Development and adaptation of constructive probabilistic neural network in freeway incident detection

Development and adaptation of constructive probabilistic neural network in freeway incident detection

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Article ID: iaor20031097
Country: United Kingdom
Volume: 10C
Issue: 2
Start Page Number: 121
End Page Number: 148
Publication Date: Apr 2002
Journal: Transportation Research. Part C, Emerging Technologies
Authors: , ,
Keywords: neural networks
Abstract:

This paper investiages the use of constructive probabilistic neural network (CPNN) in freeway incident detection, including model development and adaptation. The CPNN was structured based on mixture Gaussian model and trained by a dynamic decay adjustment algorithm. The model was first trained and evaluated on a simulated incident database in Singapore. The adaptation of CPNN on the I-880 freeway in California was then investigated in both on-line and off-line environments. This paper also compares the performance of the CPNN model with a basic probabilistic neural network (BPNN) model. The results show that CPNN has three main advantages over BPNN: (1) CPNN has clustering ability and therefore could achieve similarly good incident-detection performance with a much smaller network size; (2) each Gaussian component in CPNN has its own smoothing parameter that can be obtained by the dynamic decay adjustment algorithm with a few epochs of training; and (3) the CPNN adaptation methods have the ability to prune obsolete Gaussian components and therefore the size of the network is always within control. CPNN has shown to have better application potentials than BPNN in this research.

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