• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.3
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• pp.50-58
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• 2017

TPCLT(Twice Per Cycle Left-turn) operation reduces this left-turn 'spill-over' problem on an as needed basis by servicing the protected left-turn movement as a leading and a lagging left-turn. In this study, to evaluate the effectiveness of TPCLT applied to three-legged signalized intersection in Korea, the analysis was carried out using VISSIM and SSAM model analysis. The study was implemented by three cases which are TPCLT operation, non-TPCLT operation and half-cycle operation using VISSIM program. According to the 9-left-turn volume scenario, total delay and travel times of each case was analyzed by VISSIM program. The study result shows more effective applying TPCLT operaion in the present ~ +50% scenario area at the intersection in terms of total delay.

• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.207-218
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• 2001

As a city and industries are developed rapidly, a traffic accident and congestion take places on the road link become serious and it can be a large problem of the society in the future. Especially, most of the traffic accidents on the signalized intersection are caused by the human factor, vehicle and environmental factor mutually. The relation of the traffic accident and volume is acting on the outbreak of the traffic accident and the mistake of driver altogether as a major cause. The purpose of this paper is to develop a model for the forecasting of the traffic accident and to use research data gained to predict many traffic accidents. The data of this study were used with real one of the 73 areas of the four-legged signalized intersection in Kwang-ju city from 1996 to 1998 for three years to develop a model for the forecasting of the traffic accident. The statistical methods used in this paper are the principal component, regression and correlation analysis. We studied accident models to find out useful data from the statistics method and applied the data to the different area of the Choun-La province for the verification of the model. So, the result of this paper showed a reasonable model for the forecasting or the traffic accident and possibility of the model for simulating on real case. Finally, This study would be made of a study continually for the safe design and plan for the four-legged signalized intersection.

• Journal of Korean Society of Transportation
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• v.29 no.5
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• pp.157-167
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• 2011

Many problems, such as unexpected delay and collision with pedestrians or vehicles, occur generally at signalized intersections where bicycle users are frequently involved. These problems have hindered bicycle users from riding bicycles on urban highways. The aim of this study is to suggest proper traffic signal operation methods for safe and convenient highway crossing of bicycles. Three types of crossing methods at signalized intersections are proposed and analyzed: (1) indirect left turn, (2) direct left turn on an exclusive bicycle lane, and (3) direct left turn on a bicycle box. The VISSIM simulation tests were conducted based on fifty-four operation scenarios prepared by varying vehicle and bicycle traffic volumes. Both delay and the number of stops are used as the measures of effectiveness in the analysis. The results from the three-legged signalized intersections suggested that (1) the indirect left turn is appropriate when vehicle demand is high while bicycle demand is not, (2) direct left turn on an exclusive bicycle lane is appropriate when both vehicle and bicycle demands are high, and (3) direct left turn on a bicycle box is appropriate when both vehicle and bicycle demands are light.

• Journal of Korean Society of Transportation
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• v.32 no.3
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• pp.266-279
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• 2014

For the evaluation of roadway safety, diverse methods, including before-after studies, simple comparison using historic traffic accident data, methods based on experts' opinion or literature, have been applied. Especially, many research efforts have developed traffic accident prediction models in order to identify critical elements causing accidents and evaluate the level of safety. A traffic accident prediction model must secure predictability and transferability. By acquiring the predictability, the model can increase the accuracy in predicting the frequency of accidents qualitatively and quantitatively. By guaranteeing the transferability, the model can be used for other locations with acceptable accuracy. To this end, traffic accident prediction models using non-linear regression, artificial neural network, and structural equation were developed in this study. The predictability and transferability of three models were compared using a model development data set collected from 90 signalized intersections and a model validation data set from other 33 signalized intersections based on mean absolute deviation and mean squared prediction error. As a result of the comparison using the model development data set, the artificial neural network showed the highest predictability. However, the non-linear regression model was found out to be most appropriate in the comparison using the model validation data set. Conclusively, the artificial neural network has a strong ability in representing the relationship between the frequency of traffic accidents and traffic and road design elements. However, the predictability of the artificial neural network significantly decreased when the artificial neural network was applied to a new data which was not used in the model developing.