Reduced multivariate polynomial-based neural network for automated traffic incident detection.
This paper proposes a neural network model based on reduced multivariate polynomial pattern classifier for application in freeway incident detection. The reduced multivariate model (RM) is a recently proposed classifier model which is easy to implement and analyze, and has been observed to efficiently capture the nonlinear input-output relationships in many classification applications. Since the freeway incident detection can be treated as a two-category pattern classification problem, the reduced multivariate polynomial model is particularly suitable for this incident detection application. Both Recursive Singular Value Decomposition (RSVD)-based and gradient descent-based least square estimators were adopted to learn the RM classifier in this work. The comparison of results obtained with those from several other classification strategies demonstrates the efficacy of the proposed model for traffic incident detection.
Srinivasan
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Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576, Singapore. dipti@nus.edu.sg
Sharma
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Toh
K A
KA
D000063:Accidents, Traffic; D001185:Artificial Intelligence; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D016018:Least-Squares Analysis; D015233:Models, Statistical; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted