Doctoral Dissertations

Author

Wasim Kayani

Keywords and Phrases

Clustering; Functional Forecast; Mathematical Morphology; Traffic Flow Forecast; Traffic Patterns

Abstract

This dissertation proposes a methodology for traffic flow pattern analysis, its validation, and forecasting. The shape of the daily traffic flows are directly related to the commuter’s traffic behavior which merit analysis based on their shape characteristics. As a departure from the traditional approaches, this research proposed a methodology based on shape for traffic flow analysis. Specifically, Granulometric Size Distributions (GSDs) were used to achieve classification of daily traffic flow patterns. A mathematical morphology method was used that allows the clustering of shapes. The proposed methodology leads to discovery of interesting daily traffic phenomena such as five normal daily traffic shapes beside abnormal shapes representing accidents, congestion behavior, peak time fluctuations, and malfunctioning sensors.

To ascertain the significance of shape in traffic analysis, the proposed methodology was validated through a comparative classification analysis of the original data and GSD transformed data using the Back Prorogation Neural Network (BPNN). Results demonstrated that through shape based clustering more appropriate grouping can be accomplished that can result in better estimates of model parameters.

Lastly, a functional time series approach was proposed to forecast traffic flow for short and medium-term horizons. It is based on functional principal components decomposition to forecast three different traffic scenarios. Real-time forecast scenarios of partially observed traffic profiles through Penalized Least squares (PLS) technique were also demonstrated. Functional methods outperform the conventional ARIMA model in both short and medium-term forecast horizons. In addition, performance of functional methods in forecasting beyond one hour was also found to be robust and consistent. "--Abstract, page iii.

Advisor(s)

Guardiola, Ivan

Committee Member(s)

Samaranayake, V. A.
Mendoza, Cesar
ElGawady, Mohamed
Rogers, J. David

Department(s)

Civil, Architectural and Environmental Engineering

Degree Name

Ph. D. in Civil Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2015

Pagination

ix, 79 pages

Note about bibliography

Includes bibliographic references (pages 72-78).

Rights

© 2015 Wasim Kayani, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Library of Congress Subject Headings

Traffic flow -- Forecasting -- Computer programs
Traffic patterns -- Analysis
Traffic flow -- Simulation methods
Traffic engineering

Thesis Number

T 10757

Electronic OCLC #

921176645

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