• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.6
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• pp.63-73
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• 2011

Travel speed is an important parameter for measuring road traffic. UTIS(Urban Traffic Information System) was developed as a mobile detector for measuring link travel speeds in South Korea. After investigation, we founded that UTIS includes some missing data caused by the lack of probe vehicles on road segments, system failures and etc. Imputation is the practice of filling in missing data with estimated values. In this paper, we suggests a new model for imputing missing data to provide accurate link travel speeds to the public. In the field test, new model showed the travel speed measuring accuracy of 93.6%. Therefore, it can be concluded that the proposed model significantly improves travel speed measuring accuracy.

• Information and Communications Magazine
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• v.21 no.5
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• pp.70-79
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• 2004

최적 경로 서비스를 제공하기 위해서는 구간 통행속도, 구간 통행시간, 회전 정보, 혼잡도 등과 같은 교통정보가 필요하다. 또한, 고객에게 신뢰성 있는 최적 경로를 제공하기 위해서는 실시간 교통정보 수집은 반드시 필요하며, 이러한 실시간 교통정보 수집 방법들에 대한 고찰과 검토가 선행되어야 한다. 기존의 교통정보 수집방법을 살펴보면 지점검지 방식의 경우, 수집되는 정보가 검지기 설치 지점의 지점속도(Spot Speed)이므로 해당 링크를 주행한 통행속도(통행시간)의 대표값으로 채택하기에는 다소 무리가 있으며 구간검지 방식의 경우, 일반적으로 급격한 교통류 변동에 따른 대기행렬 검지가 늦다는 단점이 있다.(중략)

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1D
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• pp.43-50
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• 2011

The monitoring system for link travel speed using taxi probe is one of key sub-systems of ITS. Link travel speed collected by taxi probe has been widely employed for both monitoring the traffic states of urban road network and providing real-time travel time information. When sample size of taxi probe is small and link travel time is longer than a length of time interval to collect travel speed data, and in turn the missing state is inevitable. Under this missing state, link travel speed data is real-timely not collected. This missing state changes from single to multiple time intervals. Existing single interval prediction techniques can not generate multiple future states. For this reason, it is necessary to replace multiple missing states with the estimations generated by multi-interval prediction method. In this study, a multi-interval prediction method to generate the speed estimations of single and multiple future time step is introduced overcoming the shortcomings of short-term techniques. The model is developed based on Non-Parametric Regression (NPR), and outperformed single-interval prediction methods in terms of prediction accuracy in spite of multi-interval prediction scheme.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.1021-1024
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• 2009

현재 사용되고 있는 통행시간 분류방법은 하나의 통행시간을 대푯값으로 가지고 있다. 이에 문제점은 고속도로 특성으로 규정 속도 이상의 속도로 주행하는 차량, 규정 속도 및 휴게소 이용차량, 운전자의 운전 습성, 통행 목적, 피로의 정도, 운전자 성향과 도로상황에 따라 통행시간이 다르게 나타나는 점이다. TCS(Toll Collection System) 자료는 고속도로의 다양한 특성이 포함되어 있으며, 대상 구간의 거리가 멀수록 목적지에 도달하는 통행시간의 분산이 커지는 특성 또한 보인다. 따라서 이를 처리하기 위한 효율적인 통행시간 분류, 구간대표통행시간 추출 알고리즘이 필요하다. 기존의 방법은 전체 통행차량의 통행시간을 감안한 방법으로 통행시간 예측시 정확성이 저하된다. 본 연구에서는 TCS 자료를 Fuzzy c-means 알고리즘을 이용하여 일일 고속도로 통행시간의 시간별 주행특성을 고려한 대푯 값을 추출하는 알고리즘을 제안하였다. 실제 서울-청주 구간을 운행한 TCS 자료를 가지고 실시한 실험으로, 주행특성 및 도로상황을 고려한 Fuzzy c-means를 이용한 통행시간 분류방법과 기존의 통행시간 분류 방법을 통한 통행시간을 PIFAB를 사용 TCS 자료의 실제 통행시간과 경로통행시간을 비교 평가하였다. 평가한 결과 본 연구에서 제안하는 Fuzzy c-means기법은 기존 방법인 MAD기법보다 75%, 신뢰구간(95%) 추출법 대비 81%의 정확성을 제고하였다.

• Journal of Industrial Convergence
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• v.18 no.3
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• pp.53-61
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• 2020

Travel speed is an important parameter for measuring road traffic and incident detection system. In this paper I suggests a model developed for estimating reliable and accurate average roadway link travel speeds using image processing sensor. This method extracts the vehicles from the video image from CCTV, tracks the moving vehicles using deep neural network, and extracts traffic information such as link travel speeds and volume. The algorithm estimates link travel speeds using a robust data-fusion procedure to provide accurate link travel speeds and traffic information to the public. In the field tests, the new model performed better than existing methods.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.6
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• pp.33-42
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• 2010

Travel speed is an important parameter in measurement of road traffic. UTIS(Urban Traffic Information System) was developed as a type of section detector. However, UTIS incur errors caused by irregular vehicle trajectories, wireless communication range and so on. This paper suggests a new model that use an error-filtering algorithm to improve the accuracy of travel speed measurements. In the field test, the variance of the percent errors measured by the new model was reduced. Therefore, it can be concluded that the proposed model significantly improves travel speed measuring accuracy.

• 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.11 no.6
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• pp.40-48
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• 2012

In this paper, we suggests methods for determining optimal representative value and the optimal size of historical data for reliable travel speed prediction. To evaluate the performance of the proposed method in real world environments, we did field tests at four roadway links in Seoul on Tuesday and Sunday. According to the results of applying the methods to historical data of Central Traffic Information Center, the optimal representative value were analyzed to be average and weighted average. Second, it was analyzed that 2 months data is the optimal size of historical data used for travel speed prediction.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.949-960
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• 2016

This paper uses all sections of highway data (VDS) for two years (Jan. 2014-Dec. 2015), with 15 minute units. The first purpose of this study is to find clusters with similar patterns that appear repeatedly with time variables of month, week and hour. The cluster analysis results indicate a variety of patterns of average traffic speeds by time variables depending on the clusters; subsequently, these can be utilized to model for the forecast of the speed at a specific time. The second purpose is to do cluster analysis for grouping sections by effect nets that are closely related to each other. For the similarity measure we use cross-correlation functions calculated after pre-whitening the speed of each section. The cluster analysis gets 19 clusters, and sections within a cluster are geographically close. These results are expected to help to forecast a real-time speed.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.10a
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• pp.177-181
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• 2008

본 논문은 제주 택시 텔레매틱스 시스템의 운영과정에서 축적되고 있는 각 택시들의 이동이력 데이터를 기반으로 관심구간의 통행속도에 관련된 필드들을 효율적으로 추출하는 기법을 설계하고 구현한다. 구현된 인터페이스는 도로네트워크 상에서 관심구간의 양끝점을 입력받아 $A^*$ 알고리즘을 수행하여 경로상에 포함된 각 링크를 결정한 후 해당 링크 아이디를 포함하는 질의문의 스켈리튼을 생성한다. 이 질의문을 수정하여 관심구간의 속도 레코드수, 속도 평균, 승객탑승시의 속도, 요일별 시간대별 평균 속도 등 다양한 정보를 체계적으로 검색할 수 있다. 제주시 연삼로 구간에 대한 시험적 검색 결과는 승객이 탑승한 경우 전체 경우 보다 $30{\sim}50%$ 정도의 보고수, $2{\sim}4$ kmh 빠른 통행 속도 등을 보이고 있으며 시간대별 통계는 요일별 통행속도 패턴의 변화를 정량화하고 있다.