California Partners for Advanced Transit and Highways (PATH)
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A significant body of research on advanced techniques for automated freeway incident detection has been conducted at the University of California, Irvine (UCI). Such advanced pattern recognition techniques as artificial neural networks (ANNs) have been thoroughly investigated and their potential superiority to other techniques has been demonstrated. Of the investigated ANN architectures, two have shown the best potential for real-time implementation: namely, the Probabilistic Neural Network (PNN), (Abdulhai and Ritchie 1997), and the Multi-Layer-FeedForward Neural Network (MLF), (Cheu and Ritchie 1995). This project extended existing freeway incident detection research conducted under both PATH and under the ATMS Testbed Research Program, to operationalizes its principal findings. The most prosmising neural network, the PNN, was integrated into the UCI testbed for on line operation on the testbed network in Southern California. The PNN incident detection system was re-coded in Java, to facilitate network communications and platform-independent operation. A Java-based graphical user interface has been developed. The GUI components include a display of the probabilistic neural network (PNN), the current input to the PNN, a sliding window display of the output (the computed incident probability every time step) and a sliding button to allow the user to specify the desired misclassification cost ratio. The GUI code is in the form of a Java Applet object and has a modular structure that makes it easier to incorporate possible future modifications and extensions. The
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