Please use this identifier to cite or link to this item:
|Title:||An application of intelligent noise filtering techniques in demand forecasting for carsharing systems||Authors:||Khoo, H.L.
|Issue Date:||2007||Citation:||Khoo, H.L.,Fung, C.-H.,Xu, J.,Lee, D.-H.,Meng, Q.,Lim, J.S. (2007). An application of intelligent noise filtering techniques in demand forecasting for carsharing systems. 14th World Congress on Intelligent Transport Systems, ITS 2007 7 : 5602-5613. ScholarBank@NUS Repository.||Abstract:||This study deals with demand forecasting for the carsharing system with multi-station and flexible returning station and time. Neural network is employed as the simulation model to forecast the demand at each station at certain time period in the system. This study distinguished from the previous study because it introduces intelligent filtering techniques as a tool to remove the noise of the data before it is fed into the simulation model. Two filtering techniques have been tested, namely outlier analysis and cluster analysis. Results show that this extra procedure helps to enhance the forecasting model as well as improve the forecasting accuracy.||Source Title:||14th World Congress on Intelligent Transport Systems, ITS 2007||URI:||http://scholarbank.nus.edu.sg/handle/10635/83461||ISBN:||9781617387777|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on May 21, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.