| A universal freeway incident detection framework is a task that remains unfulfilled despite the promising approaches that have been recently explored. Only recently, researchers and practitioners have begun to increasingly realize that for an incident detection framework to be universally operational and successful, it needs to fulfill all components of a set of recognized needs. It is the objective of this research to define those universality requirements and produce an incident detection framework that possesses the potential to fulfill them.;A new potentially universal freeway incident detection framework has been proposed, developed and evaluated. The research effort was started by defining a comprehensive set of requirements that any universal incident detection algorithm or framework should fulfill. Among these requirements, an incident detection needs to be operationally accurate, automatically transferable, and capable of automatically adapting to changes in the freeway environment. This set of universality requirements was used as a template against which all algorithms within the scope of this study have been evaluated. The universality of the most well known existing incident detection algorithms was tested. Serious lack of universality, particularly transferability, was detected in all existing algorithms. Preliminary investigation of two promising advanced neural networks, namely the LOGICON and the PNN, was conducted. The PNN was more appealing due to its universality potential. The PNN was modified using a principal components transformation layer that resulted in performance enhancements, together with its potential universality lead to the choice of the modified PNN for in-depth development. The in-depth development stage was divided into three phases: feature extraction, on-site real time retraining of the PNN after transferability, and development of a post processor output interpreter. The overall PNN-based framework was found to be fully complaint with the entire set of universality requirements. Finally a new approach for training a multi smoothing parameters version of the PNN was investigated. The approach utilized genetic algorithms for optimizing the selection of the smoothing parameters. Obtained results indicated an improvement in performance over the single smoothing parameter PNN but on the expense of longer training time. |
