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

来源：

2018 INTERNATIONAL CONFERENCE ON COMPUTING AND BIG DATA (ICCBD 2018) | 2018年

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

页码：83 / 86

页数：4

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