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

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.