A Study on Correlation of Traffic Accident Tendency with Driver Characters Using In-Depth Traffic Accident Data

Joint Authors

Wu, Hequan
Hu, Lin
Bao, Xingqian
Wu, Wenguang

Source

Journal of Advanced Transportation

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-7, 7 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-06-02

Country of Publication

Egypt

No. of Pages

7

Main Subjects

Civil Engineering

Abstract EN

Traffic accidents are often related to the driver’s driving behavior, which is mainly decided by his or her characters.

In order to explore the correlation of traffic accident risk with driver characters, the age, driving experience, and driving style were statistically analyzed based on the China In-Depth Accident Study (CIDAS) database.

Taking the number of casualties in the accident as evaluation indicators, the grey cluster analysis was used to classify the drivers into four accident risk ranks: low, medium to low, medium to high, and high.

The results show that drivers aged 18–30 years are more likely to induce accidents; drivers with 6–10 years of driving experience have the highest risk to accidents, followed by drivers with 4-5 years of driving experience; and the driving style is also highly correlated with accident risk tendency.

American Psychological Association (APA)

Hu, Lin& Bao, Xingqian& Wu, Hequan& Wu, Wenguang. 2020. A Study on Correlation of Traffic Accident Tendency with Driver Characters Using In-Depth Traffic Accident Data. Journal of Advanced Transportation،Vol. 2020, no. 2020, pp.1-7.
https://search.emarefa.net/detail/BIM-1181063

Modern Language Association (MLA)

Hu, Lin…[et al.]. A Study on Correlation of Traffic Accident Tendency with Driver Characters Using In-Depth Traffic Accident Data. Journal of Advanced Transportation No. 2020 (2020), pp.1-7.
https://search.emarefa.net/detail/BIM-1181063

American Medical Association (AMA)

Hu, Lin& Bao, Xingqian& Wu, Hequan& Wu, Wenguang. A Study on Correlation of Traffic Accident Tendency with Driver Characters Using In-Depth Traffic Accident Data. Journal of Advanced Transportation. 2020. Vol. 2020, no. 2020, pp.1-7.
https://search.emarefa.net/detail/BIM-1181063

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1181063