A Hybrid of Computational Intelligence Techniques for Shape Analysis of Traffic Flow Curves


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 parameterizations 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 (BPNN) is used to validate the classification achieved through the employment of Mathematical Morphology (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.

Meeting Name

Transportation Research Board 94th Annual Meeting (2015: Jan. 11-15, Washington, DC)


Engineering Management and Systems Engineering

Second Department

Electrical and Computer Engineering

Research Center/Lab(s)

Center for High Performance Computing Research

Second Research Center/Lab

Intelligent Systems Center


This paper was sponsored by TRB committee AHB45 Traffic Flow Theory and Characteristics.

Keywords and Phrases

Backpropagation; Neural networks; Traffic data; Traffic flow; Validation

Document Type

Article - Conference proceedings

Document Version


File Type





© 2015 Transportation Research Board, All rights reserved.

Publication Date

01 Jan 2015