DocumentCode :
2073588
Title :
A real time neural network learning approach for traffic forecasting
Author :
Zhu, Jiasong ; Zheng, Hao
Author_Institution :
Dept. of Transp. Eng., Shenzhen Univ., Shenzhen, China
fYear :
2011
fDate :
16-18 Dec. 2011
Firstpage :
1215
Lastpage :
1219
Abstract :
Reliable and accurate short-term traffic forecasting system is crucial in supporting any Intelligent Transportation System. The past two decades have witnessed many forecasting models being developed, yet none of them could consistently outperform the others under various traffic conditions. To deal with the nonlinearity and non-stationarity of dynamic traffic process, a real time neural network learning approach is taken and a traffic flow mode based forecasting method is presented. Results obtained from case study indicate the proposed approach can enhance adaptability of short-term traffic forecasting and has the advantages of better flexibility and transferability.
Keywords :
automated highways; learning (artificial intelligence); neural nets; traffic engineering computing; dynamic traffic process; intelligent transportation system; real time neural network learning approach; short-term traffic forecasting; Accuracy; Adaptation models; Forecasting; Neural networks; Predictive models; Real time systems; Transportation; flow modes; real time learning; traffic forecasting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Transportation, Mechanical, and Electrical Engineering (TMEE), 2011 International Conference on
Conference_Location :
Changchun
Print_ISBN :
978-1-4577-1700-0
Type :
conf
DOI :
10.1109/TMEE.2011.6199424
Filename :
6199424
Link To Document :
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