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. Attached Files Name Description MIMEType Size Downloads Title Forecasting bike sharing demand using fuzzy inference mechanism Author(s) Salaken, Syed Moshfeq orcid.org/0000-0001-8632-2665 Hosen, Mohammad Anwar orcid.org/0000-0001-8282-3198 Khosravi, Abbas orcid.org/0000-0001-6927-0744 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 Collections: Centre for Intelligent Systems Research GTP Research Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 31 Mar 2016, 08:07:19 EST Thu, 31 Mar 2016, 08:15:15 EST Thu, 31 Mar 2016, 08:15:59 EST Thu, 31 Mar 2016, 13:36:06 EST Fri, 01 Apr 2016, 04:00:19 EST Fri, 01 Apr 2016, 05:07:58 EST Thu, 05 May 2016, 14:45:51 EST Mon, 18 Jul 2016, 16:04:07 EST Filtered Full Citation counts:   Cited 1 times in TR Web of Science Cited 2 times in Scopus Search Google Scholar Access Statistics: 455 Abstract Views, 4 File Downloads  -  Detailed Statistics Created: Thu, 31 Mar 2016, 13:36:06 EST

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