NDLI: Modeling and Forecasting of Urban Logistics Demand Based on Support Vector Machine

Content Provider	IEEE Xplore Digital Library
Author	Meijuan Gao Qian Feng
Copyright Year	2008
Abstract	Because logistics system was an uncertain, nonlinear, dynamic and complicated system, it was difficult to describe it by traditional methods. The support vector machine (SVM) has the ability of strong nonlinear function approach, it has the ability of strong generalization and it also has the feature of global optimization. In this paper, a modeling and forecasting method of urban logistics demand based on regression SVM is presented. The SVM network structure for forecasting of urban logistics is established. Moreover, we propose a self-adaptive parameter adjust iterative algorithm to confirm SVM parameters, thereby enhancing the convergence rate and the forecasting accuracy. With the ability of strong self-learning and well generalization of SVM, the modeling and forecasting method can truly forecast the logistics demand by learning the index information of affect logistics demand. The actual forecasting results show that this method is feasible and effective.
Starting Page	793
Ending Page	796
File Size	273944
Page Count	4
File Format	PDF
ISBN	9780769535432
DOI	10.1109/WKDD.2009.211
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-01-23
Publisher Place	Russia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Industrial economics Process planning Demand forecasting Predictive models Environmental economics demand Convergence Support vector machines support vector machine urban logistics modeling and forecasting Cities and towns Economic forecasting Logistics
Content Type	Text
Resource Type	Article

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