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Aug 30, 2023
Driving Style Recognition of Taxi Drivers Based on Naturalistic Driving Data
Authors: Pengwei Yan [email protected], Xiaohua Zhao [email protected], Ying Yao [email protected], and Xiaogang Ma [email protected]Author Affiliations
Publication: CICTP 2023
ABSTRACT
To address dynamic and accurate evaluation of driving style in taxi driver safety management, this paper establishes a dynamic recognition model of driving style using a combination of unsupervised clustering and supervised classification. Based on the natural driving data of 124 taxis in Beijing for one month, the concept of vehicle operation information entropy is proposed. The driver’s safety style is clustered by the K-means++ clustering algorithm to obtain three driving styles: “cautious,” “aggressive,” and “normal.” The dynamic recognition model of driving style is established using Gradient Boosting Decision Tree (GBDT), support vector machine (SVM), and logistic regression (LR), and the effects of models are evaluated and compared. Results show that the GBDT algorithm has a better classification effect and stronger applicability to low-dimensional data. This model accurately identifies aggressive drivers. The research results provide support for drivers’ safety management and targeted intervention in the taxi industry.
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Published online: Aug 30, 2023
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ASCE Technical Topics:
- Algorithms
- Artificial intelligence and machine learning
- Computer programming
- Computing in civil engineering
- Driver behavior
- Dynamic models
- Engineering fundamentals
- Infrastructure
- Mathematics
- Model accuracy
- Models (by type)
- Public transportation
- Taxis
- Traffic analysis
- Traffic engineering
- Traffic management
- Traffic safety
- Transportation engineering
Authors
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1Beijing Engineering Research Center of Urban Transportation Operation Guarantee, College of Metropolitan Transportation, Beijing Univ. of Technology, Chaoyang District, Beijing, PR China. Email: [email protected]
2Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing Univ. of Technology, Chaoyang District, Beijing, PR China. Email: [email protected]
3Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing Univ. of Technology, Chaoyang District, Beijing, PR China. Email: [email protected]
4Shandong Hi-Speed Co. Ltd., Jinan, PR 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.