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.
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
2008
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, .
(doi:10.1007/978-3-540-88513-9_48).
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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Published date: 2008
Organisations:
Operational Research
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Local EPrints ID: 344796
URI: http://eprints.soton.ac.uk/id/eprint/344796
ISSN: 0302-9743
PURE UUID: 9c582367-f592-444b-aec4-474b32990330
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Date deposited: 02 Nov 2012 16:55
Last modified: 15 Mar 2024 03:32
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Author:
Yang Wang
Author:
Honghai Liu
Author:
David Brown
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