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http://dx.doi.org/10.12652/Ksce.2012.32.2D.137

Building a Traffic Accident Frequency Prediction Model at Unsignalized Intersections in Urban Areas by Using Adaptive Neuro-Fuzzy Inference System  

Kim, Kyung Whan (경상대학교 도시공학과, 환경 및 지역발전연구소)
Kang, Jung Hyun (경상대학교 대학원 도시공학전공)
Kang, Jong Ho (경상대학교 대학원 도시공학전공)
Publication Information
KSCE Journal of Civil and Environmental Engineering Research / v.32, no.2D, 2012 , pp. 137-145 More about this Journal
Abstract
According to the National Police Agency, the total number of traffic accidents which occurred in 2010 was 226,878. Intersection accidents accounts for 44.8%, the largest portion of the entire traffic accidents. An research on the signalized intersection is constantly made, while an research on the unsignalized intersection is yet insufficient. This study selected traffic volume, road width, and sight distance as the input variables which affect unsignalized intersection accidents, and number of accidents as the output variable to build a model using ANFIS(Adaptive Neuro-Fuzzy Inference System). The forecast performance of this model is evaluated by comparing the actual measurement value with the forecasted value. The compatibility is evaluated by R2, the coefficient of determination, along with Mean Absolute Error (MAE) and Mean Square Error (MSE), the indicators which represent the degree of error and distribution. The result shows that the $R^2$ is 0.9817, while MAE and MSE are 0.4773 and 0.3037 respectively, which means that the explanatory power of the model is quite decent. This study is expected to provide the basic data for establishment of safety measure for unsignalized intersection and the improvement of traffic accidents.
Keywords
ANFIS; unsignalized intersection; fuzzy inference system; neuro-fuzzy;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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