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Travel speed prediction using fuzzy reasoning

Travel speed prediction using fuzzy reasoning
Travel speed prediction using fuzzy reasoning
The speed prediction algorithm introduced in this paper takes advantage of fuzzy systems that are insensitive to random noise, robust to uncertainties, and transparent to interpretation. The proposed algorithm for outlier detection selects the potential outliers based on the density rather than the deviation adopted in conventional approaches. To evaluate the developed system, a seris of experiments conducted on the real world data. The result of the comparison performed to evaluate the outliler detection method proposed reveals the benefit from the consideration of density. The cross validation results indicate the effectiveness of the fuzzy inference system developed.
0302-9743
446-455
Wang, Yang
7bfb9a35-82f9-4580-a448-c4fbffc7959c
Liu, Honghai
c4f80891-48ff-46df-a35f-3b74e6f35295
Beullens, Patrick
893ad2e2-0617-47d6-910b-3d5f81964a9c
Brown, David
68e8f8ee-6aaf-45e4-9aee-7f76e39ddefe
Wang, Yang
7bfb9a35-82f9-4580-a448-c4fbffc7959c
Liu, Honghai
c4f80891-48ff-46df-a35f-3b74e6f35295
Beullens, Patrick
893ad2e2-0617-47d6-910b-3d5f81964a9c
Brown, David
68e8f8ee-6aaf-45e4-9aee-7f76e39ddefe

Wang, Yang, Liu, Honghai, Beullens, Patrick and Brown, David (2008) Travel speed prediction using fuzzy reasoning. [in special issue: Intelligent Robotics and Applications] Lecture Notes in Computer Science, 5314/2008, 446-455. (doi:10.1007/978-3-540-88513-9_48).

Record type: Article

Abstract

The speed prediction algorithm introduced in this paper takes advantage of fuzzy systems that are insensitive to random noise, robust to uncertainties, and transparent to interpretation. The proposed algorithm for outlier detection selects the potential outliers based on the density rather than the deviation adopted in conventional approaches. To evaluate the developed system, a seris of experiments conducted on the real world data. The result of the comparison performed to evaluate the outliler detection method proposed reveals the benefit from the consideration of density. The cross validation results indicate the effectiveness of the fuzzy inference system developed.

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More information

Published date: 2008
Organisations: Operational Research

Identifiers

Local EPrints ID: 344796
URI: http://eprints.soton.ac.uk/id/eprint/344796
ISSN: 0302-9743
PURE UUID: 9c582367-f592-444b-aec4-474b32990330
ORCID for Patrick Beullens: ORCID iD orcid.org/0000-0001-6156-3550

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Date deposited: 02 Nov 2012 16:55
Last modified: 16 Oct 2019 00:34

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