文献类型：期刊文献

英文题名：Peak traffic forecasting using nonparametric approaches

作者：Zhang, Yang[1]; Wang, Meng-Ling[2]

机构：[1] Shanghai Municipal Transportation Information Center, Shanghai 200032, China; [2] Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

年份：2012

卷号：17

期号：1

起止页码：76

外文期刊名：Journal of Shanghai Jiaotong University (Science)

收录：EI(收录号：20120714764516)

语种：英文

外文关键词：Forecasting - Intelligent vehicle highway systems - Vector spaces - Intelligent systems - Real time systems - Support vector machines

摘要：States of traffic situations can be classified into peak and nonpeak periods. The complexity of peak traffic brings more difficulty to forecasting models. Travel time index (TTI) is a fundamental measure in transportation. How to master the characteristics and provide accurate real-time forecasts is essential to intelligent transportation systems (ITS). Cooperating with state space approach, least squares support vector machines (LSSVMs) are investigated to solve such a practical problem in this paper. To the best of our knowledge, it is the first time to apply the technique and analyze the forecast performance in the domain. For comparison purpose, other two nonparametric predictors are selected because of their effectiveness proved in past research. Having good generalization ability and guaranteeing global minima, LS-SVMs perform better than the others. Providing sufficient improvement in stability and robustness reveals that the approach is practically promising. ? 2012 Shanghai Jiaotong University and Springer-Verlag Berlin Heidelberg.