Does Information on Weather Affect the Performance of Short-Term Traffic Forecasting Models?

被引：30

作者：

Tsirigotis L. ^{[1
]}

Vlahogianni E.I. ^{[1
]}

Karlaftis M.G. ^{[1
]}

机构：

[1] Department of Transportation Planning and Engineering, School of Civil Engineering, National Technical University of Athens, Greece, 157 73 Athens

来源：

International Journal of Intelligent Transportation Systems Research | 2012年 / 10卷 / 01期

关键词：

Exogenous variables; Short-term speed forecasting; Time-series analysis;

D O I：

10.1007/s13177-011-0037-x

中图分类号：

学科分类号：

摘要：

Although weather, traffic mix and speed variability across lanes are largely considered as significant determinants of traffic flow characteristics on freeways, they have not been incorporated into short-term traffic forecasting models. We evaluate the effects of weather and traffic mix on the predictability of traffic speed using several vector autoregressive moving average models with exogenous variables. Results indicate that including exogenous variables in the forecasting models only marginally improves their prediction performance, while modeling innovations such as Vector and Bayesian estimation improves the models significantly. © 2011 Springer Science+Business Media, LLC.

引用

页码：1 / 10

页数：9

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