• International Journal of Highway Engineering
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• v.16 no.2
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• pp.119-129
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• 2014

PURPOSES : The purpose of this study is to develop the U-turn accident model at 4-legged signalized intersections in urban areas. METHODS : In order to analyze the characteristics of the accidents which are associated with U-turn operation at 4-legged signalized intersections in urban areas and develop an U-turn accident model by regression analysis, the tests of overdispersion and zero-inflation are conducted about the dependent variables of number of accidents and EPDO (Equivalent Property Damage Only). RESULTS : As their results, the Poisson model fits best for number of accident and the ZIP (Zero Inflated Poisson) fits best for EPOD, the variables of conflict traffic, width of opposing road, traffic passing speed are adopted as independent variable for both models. The variables of number of bus berths and rate of U-turn signal time at which the U-turn is permitted are adopted as independent variable only for EPDO. CONCLUSIONS : These study results suggest that U-turn would be permitted at the intersection where the width of opposing road is wider than 11.9 meters, the passing vehicle speed is not high and U-turn operation is not hindered by the buses stopping at bus stops.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.4
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• pp.1279-1287
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• 2014

The purpose of this study is to develop the U-turn accident model at signalized intersections in urban areas. The characteristics of the accidents which are associated with U-turn operation at 3 and 4-legged signalized intersections was analyzed and the U-turn accident model was developed by regression analysis in Changwon city. First, in order to analyze the effectiveness on traffic accidents by U-turn installation, the difference of mean of traffic accident number are measured between two groups which are composed by whether or not U-turn installation the groups by Mann-Whitney U test. The result of significance test showed that intergroup comparison on mean by accident types made difference except rear-end accident type and by accident locations exit section only showed difference in significance level at 4-legged intersections, so the accident number have more where the U-turn is permitted than not. Response measures about the number of accidents were classified by whether accidents occurred and accident model were constructed using binomial logistic regression analysis method. The developed models show that the variables of conflict traffic, number of opposing lane are adopted as independent variable for both intersections. The variables of longitudinal grade for 3-legged signalized intersection and number of crosswalk for 4-legged signalized intersection at which the U-turn is permitted is adopted as independent variable only. These study results suggest that U-turn would be permitted at the intersection where the number of opposing lane is more than 3.5 each, the longitudinal grade of opposing road is upward flow and there is need to establish the U-turn traffic sign at signalized intersections.

• International Journal of Highway Engineering
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• v.11 no.2
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• pp.17-25
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• 2009

The goal of this study is to develop Logistic regression model by accident location(entry section, exit section, inside intersection and pedestrian crossing section). Based on the accident data of Chungbuk Provincial Police Agency(2004$\sim$2005) and the field survey data, the geometric elements, environmental factor and others related to traffic accidents were analyzed. Developed models are all analyzed to be statistically significant(chi-square p=0.000, Nagelkerke $R^2$=0.363$\sim$0.819). The models show that the common factors of accidents are the traffic volume(ADT), distant of crossing and exclusive left turn lane, and the specific factors are the minor traffic volume(inside intersection model) and U-turn of main road(pedestrian crossing model). Hosmer & Loineshow tests are evaluated to be statistically significant(p$\geqq$0.05) except the entry section model. The correct classification rates are also analyzed to be very predictable(more than 73.9% to all models).