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• 2021

Deep learning methodology, which has been actively studied in recent years, has improved the performance of artificial intelligence. Accordingly, systems utilizing deep learning have been proposed in various industries. In traffic systems, spatio-temporal graph modeling using GNN was found to be effective in predicting traffic speed. Still, it has a disadvantage that the model is trained inefficiently due to the memory bottleneck. Therefore, in this study, the road network is clustered through the graph clustering algorithm to reduce memory bottlenecks and simultaneously achieve superior performance. In order to verify the proposed method, the similarity of road speed distribution was measured using Jensen-Shannon divergence based on the analysis result of Incheon UTIC data. Then, the road network was clustered by spectrum clustering based on the measured similarity. As a result of the experiments, it was found that when the road network was divided into seven networks, the memory bottleneck was alleviated while recording the best performance compared to the baselines with MAE of 5.52km/h.

• Journal of Navigation and Port Research
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• v.34 no.2
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• pp.137-144
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• 2010

In light of the growing traffic congestion problem and congestion cost, the container transport by railway has to be increased. The freight transport by railway can have decided advantages over trucks in terms of energy efficiency, emissions and cost for certain freight movements, just as transportation in the metropolitan region can have great advantages over driving truck. But the freight transport by truck should gain significant mobility benefits from a freight railway system. Thus, the DMT(Dual Mode Trailer) transport system which is coupled railway transport advantages with load transport advantages has been developed and used in the european countries. The DMT transport will therefore serve the areas required by transport organizers. The purpose of this paper is to estimate economical feasibility analysis for development of DMT transport system. Consequently, this study analyzed the characteristics of the DMT system. The horizontal load.unload system is being considered as an adoptable DMT system in consideration of the situation in Korea.

• International Journal of Highway Engineering
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• v.17 no.2
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• pp.123-134
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• 2015

PURPOSES : The key point of a multimodal LOS (level-of-service) evaluation system is that all of the modes are mutually associated to determine each mode's LOS. For example, the LOS of the bicycle mode is measured based on not only bicycle volumes, but also automobile volumes. However, the Korea Highway Capacity Manual (KHCM) still focuses on the automobile mode in evaluating the LOS of the roads. Additionally, the KHCM's LOS of the other modes, except for the automobile, is not consistent with actual road conditions. The KHCM, therefore, needs to develop and introduce a multimodal LOS system in order to evaluate the service conditions more accurately. METHODS: As a preliminary step to the introduction of multimodal LOS research, in this study the current problem of the KHCM's LOS system through a close review and comparison with other HCMs (highway capacity manuals) was identified. Secondly, a field survey and investigation of the urban streets to apply the HCM's multimodal LOS system was conducted. Finally, a comparison analysis of the results of the HCM and KHCM LOS was performed. RESULTS: In the study, it was found that the results of the LOS for the automobile mode did not show a significant difference between the HCM and KHCM. However, the LOS of the bicycle and pedestrian mode tended to be worse in the multimodal LOS system, which results from considering the effects of the automobile mode. Moreover, it was found that many cases have the potential to improve the overall LOS conditions, while reducing the automobile capacity. CONCLUSIONS: With the introduction of the multimodal LOS system, road diet and complete streets can be easily applied to ans actual road improvement project. Ultimately, the multimodal LOS system should be introduced into the KHCM, which can then be applied to traffic impact studies and other road improvement projects for more accurate evaluations.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.04a
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• pp.421-426
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• 2004

Increasing of the multinational corporation brought into the international multimodal Increasing of the multinational corporation brought into the international multimodal transport on the logistics environment. In case of Europe which have a great infrastructure, they are tried to develope a second of the silk road constantly. This paper emphasized the importance of international multimodal transport and proposed the model for northeast multimodal transport. For this research, we analyzed the multimodal transport system in Europe and north corridor of TAR. We are expecting economic effect of the route is including republic of korea and developed a model for connecting with sea, air and road. Actually, this research can not be enough data of numerical value for proving this effectiveness. but we developed and proposed a specific route of multimodal transport that was never suggested. Consequently, we established basic ground for comparing each transport route in the future research.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.104-113
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• 2019

The road management institute needs lots of cost to repair road surface damage. These damages are inevitable due to natural factors and aging, but maintenance technologies for efficient repair of the broken road are needed. Various technologies have been developed and applied to cope with such a demand. Recently, maintenance technology for road surface damage repair is being developed using image information collected in the form of a black box installed in a vehicle. There are various methods to extract the damaged region, however, we will discuss the image recognition technology of the deep neural network structure that is actively studied recently. In this paper, we introduce a new neural network which can estimate the road damage and its location in the image by region-based convolution neural network algorithm. In order to develop the algorithm, about 600 images were collected through actual driving. Then, learning was carried out and compared with the existing model, we developed a neural network with 10.67% accuracy.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.4
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• pp.55-66
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• 2020

This study developed four algorithms and their associated indices that can quantify and qualify road hazards along roadways. Initially, relevant raw data can be collected from commercial vehicles by camera and DTG. Well-processed data, such as potholes, road freezing, and fog, can be generated from the Integrated management system. Road hazard algorithms combine these data with road inventory data in the Data Sharing Platform. Depending on well-processed data, four different road hazard algorithms and their associated indices were developed. To test the algorithms, an experimental plan based on passive DTG attached in probe vehicles was performed at two different test locations. Selection of the test routes was based on historical data. Although there were limitations using random data for commercial vehicles, hazardous roadways sections, such as fog, road freezing, and potholes, were generated based on actual historical data. As a result, no algorithm error was found in the entire test. Because this study provides road hazard information according to a section, not a point, it can be practically helpful to road users as well as road agencies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1500-1505
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• 2019

Currently, as part of the ICT Smart City, the company is building C-ITS(Cooperative-Intelligent Transport Systems) for solving urban traffic problems. In order to realize autonomous driving service with C-ITS, the role of advanced road infrastructure is important. In addition to the study of mid- to long-term C-ITS and autonomous driving services, it is necessary to present more realistic solutions for road traffic safety in the short term. Therefore, in this paper, we propose a highway accident detection alarm system that can detect and analyze traffic flow and risk information, which are essential information of C-ITS, based on IoT guard rail and provide immediate alarm and remote control. Intelligent IoT guard rail is expected to be used as an intelligent advanced road infrastructure that provides data at actual road sites that are required by C-ITS and self-driving services in the long term.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.155-163
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• 2019

This paper proposes a new methodology to recognize cracks on asphalt road surfaces using the image data obtained with drones. The target section was Yuseong-daero, the main highway of Daejeon. Furthermore, two object detection algorithms, such as Tiny-YOLO-V2 and Faster-RCNN, were used to recognize cracks on road surfaces, classify the crack types, and compare the experimental results. As a result, mean average precision of Faster-RCNN and Tiny-YOLO-V2 was 71% and 33%, respectively. The Faster-RCNN algorithm, 2Stage Detection, showed better performance in identifying and separating road surface cracks than the Yolo algorithm, 1Stage Detection. In the future, it will be possible to prepare a plan for building an infrastructure asset-management system using drones and AI crack detection systems. An efficient and economical road-maintenance decision-support system will be established and an operating environment will be produced.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.59-74
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• 2021

The domestic road space is reaching the limit of planar space distribution, and Increasingly, the importance of three-dimensional space distribution through the development of underground space. therefore, In this study, a study was conducted on a traffic control method that can safely induce two different traffic flows in the connection between the ground road and the underground road. Through VISSIM, we calculated the appropriate amount of outflow and inflow traffic compared to the capacity of the main line when there is a Merge/Diverge section in the underground road. and Through the analysis of the number of conflicts, the appropriate traffic control level for safety in the underground, A basic study was conducted on the level of risk in the underpass according to the level of delay in the ground part through the analysis of the delay scenario of the ground road.