Traffic Volume Prediction on Busy Road Junctions

被引:0
|
作者
Wang, Yubo [1 ]
Xu, Ningwei [1 ]
Wang, Kaiyu [1 ]
Li, Xiangru [1 ]
Mu, Lintao [1 ]
机构
[1] 4699 Qianjin Rd, Changchun, Jilin, Peoples R China
关键词
Traffic Volume; SARIMA; Random Forest; GBDT;
D O I
10.1145/3277104.3277116
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Traffic volume prediction has been a hot spot of the realm of data mining owing to its scientific nature and practical value. The prediction needs an integrated model of linear regression, SARIMA, random forest, KNN, GBDT and the method of weight fusion. This new prediction strategy solves the problem of big forecasting bias produced by a single model. The test result of real data demonstrates that this algorithm improves the accuracy of traffic volume prediction in complex crossings.
引用
收藏
页码:83 / 86
页数:4
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