Technology of Network & Communication
|
2395-2399

Traffic accident prediction approach based on ensemble approach

Shi Xuehuaia
Qi Yonga
Zhang Weibinb
Li Qianmua
a. School of Computer Science & Engineering, b. School of Electronic Engineering & Photoelectric Technology, Nanjing University of Science & Technology, Nanjing 210094, China

Abstract

As the limitations of the single classifier on traffic accident severity, this paper proposed an ensemble approach for improving the prediction performance. It used CNN(convolutional neural network) to extract the features from the spatial dimension, got an ensemble approach with XGBoost and CNN by stacking(multi-level boosting algorithm). The predicting precision of the approach is 91.51% on the validation set. In comparison with the single classification model, the result of the experiment shows a better performance. For providing useful information for reducing the number of traffic accidents and downgrading the severity of traffic accident, the paper gave out a correlation analysis by sorting the features based on the predictions.

Foundation Support

国家重点研发计划政府间国际科技创新合作重点专项资助项目(2016YFE0108000)
江苏省重点研发计划资助项目(BE2017163)
中央高校基本科研业务费专项资金资助项目(30916015104)
中兴合作研究项目(2016ZTE04-11)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.03.0273
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 8
Section: Technology of Network & Communication
Pages: 2395-2399
Serial Number: 1001-3695(2019)08-032-2395-05

Publish History

[2019-08-05] Printed Article

Cite This Article

石雪怀, 戚湧, 张伟斌, 等. 基于组合模型的交通事故严重程度预测方法 [J]. 计算机应用研究, 2019, 36 (8): 2395-2399. (Shi Xuehuai, Qi Yong, Zhang Weibin, et al. Traffic accident prediction approach based on ensemble approach [J]. Application Research of Computers, 2019, 36 (8): 2395-2399. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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