• Journal of the Korean Society of Industry Convergence
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• v.6 no.2
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• pp.131-139
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• 2003

The links operating interrupted flow are intend to yield the traffic between the out flow and inflow part effect zone of street section, we build the delay model using the time gap between under construction and not. We review the applicability of interrupted flow, and thus we can put this data to practical use as the basis data to compute the inducement charge for traffic delay. Also building about traffic effect zone of interrupted flow wouldn't produce at the section beside occupying roads and construction cross section, thus we must review the plan to minimize traffic delay by the construction occupying road. In future there must be advanced the incomplete in this study, and groping for the various alternatives to minimize the traffic delay by the road occupying construction, with developing the various sets of detailed analyzing models, that is analysis on the street strength, crossroads geometrical forms of crossroads, public traffics, pedestrians, occupying types.

• Journal of Korean Society of Transportation
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• v.35 no.1
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• pp.63-78
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• 2017

The purpose of this study is to determine the optimal aggregation interval to increase the reliability when estimating representative value of individual vehicle travel time collected by DSRC equipment in interrupted traffic flow section in National Highway. For this, we use the bimodal asymmetric distribution data, which is the distribution of the most representative individual vehicle travel time collected in the interrupted traffic flow section, and estimate the MSE(Mean Square Error) according to the variation of the aggregation interval of individual vehicle travel time, and determine the optimal aggregation interval. The estimation equation for the MSE estimation utilizes the maximum estimation error equation of t-distribution that can be used in asymmetric distribution. For the analysis of optimal aggregation interval size, the aggregation interval size of individual vehicle travel time was only 3 minutes or more apart from the aggregation interval size of 1-2 minutes in which the collection of data was normally lost due to the signal stop in the interrupted traffic flow section. The aggregation interval that causes the missing part in the data collection causes another error in the missing data correction process and is excluded. As a result, the optimal aggregation interval for the minimum MSE was 3~5 minutes. Considering both the efficiency of the system operation and the improvement of the reliability of calculation of the travel time, it is effective to operate the basic aggregation interval as 5 minutes as usual and to reduce the aggregation interval to 3 minutes in case of congestion.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.6
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• pp.74-83
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• 2011

The high-pass transportation information system directly collects section information by using probe cars and therefore can offer more reliable information to drivers. However, because the running condition and features of probe cars and statistical processing methods affect the reliability of the information and particularly because the section travel time is greatly influenced by whether there has been delay by signals on urban roads or not, there can be much deviation among the collected individual probe data. Accordingly, researches in multilateral directions are necessary in order to enhance the credibility of the section information. Yet, the precedent studies related to high-pass information provision have been conducted on the highway sections with the feature of continuous flow, which has a limit to be applied to the urban roads with the transportational feature of an interrupted flow. Therefore, this research aims at analyzing the features of high-pass transportation data on urban roads and finding a proper processing method. When the characteristics of the high-pass data on urban roads collected from RSE were analyzed by using a time-space diagram, the collected data was proved to have a certain pattern according to the arriving cars' waiting for signals with the period of the signaling cycle of the finish node. Moreover, the number of waiting for signals and the time of waiting caused the deviation in the collected data, and it was bigger in traffic jam. The analysis result showed that it was because the increased number of waiting for signals in traffic jam caused the deviation to be offset partially. The analysis result shows that it is appropriate to use the mean of this collected data of high-pass on urban roads as its representative value to reflect the transportational features by waiting for signals, and the standard of judgment of delay and congestion needs to be changed depending on the features of signals and roads. The results of this research are expected to be the foundation stone to improve the reliability of high-pass information on urban roads.