Development Testing And Evaluation Of Advanced Techniques For Freeway Incident Detection
Stephen G. Ritchie and
Institute of Transportation Studies, Research Reports, Working Papers, Proceedings from Institute of Transportation Studies, UC Berkeley
In this research, the authors introduce and define a universal incident detection framework that is capable of fulfilling all components of a set of recognized needs. An algorithm is presented that has the potential to fulfill the defined universality requirements. It is a modified form of a probabilistic neural network (PNN) that utilizes the concept of statistical distance. The first part of the report presents a definition of the attributes and capabilities that a potentially universal freeway incident detection framework should possess. The second part discusses the training and testing of the PNN. The third section evaluates the PNN relative to the proposed universality template. In addition to a large set of simulated incidents, the authors utilize a large real incident database from the I-880 freeway in California to comparatively evaluate the performance and transferability of different algorithms including the PNN.
Keywords: Bayesian statistical decision theory; Neural networks (Computer science); Automatic incident detection (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:cdl:itsrrp:qt5720z2mw
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