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The Application of Fuzzy Interval Correlation
Evaluating the Relationship Between
Transportation Engineering and Air Pollution
Yu-Ting Cheng and Chih-Ching Yang
Abstract
To evaluate a proper correlation coefficient with fuzzy data is an
important topic in the transportation engineering, especially when the data
illustrate uncertain, inconsistent, and incomplete type. In general, we use Pearson's
correlation coefficient to measure the correlation of data with real values. However,
when the data are composed of fuzzy interval values, it is not feasible to use such
a classical approach to determine the correlation coefficient. This study proposes
the computation of fuzzy correlation coefficient with fuzzy interval data. Empirical
studies are employed to explain the application for evaluating fuzzy correlation.
More related practical phenomena can be explained using the application of fuzzy
correlation.
Keywords
Fuzzy correlation Fuzzy interval data Evaluation Air pollution
Transportation engineering
1
Introduction
In classical statistics, the two-valued logic will be reflected. Investigating the
phenomena of nature, socials, or economics, fuzzy logic should be applied to
account for the full range of possible values. Since
Zedah
(
1965
) developed fuzzy
set theory, its applications have been extended to traditional statistical inferences
and methods in social or engineering or economics, including medical diagnosis or
stock investment systems. For example, a continuing series of studies displayed
approximate reasoning methods for econometrics (
Lowen 1990
;
Ruspini 1991
;
Dubois and Prade 1991
) and a fuzzy time series model to overcome the bias of
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