This paper highlights and validates the use of shape analysis using Mathematical Morphology tools as a means to develop meaningful clustering of historical data. Furthermore, through clustering more appropriate grouping can be accomplished that can result in the better parameterization or estimation of models. This results in more effective prediction model development. Hence, in an effort to highlight this within the research herein, a Back-Propagation Neural Network is used to validate the classification achieved through the employment of MM tools. Specifically, the Granulometric Size Distribution (GSD) is used to achieve clustering of daily traffic flow patterns based solely on their shape. To ascertain the significance of shape in traffic analysis, a comparative classification analysis of original data and GSD transformed data is carried out. The results demonstrate the significance of functional shape in traffic analysis. In addition, the results validate the need for clustering prior to prediction. It is determined that a span of two through four years of traffic data is found sufficient for training to produce satisfactory BPNN performance.
W. I. Kayani et al., "Shape Analysis of Traffic Flow Curves using a Hybrid Computational Analysis," Procedia Computer Science, vol. 95, pp. 457-466, Elsevier B.V., Nov 2016.
The definitive version is available at https://doi.org/10.1016/j.procs.2016.09.320
Complex Adaptive Systems, 2016 (2016: Nov. 2-4, Los Angeles, CA)
Engineering Management and Systems Engineering
Electrical and Computer Engineering
Center for High Performance Computing Research
Keywords and Phrases
Clustering; Functional data; Granulometric Size Distribution (GSD); Traffic flow
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 2016 The Authors, All rights reserved.