Peak Traffic Forecasting Using Nonparametric Approaches

doi:10.1007/s12204-012-1232-x

上海交通大学学报（英文版） ›› 2012, Vol. 17 ›› Issue (1): 76-081.doi: 10.1007/s12204-012-1232-x

Peak Traffic Forecasting Using Nonparametric Approaches

ZHANG Yang (张扬), WANG Meng-ling (王梦灵)

(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)

收稿日期:2011-05-12 出版日期:2012-02-29 发布日期:2012-03-21
通讯作者: ZHANG Yang (张扬), E-mail: china.zhangyang@gmail.com

Peak Traffic Forecasting Using Nonparametric Approaches

ZHANG Yang (张扬), WANG Meng-ling (王梦灵)

(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)

Received:2011-05-12 Online:2012-02-29 Published:2012-03-21
Contact: ZHANG Yang (张扬), E-mail: china.zhangyang@gmail.com

摘要/Abstract

摘要： Abstract: 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 (LS-SVMs) 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.

关键词: traffic forecasting, peak traffic, nonparametric Techniques

Abstract: Abstract: 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 (LS-SVMs) 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.

Key words: traffic forecasting, peak traffic, nonparametric Techniques

中图分类号:

U491.1

ZHANG Yang (张扬), WANG Meng-ling (王梦灵). Peak Traffic Forecasting Using Nonparametric Approaches[J]. 上海交通大学学报（英文版）, 2012, 17(1): 76-081.

ZHANG Yang (张扬), WANG Meng-ling (王梦灵). Peak Traffic Forecasting Using Nonparametric Approaches[J]. Journal of shanghai Jiaotong University (Science), 2012, 17(1): 76-081.

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Peak Traffic Forecasting Using Nonparametric Approaches

Peak Traffic Forecasting Using Nonparametric Approaches

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