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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 82
Edited by: B.H.V. Topping
Paper 48

Traffic Pattern Recognition using an Active Learning Neural Network and Principal Components Analysis

L. Yan+, M. Fraser+, K. Oliver+, A. Elgamal+ J.P. Conte+ and T. Fountain*

+Department of Structural Engineering
*San Diego Supercomputer Center
University of California, San Diego, United States of America

Full Bibliographic Reference for this paper
L. Yan, M. Fraser, K. Oliver, A. Elgamal J.P. Conte, T. Fountain, "Traffic Pattern Recognition using an Active Learning Neural Network and Principal Components Analysis", in B.H.V. Topping, (Editor), "Proceedings of the Eighth International Conference on the Application of Artificial Intelligence to Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 48, 2005. doi:10.4203/ccp.82.48
Keywords: structural health monitoring, traffic pattern recognition, neural network, principal components analysis, active learning.

Information Technologies (IT) are increasingly allowing for advances in monitoring of civil infrastructure systems [1]. An integrated framework encompasses data acquisition, database archiving, and model-free/model-based system-identification/data-mining techniques, towards the development of practical decision-making tools [2,3].

In order to develop such an integrated framework, three composite bridge decks on the UCSD (University of California, San Diego) campus, instrumented with heterogeneous sensors [4] including a video camera [5], are employed as a testbed for a pilot study. Live data from selected sensors and the video feed are now available on-line on a 24/7 basis over a web-site for worldwide access at

As an important element in the integrated framework, a traffic pattern recognition system is developed in this paper. Using features extracted by the Principal Components Analysis (PCA) technique [6], a neural network [7] is employed in this system to identify the classes of crossing vehicles based on changes in traffic-induced strain response. For this purpose, neural network training is accomplished in a supervised-learning mode [7], which requires a large number of labelled patterns. Considering that data labelling is labor-intensive and time-consuming, an active learning procedure [8,9] is proposed to provide a more efficient solution for practical implementation.

Using the real data collected from the UCSD bridge deck testbed, comparison studies were performed to test and validate the proposed approach. Final results show that a neural network involving PCA is a feasible and efficient solution for strain-based vehicle identification. In addition, the time, labor, and expense for data labelling can be significantly reduced by taking advantage of the developed active learning procedure.

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