Comparison of Fuzzy-Wavelet Radial Basis Function Neural Network Freeway Incident Detection Model with California Algorithm

Abstract

A multiparadigm general methodology is advanced for development of reliable, efficient, and practical freeway incident detection algorithms. The performance of the new fuzzy-wavelet radial basis function neural network (RBFNN) freeway incident detection model of Adeli and Karim is evaluated and compared with the benchmark California algorithm #8 using both real and simulated data. The evaluation is based on three quantitative measures of detection rate, false alarm rate, and detection time, and the qualitative measure of algorithm portability. The new algorithm outperformed the California algorithm consistently under various scenarios. False alarms are a major hindrance to the widespread implementation of automatic freeway incident detection algorithms. The false alarm rate ranges from 0 to 0.07% for the new algorithm and from 0.53 to 3.82% for the California algorithm. The new fuzzy-wavelet RBFNN freeway incident detection model is a single-station pattern-based algorithm that is computationally efficient and requires no recalibration. The new model can be readily transferred without retraining and without any performance deterioration.