• Journal of Korea Spatial Information System Society
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• v.10 no.3
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• pp.55-65
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• 2008

For obtaining telematics traffic information(travel time or speed in an individual link), there are many kinds of devices to collect traffic data. Since the GPS satellite signals have been released to civil society, thank to the development of GPS technology, the GPS has become a very useful instrument for collecting traffic data. GPS can reduce the cost of installation and maintenance in contrast with existing traffic detectors which must be stationed on the ground. But. there are Problems when GPS data is applied to the existing filtering techniques used for analyzing the data collected by other detectors. This paper proposes a method to provide users with correct traffic information through filtering abnormal data caused by the unusual driving in collected data based on GPS. We have developed an algorithm that can be applied to real-time GPS data and create more reliable traffic information, by building patterns of past data and filtering abnormal data through selection of filtering areas using Quartile values. in order to verify the proposed algorithm, we experimented with actual traffic data that include probe cars equipped with a built-in GPS receiver which ran through Gangnam Street in Seoul. As a result of these experiments, it is shown that link travel speed data obtained from this algorithm is more accurate than those obtained by existing systems.

• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.73.1-73.1
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• 2010

For the successful cold starting of a fuel cell engine, either internal of external heat supply must be made to overcome the formation of ice from water below the freezing point of water. In the present study, switchable vanadium oxide compounds as variable temperature-electrical resistance materials onto the surface of flat metallic bipolar plates have been prepared by a dip-coating technique via an aqueous sol-gel method. Subsequently, the chemical composition and micro-structure of the polycrystalline solid thin films were analyzed by X-ray diffraction, X-ray fluorescence spectroscopy, and field emission scanning electron microscopy. In addition, it was carefully measured electrical resistance hysteresis loop over a temperature range from $-20^{\circ}C$ to $80^{\circ}C$ using the four-point probe method. The experimental results revealed that the thin films was mainly composed of Karelianite $V_2O_3$ which acts as negative temperature coefficient materials. Also, it was found that thermal dissipation rate of the vanadium oxide thin films partially satisfy about 50% saving of the substantial amount of energy required for ice melting at $-20^{\circ}C$. Moreover, electrical resistances of the vanadium-based materials converge on an extremely small value similar to that of pure flat metallic bipolar plates at higher temperature, i.e. $T{\geq}40^{\circ}C$. As a consequence, experimental studies proved that it is possible to apply the variable temperature-electrical resistance material based on vanadium oxides for the cold starting enhancement of a fuel cell vehicle and minimize parasitic power loss and eliminate any necessity for external equipment for heat supply in freezing conditions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.7
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• pp.1325-1334
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• 2016

The video incident detection System is a probe for rapid detecting the walker, falling, stopped, backwards, smoke situation in tunnel. Recently, the importance is increases from the downtown double deck tunnel in great depth underground space[1], but the legal basis is weak and the vulnerable situation experimental data. So, In this paper, we introduce a long-term log data analysis information in the tunnenl video incident detection system installed and experimental results in order to verify the feasibility of apply to video incident detection system for the double deck tunnel. It is proposed a few things about derives the problem of existing video incident detection system, improvements and reflect attributes for double deck tunnel. The contents described in this paper will contribute to refine the prototype of video incident detection system will apply to future double deck multi-layer tunnels.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.169-175
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• 2004

This paper develops a dynamic travel time prediction model for ATIS in a national highway. While there have been many research on travel time prediction, none of them is for national highway in Korea. The study uses AVI data installed on the national highway No.1 with 10km interval for travel time prediction model, and probe vehicle data for evaluating the model. The study area has many access points, so there are many outlying observations in the raw AVI data. Therefore, this study uses the algorithm proposed by the author for removing the outliers, and then Kalman filtering algorithm is applied for the travel time prediction. The prediction model is performed for 5, 10, 15 and 30 minute-aggregating interval and the results are $0.061{\sim}0.066$ for 5, 10 and 15 interval and 0.078 for 30 minute one with a little low performance as MAREs.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.4
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• pp.156-161
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• 2015

In this paper, a dual-polarized multi-band multi-layer antenna for a vehicle, which operates in the GPS, bluetooth, and DSRC bands, was implemented. The antenna was designed as a multi-layer structure, and a FR4-epoxy substrate with =4.4 and =1.6mm was used. GPS and DSRC antennas have circular polarized characteristics, and a single probe feeding method was applied. Simulated results by Ansys HFSS v11 was compared with the measured ones. The size of the optimally designed antenna is $67mm{\times}67mm{\times}4.8mm$, -10dB bandwidth of the anatenna was measured to be 820MHz, 127MHz, and 862MHz in each band, and 3dB AR bandwidth of the antenna was simulated to be 19MHz and 110MHz in GPS and DSRC bands. The results confirmed that suggested system satisfies the system requirements.

• Journal of Korean Society of Transportation
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• v.24 no.2 s.88
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• pp.19-30
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• 2006

This study deals with the optimized detector location considering the traffic characteristics in National Highway. Although there ave many construction works for ITS in National Highway, there is not specific criteria for detector location which can effect the accuracy of traffic information. This study. therefore. aims to Provide the optimized detector location criteria which can represent the traffic characteristics of National Highway. It collects traffic factors of study area by GPS Probe-car and defector, and Presents the optimized detector location by the correlation analysis between spot-speed and link-travel-time. The main results of this study are as followings ; First, the correlation between the spot-speed and link-travel-time Presents the opposite bell shape of the graph (U-type owe) which is increased it?on the upstream then, declined through some unspecified Point of the link. Second, the optimized detector location usually distributes around midstream of link, even though it does not have a consistency. Third, therefore, the optimized detector location generally should be located between $55{\sim}60%$ of total link length. Forth. high level of vertical slope is one of the most important factors of detector location, so it should be excluded for determination of optimized detector location. Finally, expecting that the results of this study would improve the accuracy of travel time estimation and forecasting.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.121-132
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• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.