• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.3
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• pp.45-51
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• 2016

Existing real-time signal control system was brought up typical problems which are supersaturated condition, point detection system and loop detection system. For that reason, the next generation signal control system of advanced form is required. Following thesis aimed at calculating queue length for the next generation signal control system to utilize basic parameter of signal control in crossing queue instead of the volume of real-time through traffic. Overflow saturated condition which was appeared as limit of existing system was focused to set-up range. Real-time location information of individual vehicle which is collected by GPS data. It converted into the coordinate to apply shock wave model with an linear equation that is extracted by regression model applied by a least square. Through the calculated queue length and link length by contrast, If queue length exceed the link, queue of downstream intersection is included as queue length that upstream queue vehicle is judeged as affecting downstream intersection. In result of operating correlation analysis among link travel time to judge confidence of extracted queue length, Both of links were shown over 0.9 values. It is appeared that both of links are highly correlated. Following research is significant using real-time data to calculate queue length and contributing to signal control system.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.29-44
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• 2009

A variety of research efforts, using advanced wireless communication technologies, have been made to develop more reliable traffic information system. This study presents a novel decentralized traffic information system based on vehicle infrastructure integration (VII). A major objective of this study was also to devise a methodology for determining appropriate spacing of roadside equipment (RSE) to fully exploit the benefits of the proposed VII-based traffic information system. Evaluation of travel time estimation accuracy was conducted with various RSE spacings and the market penetration rates of equipped vehicle. A microscopic traffic simulator, VISSIM, was used to obtain individual vehicle travel information for the evaluation. In addition, the ANOVA tests were conducted to draw statistically significant results of simulation analyses in determining the RSE spacing. It is expected that the proposed methodology will be a valuable precursor to implementing capability-enhanced next generation traffic information systems under the forthcoming ubiquitous transportation environment.

• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.567-578
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• 2014

The study suggested a method to improve analysis accuracy such that the interactive effects of transportation changes between outside and inside of sub-areas were sequentially considered in the analysis by linking a macroscopic network analysis and a microscopic traffic simulation. A dynamic O/D estimation method was developed for practical implement of sub-area microscopic simulation analysis by using the results of macroscopic network analysis, the results of selected link analysis at the cordon line of the sub-area, departure time data of household travel survey, timely observed traffic volume data at the cordon. This estimated dynamic O/D for the sub-area made it possible to analyze traffic phenomena in details. Various detailed phenomena such as traffic queues, delay at intersection, and conflicts between vehicles, which is impossible to be grasped through a macroscopic analysis, can be analyzed with the dynamic microscopic traffic analysis. Through implementing an empirical study and validation, the study provided a reference result about accuracy of a microscopic traffic simulation of a sub-area to help its application for real transportation policy analysis.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.121-131
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• 2014

Traffic speed is an important measure in transportation. It can be employed for various purposes, including traffic congestion detection, travel time estimation, and road design. Consequently, accurate speed prediction is essential in the development of intelligent transportation systems. In this paper, we present an analysis and speed prediction of a certain road section in Busan, South Korea. In previous works, only historical data of the target link are used for prediction. Here, we extract features from real traffic data by considering the neighboring links. After obtaining the candidate features, linear regression, model tree, and k-nearest neighbor (k-NN) are employed for both feature selection and speed prediction. The experiment results show that k-NN outperforms model tree and linear regression for the given dataset. Compared to the other predictors, k-NN significantly reduces the error measures that we use, including mean absolute percentage error (MAPE) and root mean square error (RMSE).