• 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.

• 한국ITS학회:학술대회논문집
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• 2005.11a
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• pp.197-202
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• 2005

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.6
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• pp.1873-1879
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• 2014

There are various methodologies to forecast the travel time using real-time data but the K-nearest neighborhood (KNN) method in general is regarded as the most one in forecasting when there are enough historical data. The objective of this study is to evaluate applicability of KNN method. In this study, real-time and historical data of toll collection system (TCS) traffic flow and the dedicated short range communication (DSRC) link travel time, and the historical path travel time data are used as input data for KNN approach. The proposed method investigates the path travel time which is the nearest to TCS traffic flow and DSRC link travel time from real-time and historical data, then it calculates the predicted path travel time using weight average method. The results show that accuracy increased when weighted value of DSRC link travel time increases. Moreover the trend of forecasted and real travel times are similar. In addition, the error in forecasted travel time could be further reduced when more historical data could be available in the future database.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.833-839
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• 2019

Bus Information System (BIS) collects information related to the operation of buses and provides information to users through predictive algorithms. Method of predicting through recent information in same section reflects the traffic situation of the section, but cannot reflect the characteristics of the target line. The method of predicting the historical data at the same time zone is limited in forecasting peak time with high volatility of traffic flow. Therefore, we developed a pattern recognition bus arrival time prediction algorithm which could be overcome previous limitation. This method recognize the traffic pattern of target flow and select the most similar past traffic pattern. The results of this study were compared with the BIS arrival forecast information history of Seoul. RMSE of travel time between estimated and observed was approximately 35 seconds (40 seconds in BIS) at the off-peak time and 40 seconds (60 seconds in BIS) at the peak time. This means that there is data that can represent the current traffic situation in other time zones except for the same past time zone.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.4
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• pp.11-23
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• 2011

The objective of this paper is to develop a daily pattern clustering algorithm using historical traffic data that can reliably detect under various traffic flow conditions in urban streets. The developed algorithm in this paper is categorized into two major parts, that is to say a macroscopic and a microscopic points of view. First of all, a macroscopic analysis process deduces a daily peak/non-peak hour and emphasis analysis time zones based on the speed time-series. A microscopic analysis process clusters a daily pattern compared with a similarity between individuals or between individual and group. The name of the developed algorithm in microscopic analysis process is called "Two-step speed clustering (TSC) algorithm". TSC algorithm improves the accuracy of a daily pattern clustering based on the time-series speed variation data. The experiments of the algorithm have been conducted with point detector data, installed at a Ansan city, and verified through comparison with a clustering techniques using SPSS. Our efforts in this study are expected to contribute to developing pattern-based information processing, operations management of daily recurrent congestion, improvement of daily signal optimization based on TOD plans.

• 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.

• 한국공간정보시스템학회:학술대회논문집
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• 2007.06a
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• pp.177-182
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• 2007

최근 텔레매틱스 분야에서 GPS 수신기를 장착한 probe car를 통해 교통 정보를 수집하는 방법에 대한 연구가 활발히 진행되고 있다. 이 방법은 기존에 교통 정보를 수집하기 위해 활용되고 있던 고정식 검지기들에 비해 수집되는 정보가 높은 신뢰성을 가지고, 도로 환경에 민감하지 않으며, 낮은 유지비용으로 운용할 수 있다는 장점을 가지고 있다. 하지만, probe car는 자신의 위치 정보를 교통 정보 센터로 전송해 주어야 하기 때문에 프라이버시가 노출될 수 있고, 주차되어 있는 시간에는 통행 정보를 보내줄 수가 없다. 이런 이유로 대중 교통차량이나 상업용 차량이 주로 probe car로 활용되어지게 되는데, 그 수가 많지 않을뿐더러 운행 구간이 고르게 분포되지 않아 probe car가 지나지 않는 구간, 즉 교통 정보 누락 구간이 존재할 수 있는 문제점을 가지고 있다. 본 논문에서는 교통 정보 누락 구간의 처리를 위해 과거의 이력 정보로 대체하는 방법, 주변 도로의 구간 정보로 예측하는 방법, 회귀 분석을 통한 예측 방법 등을 기술하고 실제 probe car들로 수집된 서울시 강남대로 구간의 자료로 각 방법에 대한 실험을 실시하여 각각의 방법에 대한 결과를 비교 분석한다.