• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.3
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• pp.53-64
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• 2013

The car-following model is one of core models in Advanced Vehicle & Highway Systems (AVHS). The car-following model has been developed in aspects such as human factor and reduction error rates. However, the consideration of safety depending on weather condition has not been completed yet. In this paper, therefore, changes of driving condition for car-following due to different road condition were dealt with, and optimal safety distance corresponding to road condition such as dry, wet and snowy were computed. The GMIT(GM Model with Instantaneous T) model was picked over for simulation of adaptive cruise control applied the suggested optimal safety distance. As the results, the 1.7 times longer safety distance was required for wet road condition than dry road condition, and the 5.6 times longer safety distance was required for snowy road condition.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.4
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• pp.48-56
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• 2014

Even though vehicle types, gradient, pavement conditions and types of pavement should be considered for estimating fuel consumption, existing models were developed as a function of vehicle types and vehicle speed. Therefore in this study, the model of fuel consumption was developed using field test data in order that effect analysis on the passenger vehicle fuel consumption by road gradient. At first, fuel consumption was measured in second-based, using GPS device and fuel consumption measurement device for development of fuel consumption model considered road gradient. The road gradient was classified as flatland, up-hill and down-hill. Development of model was using by regression model which vehicle speed(km/h) and fuel consumption(${\ell}/km$). The on-road test proved that fuel consumption of passenger vehicle is affected by road gradient.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.5
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• pp.1-12
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• 2013

Road's accidents caused by Ice, snow, Wet of roads surface conditions and weather conditions situations that are constantly occurring. That is, driver's negligence and safe driving ability of individuals due to lack of awareness, and Road management main agent(the government and the public, etc.) due to road conditions, if there is insufficient information. So Related research needs is a trend that is required. In this study, gather Camera(Stereo camera)'s image data, and analysis polarization coefficients and wavelet transform. And unlike traditional single-dimensional classification algorithms as multi-dimensional analysis by using SVM classification techniques, develop an algorithm to determine road conditions. Four on the road conditions (dry, wet, snow, ice) recognition success rate for the detection and analysis of experiments.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.4
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• pp.38-47
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• 2014

Ministry of Land, Infrastructure and Transport (MOLIT) Affairs of Korean government improved RSMS to be linked with GIS data and now we have research foundation. In case of expressway and rural national highway, there is a referenced place-name for direction road sign, but there is no reference in urban road and is only a guideline. Direction signs in urban could not have consistent place-name and it is vary difficult to select the proper place-name. Based on the change of analysis environment and perception of road sign - related problem, This study is aimed to suggest how to deduce the spatial impact zone of place-name from DB and GIS in RSMS of Changwon City. The results indicated that there is a spatial difference between place-names according to whether is near or far on the road sign. It is expected that this method would be effectively used in case of new road sign and so the process to select the place-name would be simple.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.244-257
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• 2017

Traffic congestion occurs from drivers' human factors such as driver reaction time, reckless lane change, and inexperienced driving. When Automated Vehicles are introduced, human factors are excluded, resulting in increased average vehicle speed, stabilizing traffic flow, and increasing road capacity. This study analyzed traffic flow changes through traffic volume-speed-density plots, and increased road capacity due to Automated Vehicles. As a result of the analysis, when rate of automated vehicles gests higher, the traffic flow became stable. Additionally, it was analyzed that when all vehicles were automated, the road capacity increased by about 120 %. It is expected that there will be a positive expectation in terms of traffic congestion and traffic demand management due to the introduction of Automated Vehicles.

• 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 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.

• Journal of the Korean Society for Railway
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• v.16 no.5
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• pp.418-429
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• 2013

This study assesses the appropriate amounts and applicable ranges of travel time values for freight transport modes in Korea. The findings suggest that it is possible to determine appropriate sizes and applicable ranges of travel time values of goods set for road transport mode (i.e. truck), whereas those for railway, inland waterway, marine, and air transport modes are not as easily estimated. The adequate travel time value for trucks is 20,000won/vehicle hour, and the application range of travel time value of trucks is 17,000~23,000won/vehicle hour considering other variables. The most adequate estimation method of determining the travel time value for road transport is the wage rate method, whereas the marginal rate of substitution method is more rational for railway, inland waterway, marine, and air transport modes considering the application purpose and characteristics of the goods transported.

• 도로교통
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• s.104
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• pp.52-65
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• 2006

• 한국ITS학회:학술대회논문집
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• v.2006 no.10
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• pp.123-128
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• 2006