Road Safety Estimation Utilizing Big and Heterogeneous Vehicle Recorder Data

被引：0

作者：

Toyoda, Masashi ^{[1
]}

Yokoyama, Daisaku ^{[1
]}

Komiyama, Junpei ^{[1
]}

Itoh, Masahiko ^{[1
]}

机构：

[1] Univ Tokyo, Inst Ind Sci, Meguro Ku, 4-6-1 Komaba, Tokyo 1538505, Japan

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2017年

关键词：

mobility big data; vehicle recorder; dash cam; road safety assesment; traffic accident; PERFORMANCE FUNCTIONS; VARIABLES;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a method for estimating road safety using large-scale and heterogeneous vehicle recorder data. Our assamption in this study is that occurrences of traffic accidents on crossings could be estimated by their road shape, driving behaviors over them, and photos taken by dash cams. Utilizing 3 years dataset from 1,500 drivers in a fleet delivery company, we try to detect risky crossings based on supervised machine learning. Results show that our method provides good performance, and can extract potentially risky crossings.

引用

页码：4841 / 4842

页数：2

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