• Journal of Korean Society of Transportation
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• v.25 no.6
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• pp.99-109
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• 2007

There have been few studies on the impacts of independent recurrent and non-recurrent congestion on freeway networks. The main reason is due partly to the lack of traffic data collected during those periods of recurrent and non-recurrent congestion and partly to the difficulty of using the simulation tools effectively. This study has suggested a methodology to analyze the independent impacts of the recurrent and non-recurrent congestion on target freeway segments. The proposed methodology is based on an elaborately calibrated simulation analysis, using real traffic data obtained during the recurrent and non-recurrent congestion periods. This paper has also summarized the evaluation results from the field tests of two ITS technologies, which were developed to provide drivers with real-time traffic information under traffic congestion. As a result, their accuracy may not be guaranteed during the transition periods such as the non-recurrent congestion. In summary, this study has been focused on the importance of non-recurrent congestion compared to recurrent congestion, and the proposed methodology is expected to provide a basic foundation for prioritizing limited government investments for improving freeway network performance degraded by recurrent or non-recurrent congestion.

• Proceedings of the KOR-KST Conference
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• 2007.05a
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• pp.285-294
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• 2007

• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.105-118
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• 2001

Incident detection rate and false alarm rate are the key measures tot estimating the performance of automatic incident detection algorithms. It is, however inherently very difficult to improve the two measures simultaneously. The main purpose of this study is to present some methods for solving the problem. For this, an incident detection algorithm has been designed in this study. The algorithm is consisted of two functions, one for detecting incident and another for detecting congestion. A logic for distinguishing non-recurrent congestion from recurrent congestion was employed in the algorithm. The new algorithm basically requires speed, flow, and occupancy data for defining incident situation, but the algorithm is able to perform this task without one of the three parameters. The performance of the algorithm has been evaluated by using the field data collected from Interstate Highway 880 in Bay Area, California. The empirical analysis results are very promising and thus, the algorithm proposed may be very useful for the analysts. This paper presents some empirical test results for the performance of California incident detection algorithm, only for the reference purpose.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.67-78
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• 2017

Traffic congestion cost is increasing annually. Specifically congestion caused by the CDB traffic contains more than a half of the total congestion cost. Recent advancement in the field of Big Data, AI paved the way to industry revolution 4.0. And, these new technologies creates tremendous changes in the traffic information dissemination. Eventually, accurate and timely traffic information will give a positive impact on decreasing traffic congestion cost. This study, therefore, focused on developing both recurrent and non-recurrent congestion prediction models on urban roads by adopting Recurrent Neural Network(RNN), a tribe in machine learning. Two hidden layers with scaled conjugate gradient backpropagation algorithm were selected, and tested. Result of the analysis driven the authors to 25 meaningful links out of 33 total links that have appropriate mean square errors. Authors concluded that RNN model is a feasible model to predict congestion.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.6 no.2
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• pp.1-14
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• 2007

The purpose of this research is to investigate service item to develop archived VDS(Vehicle Detecting System) data user services. Through the review of related studies and literature and investigation of the current application status of the vehicle detector data, the service item from the historical detector data were identified. The relative importance of the identified service item was measured based on the application purpose, usage and frequency of application.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.6 no.1 s.12
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• pp.13-26
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• 2007

A VDS data in the domestic has been used within limits to real time information such as congestion management, incident management, and route guidance service. On the other hand, a VDS data in the foreign countries had been used to various objectives such as transportation policy assessment, transportation construction evaluation, franc safety improvement, and etc. The scope and method of the study is the VDS data which was installed in the uninterrupted flow such as the freeway and the interrupted flow in a diversion route of the leeway. It has investigated and analyzed the VDS as our subject to study, study objective and study methodology for each study generally classified as 1) data collection 2) data processing 3) data store and 4) data quality section. This study has investigated and analyzed the various literatures in domestic and foreign countries regarding the VDS data. And It drew the development direction of the study which is about VDS data in domestic from now.