Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling

被引：169
|
作者：
Moretti, Fabio [1 ,2 ]
Pizzuti, Stefano [1 ,2 ]
Panzieri, Stefano [2 ]
Annunziato, Mauro [1 ]
机构：
[1] ENEA, Kista, Sweden
[2] Univ Roma Tre, Comp Sci & Automat Dept, Rome, Italy
来源：
NEUROCOMPUTING | 2015年 / 167卷
关键词：
Traffic flow forecasting; Ensembling; Bagging; Neural networks; PREDICTION;
D O I：
10.1016/j.neucom.2014.08.100
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper we show a hybrid modeling approach which combines Artificial Neural Networks and a simple statistical approach in order to provide a one hour forecast of urban traffic flow rates. Experimentation has been carried out on three different classes of real streets and results show that the proposed approach outperforms the best of the methods it puts together. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码：3 / 7
页数：5
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