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
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.
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