• Journal of Korean Society of Transportation
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• v.20 no.4
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• pp.177-185
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• 2002

본 논문의 목적은 링크통행시간 자료를 수집하는 시스템에서 소요 프로브차량대수에 영향을 주는 요소들을 규명하고. 최적의 소요 프로브차량대수를 결정하는 모형을 개발하는데 있다. 자가용승용차, 택시, 버스, 택배차량 등 여러 종류의 차량들이 프로브차량으로 사용될 수 있다. 그러나 일정한 정확도 이상의 교통정보를 수집하기 위해서 얼마나 많은 프로브차량이 필요한지에 대한 연구는 그다지 깊이 있게 이루어지지 않았다. 적정 소요 프로브차량대수는 링크통행시간 자료수집 기술 수집대상 링크의 공간적 범위, 프로브차량의 종류 및 운행 특성, 자료수집 시스템의 신뢰도, 수집되는 자료의 정확도 등에 영향을 받게 된다. 소요 프로브차량대수를 결정하는 링크당 평균 통행시간 자료수, 프로브차량 밀도의 최소 확률, 그리고 자료 미수집링크의 허용비율의 3가지 결정기준이 정의되었다. 또한 이러한 결정기준에 대해 소요 프로브차량대수를 산출하는 모형이 개발되었다. 일반적으로 주기당, 링크당 평균 필요 통행시간 자료수$(d_R)$, 단위길이당 프로브차량의 대수 또는 밀도$(n_{min} or {\alpha})$, 일정 프로브차량밀도 이상의 확률($\beta$), 그리고 자료 미수집링크의 비율($\gamma$)이 클수록 소요 프로브차량대수는 증가한다. 민간 교통정보회사의 통행시간 수집시스템에서 소요 프로브차량대수를 산정하는 사례연구가 수행되었으며, 여러가지 조건에서 소요 프로브차량대수가 산출되었다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.2
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• pp.133-141
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• 2021

The probe vehicle penetration rate is a required parameter in the estimation of entire volume, density, and queue length from probe vehicle data. The previous studies have proposed estimation methods without point detectors, which are based on probability structures for the locations of probe and non-probe vehicles; however, such methods are poorly suited to the case of multi-lane streets. Therefore, this study aimed to estimate the probe vehicle penetration rate at a multi-lane intersection and introduce a probability distribution of the queue length of each lane. Although a gap between estimates and observations was found, the estimates followed the trend of observations; the estimation could be improved by the correction factor hereafter. This study is expected to be used as a basic study for the estimation of entire volume, density, and queue length at multi-lane intersections without point detectors.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.2
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• pp.33-40
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• 2010

In this paper, we consider three macroscopic traffic flow monitoring indices, Travel Time Index(TTI), Acceleration Noise(AN) and Two Fluid(TF) and investigate how to determine a proper rate of probe cars for producing reliable values of these indices. For the analysis, we use classical sampling theories and provide numbers of probe rates using simulation data.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.183-193
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• 2019

This study developed a shockwave detection and prediction of their extinction point method based on continuous wavelet transform using trajectory data from probe vehicles equipped with automotive sensors.. To analyze the effectiveness of the proposed method, this paper proposed two measures which are a distance error between the extinction points of the predictor and an time-location error of the extinction points. The proposed concept was proved using the micro simulation based experiment with three exogenous variables of traffic volume, lane-close duration, market penetration of probe vehicles. The analysis results show that the proposed method is capable of detecting the traffic shockwaves as well as predicting their extinction point, and also that the accuracy of the proposed method is highly dependent on the rate of the probe vehicles.

• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.123-133
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• 2008

Information collection systems and applications in a ubiquitous environment has emerged as a leading issue in transportation and logistics. A productive application example is a traffic information collection system based on probe vehicles and wireless communication technology. Estimation of hourly OD pairs using probe OD data is a possible target. Since probe OD data consists of sample OD pairs, which vary over time and space, computation of sample rates of OD pairs and expansion of sample OD pairs into static OD pairs is required. In this paper, the authors proposed a method to estimate sample OD data with probe data in Jeju City and expand those into static OD data. Mean absolute percentage difference (MAPD) error between observed traffic volume and assigned traffic volume was about 22.9%. After removing abnormal data, MAPD error improved to 17.6%. Development of static OD estimation methods using probe vehicle data in a real environment is considered the main contribution of this paper.

• International Journal of Highway Engineering
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• v.13 no.2
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• pp.49-56
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• 2011

Understanding various casual factors affecting the occurrence of freeway traffic crash is a backbone of deriving effective countermeasures. The first step toward understanding such factors is to identify crash risks on freeways. Unlike existing studies, this study focused on the unsafe vehicle maneuvering that can be detected by in-vehicle sensors. The recent advancement of sensor technologies allows us to gather and analyze detailed microscopic events leading to crash occurrence such as the abrupt change in acceleration. This study used an accelerometer to capture the unsafe events. A set of candidate variables representing unsafe events were derived from analyzing acceleration data obtained by the accelerometer. Then, the crash risk was modeled by the binary logistic regression technique. The probabilistic outcome of crash risk can be provided by the proposed model. An application of the methodology assessing crash risk was presented, and further research items for the successful field implementation were also discussed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.6
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• pp.1851-1861
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• 2014

This study aims to calculate reliable sectional travel speeds with the consideration of the characteristics of the probe data collected in the interrupted traffic flow. First, in order to analysis the characteristics of the probe data, we looked into the distribution of the sectional travel times of each probe vehicle and compared the difference in the sectional travel speeds of each probe vehicle collected by DSRC. As a result, it is shown that outliers should be removed for the distribution of the sectional travel times. However, The comparison results show that the sectional travel speeds from the DSRC probe vehicles are not significantly different. Finally, based on the distribution characteristics of the sectional travel speeds of each probe vehicle and the representative values counted during a collection period, we drew the optimal outlier removal procedure and evaluated the estimation errors. The evaluation results showed that the DSRC sectional travel speeds were found to be similar to the observed values from actually running vehicles. On the contrary, in the case of the sectional travel speeds of intra-city bus, it was analyzed that they were less accurate than the DSRC sectional travel speeds. In the future, it will be necessary to improve BIS process and make use of the travel information on intra-city buses collected in real time to find various ways of applying it as traffic information.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.4 no.3 s.8
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• pp.23-31
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• 2005

It is essential that is the collection of more accurate data to provide reliable traffic information. Currently collection of traffic information which uses the taxi or the passenger car by the probe vehicle is low reliability. If it develops the model which estimates car travel-time by using bus travel-time, it means that the sheep or duality of information using the passenger car and the taxi by the probe vehicle than will improve. Consequently the research which develops to each situation in accordance withtraffic volume and bus whole aspect car execution yes or no and bus stand form.

• 한국ITS학회:학술대회논문집
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• v.2007 no.10
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• pp.297-302
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• 2007

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.173-184
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• 2018

As the portable traffic information collection system using probe vehicles spreads, it is becoming possible to collect road hazard information such as portholes, falling objects, and road surface freezing using in-vehicle sensors in addition to existing traffic information. In this study, we developed a integration and decision algorithm that integrates time and space in real time when multiple probe vehicles detect events such as road hazard information based on GPS coordinates. The core function of the algorithm is to determine whether the road hazard information generated at a specific point is the same point from the result of detecting multiple GPS probes with different GPS coordinates, Generating the data, (3) continuously determining whether the generated event data is valid, and (4) ending the event when the road hazard situation ends. For this purpose, the road risk information collected by the probe vehicle was processed in real time to achieve the conditional probability, and the validity of the event was verified by continuously updating the road risk information collected by the probe vehicle. It is considered that the developed hybrid processing algorithm can be applied to probe-based traffic information collection and event information processing such as C-ITS and autonomous driving car in the future.