Forecasting bike sharing demand using fuzzy inference mechanism

Salaken, Syed Moshfeq, Hosen, Mohammad Anwar, Khosravi, Abbas and Nahavandi, Saeid 2015, Forecasting bike sharing demand using fuzzy inference mechanism, in ICONIP 2015 : Proceedings of the 22nd International Conference on Neural Information Processing, Springer, New York, N.Y., pp. 567-574, doi: 10.1007/978-3-319-26555-164.

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Title Forecasting bike sharing demand using fuzzy inference mechanism
Author(s) Salaken, Syed Moshfeq
Hosen, Mohammad AnwarORCID iD for Hosen, Mohammad Anwar orcid.org/0000-0001-8282-3198
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Conference name Neural Information Processing International Conference ( 22nd : 2015 : Istanbul, Turkey)
Conference location Istanbul, Turkey
Conference dates 9-12 Nov. 2015
Title of proceedings ICONIP 2015 : Proceedings of the 22nd International Conference on Neural Information Processing
Publication date 2015
Series Neural Information Processing
Conference series 22nd International Conference, ICONIP 2015, Istanbul, Turkey, November 9-12, 2015, Proceedings Part III
Start page 567
End page 574
Total pages 8
Publisher Springer
Place of publication New York, N.Y.
Summary Forecasting bike sharing demand is of paramount importance for management of fleet in city level. Rapidly changing demand in this service is due to a number of factors including workday, weekend, holiday and weather condition. These nonlinear dependencies make the prediction a difficult task. This work shows that type-1 and type-2 fuzzy inference-based prediction mechanisms can capture this highly variable trend with good accuracy. Wang-Mendel rule generation method is utilized to generate rule base and then only current information like date related information and weather condition is used to forecast bike share demand at any given point in future. Simulation results reveal that fuzzy inference predictors can potentially outperform traditional feed forward neural network in terms of prediction accuracy.
ISBN 9783319265544
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-26555-164
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
08 Information And Computing Sciences
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082482

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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