Incident Duration Model on Urban Freeways Based on Classification and Regression Tree

被引：8

作者：

Zhao Xiaoqiang ^{[1
]}

Li Ruimin ^{[1
]}

Yu Xinxin ^{[1
]}

机构：

[1] Tsinghua Univ, Inst Transportat Engn, Beijing 100084, Peoples R China

来源：

ICICTA: 2009 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION, VOL III, PROCEEDINGS | 2009年

关键词：

Decision tree; Classification and regression tree; Incident duration; Mutiple linear regression;

D O I：

10.1109/ICICTA.2009.616

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Effective incident management requires accurate prediction of incident duration. In this paper, Classification and Regression Tree (CART) is employed to model the incident duration. All 65000 incident records from Beijing Transportation Management Bureau are used for model establishment and another 8000 records for validation. The average relative error of the CART model is 29.5197%. It shows that the reliability of the model is quite satisfactory. The average relative error of the prediction on different ring roads of Beijing is approximately the same.

引用

页码：625 / 628

页数：4

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