• The KIPS Transactions:PartD
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• v.17D no.2
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• pp.87-102
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• 2010

A vast amount of traffic data is produced every day from detection devices but this data includes a considerable amount of errors and missing values. Moreover, this information is periodically deleted before it could be used as important analysis information. Therefore, this paper discusses the implementation of an integrated traffic history database system that continuously stores the traffic data as a multidimensional model and increases the validity and completeness of the data via a flow of processing steps, and provides a what-if analysis function. The implemented system provides various techniques to correct errors and missing data patterns, and a what-if analysis function that enables the analysis of results under various conditions by allowing the flexible definition of various process related environment variables and combinations of the processing flows. Such what-if analysis functions dramatically increase the usability of traffic data but are not provided by other traffic data systems. Experimantal results for cleaning the traffic history data showed that it provides superior performance in terms of validity and completeness.

• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.522-530
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• 2014

Spatio-temporal congestion evolution pattern can be reproduced using the VDS(Vehicle Detection System) historic speed dataset in the TMC(Traffic Management Center)s. Such dataset provides a pool of spatio-temporally experienced traffic conditions. Traffic flow pattern is known as spatio-temporally recurred, and even non-recurrent congestion caused by incidents has patterns according to the incident conditions. These imply that the information should be useful for traffic prediction and traffic management. Traffic flow predictions are generally performed using black-box approaches such as neural network, genetic algorithm, and etc. Black-box approaches are not designed to provide an explanation of their modeling and reasoning process and not to estimate the benefits and the risks of the implementation of such a solution. TMCs are reluctant to employ the black-box approaches even though there are numerous valuable articles. This research proposes a more readily understandable and intuitively appealing data-driven approach and developes an algorithm for identifying congestion patterns for recurrent and non-recurrent congestion management and information provision.

• 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.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.06a
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• pp.153-154
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• 2010

최근 GPS를 이용한 위치기반서비스(LBS)는 일상생활의 큰 변화를 가져왔다. 이러한 LBS 중에서도 가장 흔히 접할 수 있는 것이 차량 항법용 장치인 네비게이션이다. 초창기의 네비게이션이 단순히 목적지까지 가기 위한 가장 짧은 도로를 안내해 주는 것이 목적이었다면 최근에는 TPEG과 같은 실시간 교통 상황 안내, 이용자 선호도를 고려한 경로탐색, 에코 드라이빙과 같은 좀 더 높은 수준의 서비스가 개발되고 있다. 하지만 현재의 차량항법 시스템은 교통상황 안내 이외에는 현실적인 도로상황 반영이 어렵고 시간대별 통행량의 차이가 큰 국내 교통상황에 대한 고려가 미흡하다. 또한 교통정보 수집장치가 설치되지 않은 도로는 교통상황 안내마저도 제공되지 않는다. 본 논문에서는 이러한 문제점을 개선하기 위하여 임베디드 DBMS를 이용하여 과거 통행이력정보를 저장하고 활용할 수 있도록 도로망 DB스키마를 정의하였다. 그리고 이를 탐색경로의 소요 시간 산출에 활용하여 실제적인 도로상황을 고려한 경로안내 방법을 제안한다. 실제주행이력은 네트워크 데이터의 링크단위로 관리되며 저장된 주행이력은 경로탐색에 필요한 정보인 진입시각, 진출시각, 요일, 통행횟수로 관리된다. 이렇게 단말기에 저장된 주행이력은 중앙DB를 통해 사용자간 공유가 가능하고 향후에는 실시간 동기화를 통해 더욱 신뢰도 높은 경로탐색에 활용될 수 있다.

• Journal of Korean Society of Transportation
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• v.26 no.5
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• pp.131-141
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• 2008

The objective of this study is to propose methods for determining optimal representative value and the optimal size of historical data for reliable travel time forecasting. We selected values with the smallest mean of forecasting errors as the optimal representative value of travel time pattern data. The optimal size of historical data used was determined using the CVMSE(Cross Validated Mean Square Error) method. According to the results of applying the methods to point vehicle detection data of Korea Highway Corporation, the optimal representative value were analyzed to be median. Second, it was analyzed that 60 days' data is the optimal size of historical data usedfor travel time forecasting.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.07a
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• pp.117-118
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• 2014

최근 공공기관의 공공데이터 제공이 활성화 되고 있으며, 이를 활용한 응용서비스에 대한 요구도 증가하고 있는 추세이다. 현재 교통정보예측 플랫폼은 실시간 교통정보 또는 과거 교통정보이력을 분석하여 미래의 교통량이나 도착시간정보를 제공하고 있으나 날씨, 사고 등과 같은 미래 교통정보에 즉각적인 영향을 줄 수 있는 요소를 배제하고 있어 높은 신뢰도를 확보하기 어렵다. 본 논문에서는 교통정보예측에 영향을 주는 요소인 기상, 사고, 교통정보와 같은 공공데이터를 효율적으로 수집 저장 처리할 수 있는 저장방식 및 신뢰도 높은 교통정보를 예측할 수 있는 예측기술이 포함된 b-Traffic 서비스 플랫폼을 제시한다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.86-99
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• 2021

One of the methods to alleviate traffic congestion is to increase the efficiency of the roads by providing traffic condition information on road user and distributing the traffic. For this, reliability must be guaranteed, and quantitative real-time traffic speed prediction is essential. In this study, and based on analysis of traffic speed related to traffic conditions, historical data correlated with traffic flow were used as input. We developed an LSTM model that predicts speed in response to normal traffic conditions, along with a CNN-LSTM model that predicts speed in response to incidents. Through these models, we try to predict traffic speeds during the hour in five-minute intervals. As a result, predictions had an average error rate of 7.43km/h for normal traffic flows, and an error rate of 7.66km/h for traffic incident flows when there was an incident.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.1
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• pp.15-27
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• 2013

While the vehicle detector is collected from a variety of information was mainly used as a real-time data. Recently scheme of application for archived traffic data has become increasingly important. In this background, this research were conducted on the improvement of the preprocessing for archived traffic data application. The purpose of improving specific preprocessing was reflect transportation phenomena by traffic data. As evaluation result, improvement preprocessing was close to the actual value than exist preprocessing.

• The Journal of the Korea Contents Association
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• v.6 no.1
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• pp.114-125
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• 2006

Since wireless Internet services and small mobile communication devices come into wide use as well as the use of GPS is rapidly growing, researches on moving object, whose location information shifts sequently in accordance with time interval, are being carried out actively. Especially, the researches on vehicle moving object are applied to Advanced traveler information system, vehicle tracking system, and distribution transport system. These systems are very useful in searching previous positions, predicted future positions, the optimum course, and the shortest course of a vehicle by managing historical data of the vehicle movement. In addition, vehicle historical data are used for distribution transport plan and vehicle allocation. Vehicle historical data are stored at regular intervals, which can have a pattern. For example, a vehicle going repeatedly around a specific section follows a route very similar to another. If historical data of the vehicle with a repeated route course are stored at regular intervals, many redundant data occur, which result in much waste of storage. Therefore this thesis suggest a vehicle historical data store scheme for vehicles with a repeated route course using similar trajectory which efficiently store vehicle historical data.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.1005-1008
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• 2011

본 논문에서는 교통 이력자료의 시공간 데이터를 활용하여 교통 분석 및 예측에 필요한 신뢰성 높은 데이터를 제공하기 위한 TCS/HI-PASS 전처리 알고리즘을 제안한다. 시공간 데이터의 전처리 알고리즘은 각종 교통정보에 이용되고 있으며, 그 중 대표적으로 활용되고 있는 것이 차량 검지기(VDS)를 통해 수집된 교통량, 속도, 점유율 정보이다. 이러한 정보에 가공처리 알고리즘을 적용하여 공간평균속도 기반의 통행시간을 산정하고 있으며, 고속도로 통행료 수납시스템(TCS)으로 부터는 출발영업소와 도착영업소의 진 출입시간을 기반으로 평균통행시간을 산정하고 있다. 본 연구에서는 차량 검지기(VDS) 데이터와 기존 TCS 데이터의 전처리 알고리즘을 분석하여 TCS와 HI-PASS 데이터 기반의 개선된 전처리 알고리즘을 설계, 구현하였다.