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Forecasting bike sharing demand using fuzzy inference mechanism
conference contribution
posted on 2015-01-01, 00:00 authored by Syed Moshfeq Salaken, Anwar HosenAnwar Hosen, Abbas KhosraviAbbas Khosravi, Saeid NahavandiForecasting 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.
History
Event
Neural Information Processing International Conference ( 22nd : 2015 : Istanbul, Turkey)Volume
9491Series
Neural Information ProcessingPagination
567 - 574Publisher
SpringerLocation
Istanbul, TurkeyPlace of publication
New York, N.Y.Publisher DOI
Start date
2015-11-09End date
2015-11-12ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319265544Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2015, SpringerTitle of proceedings
ICONIP 2015 : Proceedings of the 22nd International Conference on Neural Information ProcessingUsage metrics
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