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Application of support vector machines in traffic volume forecast

WANG Ji-sheng, GAO Bao-cheng, SHI Liang-ping (Automation School, Beijing University of Post & Telecommunication, Beijing 100876, China)  
Traffic volume forecast is very important to region traffic plan. A new method based on SVM for the traffic volume forecast is presented. SVM algorithm is based on statistical theory. Analysis of the experimental results proved that the algorithm of could achieve much effective than that of BP neutral network.
【CateGory Index】: TP183
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