Travel Time Prediction on Urban Networks Based on Combining Rough Set with Support Vector Machine

被引：0

作者：

Chen, Yao ^{[1
]}

van Zuylen, Henk J. ^{[2
]}

Yan Qipeng ^{[1
]}

机构：

[1] SW Jiaotong Univ, Coll Traff & Transportat, Chengdu 610031, Peoples R China

[2] Delft Univ Technol, Fac Civil Engn & Geosci, NL-2600 AA Delft, Netherlands

来源：

PROCEEDINGS OF 2010 INTERNATIONAL CONFERENCE ON LOGISTICS SYSTEMS AND INTELLIGENT MANAGEMENT, VOLS 1-3 | 2010年

关键词：

Travel Time Prediction; Rough Set; Support Vector Machine; Committee; Urban Network;

D O I：

暂无

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Urban travel time prediction is one of interesting topics in current transportation research and practice. In this paper, a new prediction model is proposed which combines rough set with support vector machine. Rough set is used to pre-process the traffic data that is noisy, missing, and inconsistent then deduce some rules for framing support vector machine (SVM) model. When comparing the committee model to the single SVM predictions utilizing real traffic data collected in Chengdu, it is concluded the new approach indeed leads to improved travel time predicting accuracy and velocity.

引用

页码：586 / +

页数：3

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