Chapter
Dec 14, 2023
Research on Abnormal Driving Behavior Identification of Freeway Based on XGBoost Method
Authors: Kun Liu [email protected], Chuanjiao Sun [email protected], and Chunyang Li [email protected]Author Affiliations
Publication: CICTP 2023
ABSTRACT
Based on the vehicle track information on expressway collected by the radar on the roadside, the information on abnormal driving behavior of vehicles is analyzed. The XGBoost method is used to classify and analyze abnormal driving behaviors such as rapid acceleration, rapid deceleration, speeding, and emergency stop. From the result, we can see the accuracy rate in training set can reach 81.23%. Different methods such as decision tree, SVM, random forest, and GBDT are also used to analyze the same abnormal driving; the accuracy is not above 81%. The method of expressway classification warning method can provide technical support for expressway traffic safety management.
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Published In
CICTP 2023
Pages: 284 - 289
History
Published online: Dec 14, 2023
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ASCE Technical Topics:
- Business management
- Data collection
- Driver behavior
- Engineering fundamentals
- Highway and road management
- Highway transportation
- Highways and roads
- Infrastructure
- Management methods
- Methodology (by type)
- Models (by type)
- Practice and Profession
- Research methods (by type)
- Traffic analysis
- Traffic engineering
- Traffic management
- Traffic models
- Traffic safety
- Transportation engineering
- Vehicles
Authors
Affiliations
1Hong Kong-Zhuhai-Macao Bridge Authority, Zhuhai, Guangdong, China. Email: [email protected]
2Road Safety Research Centre, RIOH, Beijing, China. Email: [email protected]
3Road Safety Research Centre, RIOH, Beijing, China. Email: [email protected]
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.