Abstract :
In this study, a back propagation artificial neural network model utilizing characteristics of urban signalized intersections for occurrence prediction of intersection - related traffic crashes, along with its application for crash reduction, are proposed. The 1,593 traffic crashes reported at 62 signalized intersections in Taichung city, Taiwan during year 2000 - 2001 were analyzed. These intersections were decomposed into 636 approaching direction combinations (ADCs). Characteristics, as well as the number of crashes, corresponding to each ADC were investigated. A back propagation artificial neural network (ANN) model was then generated. It was found that the ANN model exhibited great prediction ability with 0.992 correlation coefficient, and 3.38E- 06 mean square error. With the ANN model, a proposed decision-making scheme for intersection rehabilitation was suggested. Case study indicated that the suggested scheme could serve well as a referable tool.
Keywords :
backpropagation; decision making; road accidents; road safety; road traffic; traffic engineering computing; artificial neural network model; back propagation ANN model; decision-making scheme; intersection rehabilitation; signalized intersection crashes; traffic crashes; Artificial neural networks; Cities and towns; Computer crashes; Decision making; Mean square error methods; Neural networks; Predictive models; Signal analysis; Telecommunication traffic; Traffic control;
