Implementation of Advanced Techniques for Automated Freeway Incident Detection

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-Feed-Forward 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 PNN algorithm itself was then translated from C to Java as a stand alone application object and was interfaced to the GUI applet running on the same host. The GUI display is updated each time a new output is computed by the PNN. The PNN algorithm and the GUI display update run as separate threads of control in Java; this concurrency leads to better utilization of CPU resources. A new module for computing the principal component transformation of the volume and occupancy inputs was developed to replace using statistical packages for this transformation. This was needed for maximum portability and independence of the overall system. Another module for computing volume and occupancy historical Averages for different Times and Locations (ATLs.) was alsodeveloped to prepare the ATLs from real freeway data. The PNN and GUI were tested and correct operation was confirmed with sample inputs from data files. The whole package was interfaced to a remote C++ CORBA client program that acquires online CalTrans traffic data from a CORBA server in the Testbed. Communication modules were added to the CORBA client program as well as the PNN to enable online volume and occupancy data from different freeway sections to be sent from the CORBA client to the PNN at a specific rate (once every 30s). The data are sent to the PNN using a reliable TCP/IP streams sockets connection. An on-line retraining module was developed as well. This module enables the TMC operator to initiate retraining on recently captured incident data, on-line without disturbing the operation of the system. The PNN was then started on-line on a 5 mile section of the 405 freeway, for on line monitoring and testing. The overall on-line operation of the PNN was demonstrated to Caltrans engineers from D12. Currently, efforts are in progress to expand the network coverage to enhance the odds of capturing incidents. On line evaluation will be performed next.