Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0968-090X(01)00007-9
Title: Development and adaptation of constructive probabilistic neural network in freeway incident detection
Authors: Jin, X.
Cheu, R.L. 
Srinivasan, D. 
Keywords: Constructive probabilistic neural network
Dynamic decay adjustment algorithm
Incident detection
Model adaptation
Network pruning
Issue Date: Apr-2002
Source: Jin, X., Cheu, R.L., Srinivasan, D. (2002-04). Development and adaptation of constructive probabilistic neural network in freeway incident detection. Transportation Research Part C: Emerging Technologies 10 (2) : 121-147. ScholarBank@NUS Repository. https://doi.org/10.1016/S0968-090X(01)00007-9
Abstract: This paper investigates 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. © 2002 Elsevier Science Ltd. All rights reserved.
Source Title: Transportation Research Part C: Emerging Technologies
URI: http://scholarbank.nus.edu.sg/handle/10635/50690
ISSN: 0968090X
DOI: 10.1016/S0968-090X(01)00007-9
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