• Journal of Korean Society of Transportation
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• v.36 no.1
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• pp.51-65
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• 2018

Intentional aggressive driving (IAD) is defined as a hazardous driving event that the aggressive driver intentionally threatens neighbor drivers with abrupt longitudinal and lateral maneuvering. This study developed a methodology for detecting IAD events based on the analysis of interactions between aggressive driver and normal driver. Three major aggressive events including rear-close following, side-close driving, and sudden deceleration were analyzed to develop the algorithm. Then, driving simulation experiments were conducted using a multi-agent driving simulator to obtain data to be used for the development of the detection algorithm. In order to detect the driver's intention to attack, a relative evaluation index (Erratic Driving Index, EDI) reflecting the driving pattern was derived. The derived IAD event detection algorithm utilizes both the existing absolute detection method and the relative detection method. It is expected that the proposed methodology can be effectively used for detecting IAD events in support of in-vehicle data recorder technology in practice.

• Journal of Korean Society of Transportation
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• v.27 no.2
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• pp.47-56
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• 2009

Effective detection and correction of outliers of raw traffic data collected from the field is of keen interest because reliable traffic information is highly dependent on the quality of raw data. Global positioning system (GPS) based traffic surveillance systems are capable of producing individual vehicle speeds that are invaluable for various traffic management and information strategies. This study proposed a locally weighted regression (LWR) based filtering method for individual vehicle speed data. An important feature of this study was to propose a technique to generate synthetic outliers for more systematic evaluation of the proposed method. It was identified by performance evaluations that the proposed LWR-based method outperformed an exponential smoothing. The proposed method is expected to be effectively utilized for filtering out raw individual vehicle speed data.

• Journal of Korean Society of Transportation
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• v.16 no.2
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• pp.53-65
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• 1998

합류구간에 설치된 차량검지기에서 수집된 조사자료들을 이용하여 본 연구는 교통와해 발생원인과 용량감소 문제를 분석하였다. 본 연구에서는 거시적 및 미시적인 방법을 가지고 수행하였으며 거시적인 방법으로는 연결로 교통량의 상대적 비중차이를 분석하였고 미시적으로는 본선에 진입하는 차량들의 패턴을 분석하였다. 거시적인 측면에서 합류부의 교통와해는 같은 합류교통량에 대한 연결로교통량의 비중 차이레 의해 발생된다. 즉, 연결로교통량 비중이 높아질수록 교통와해 발생확률은 증가할 것이다. 또한 미시적인 측면에서는 연결로 교통량이 같을 때 본선에 진입하는 차량군의 크기 및 발생회수, 표준편차가 높아진다고 할 수 있다. 따라서 교통와해의 주요한 원인은 연결로교통량의 영향에 기인한다고 할 수 있고 이러한 영향을 감소시키기 위하여 램프미터링과 같은 고속도로 교통통제 기법이 요구된다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.2
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• pp.48-56
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• 2012

One kind of crowdsourcing techniques which allow drivers to collect and provide traffic information using smartphones and applications is being introduced to ITS system as well. The introduction of crowdsourcing techniques requires changes in the existing ITS based on Insourcing which provide traffic information generated by detectors through VMS. ITS Information have had two problems, one is the high cost and the other is an interrupted service. Experts expect crowdsourcing technique which is created SNS, will overcome problems of ITS. But, there are not many examples and research results. Crowdsourcing technique was utilized in Jeju ATMS project to install ITS on the coastal round roads around Jeju since ITS to install point detectors turned out to be non-economic method in case of the coastal round roads with low traffic volume. However, there existed links in which traffic information cannot be generated as there were no smartphone users (crowds) even in the cost-effective crowdsourcing techniques, which indicates the fact that the crowdsourcing method is suitable for urban roads with many smartphone users, but not for local minor roads. On the contrary, insourcing-based ITS is considered to be non-economic method in applying to all roads in the city, but it can be effectively utilized in the local minor roads. Accordingly, Inter-sourcing based ITS operating system in which insourcing is connected with crowdsourcing was suggested in this study.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.3
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• pp.66-77
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• 2014

UTIS(Urban Traffic Information System) directly collects link travel time in urban area by using probe vehicles. Therefore it can estimate more accurate link travel speed compared to other traffic detection systems. However, UTIS includes some missing data caused by the lack of probe vehicles and RSEs on road network, system failures, and other factors. In this study, we suggest a new model, based on k-NN algorithm, for imputing missing data to provide more accurate travel time information. New imputation model is an adaptive k-NN which can flexibly adjust the number of nearest neighbors(NN) depending on the distribution of candidate objects. The evaluation result indicates that the new model successfully imputed missing speed data and significantly reduced the imputation error as compared with other models(ARIMA and etc). We have a plan to use the new imputation model improving traffic information service by applying UTIS Central Traffic Information Center.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.1
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• pp.66-76
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• 2016

Determination of type of a vehicle is being used in various areas such as collecting tolls, collecting statistical traffic data and traffic prognosis. Because most of the vehicle type classification systems depend on vehicle length indirectly or directly, highly reliable automatic vehicle length measurement system is crucial for them. This study makes use of a pencil beam laser rangemeter and devises a mechanical device which rotates the laser rangemeter. The implemented system measures the range between a point and the laser rangemeter then indicates it as a spherical coordinate. We obtain several silhouettes of cross section of the vehicle, the rate of change of the silhouettes, signs of the rates then squares the rates to apply cell averaging constant false alarm rate (CA-CFAR) technique to find out where the border is between the vehicle and the background. Using the border and trigonometry, we calculated the length of the vehicle and confirmed that the calculated vehicle length is about 94% of actual length.