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Estimation of travel time using fuzzy clustering method

Zheng, P. and McDonald, M. (2009) Estimation of travel time using fuzzy clustering method Institution of Engineering & Technology, Intelligent Transport Systems, 3, (1), pp. 77-86. (doi:10.1049/iet-its:20080021).

Record type: Article

Abstract

A methodology to estimate overall travel time from individual travel time measurements within a time window is presented. To better handle data with complex outlier generation mechanisms, fuzzy clustering techniques have been used to represent relationships between individual travel time data collected within a measuring time window. The data set is considered to be a fuzzy set to which each data point belongs at some degrees of membership. This allows transitions from the main body of data to extreme data points to be treated in a smooth and fuzzy fashion. Two algorithms have been developed based on ‘point’ and ‘line’ fuzzy cluster prototypes. Iterative procedures have been developed to calculate the fuzzy cluster centre and the fuzzy line. A novel estimation method based on time projection of a fuzzy line has been proposed. The method has the advantage of being robust by using a wide time window and the timeliness by employing time projection in resolving the most recent travel time estimation. Unlike deterministic approaches where hard thresholds need to be specified in order to exclude outliers, the proposed methods estimate travel times using all available data and, thus, can be applied in a wide variety of scenarios without fine tuning of the threshold.

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More information

Published date: 2009
Additional Information: The Institution of Engineering and Technology is a new institution which has been formed by joining the IEE (Institution of Electrical Engineers) and the IIE (The Institution of Incorporated Engineers)
Keywords: estimation theory, fuzzy set theory, pattern clustering, road traffic
Organisations: Civil Engineering & the Environment

Identifiers

Local EPrints ID: 74268
URI: http://eprints.soton.ac.uk/id/eprint/74268
ISSN: 1751-956X
PURE UUID: 8ffd0aab-9c1f-405c-9314-03db4a30046d

Catalogue record

Date deposited: 11 Mar 2010
Last modified: 18 Jul 2017 23:46

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