Risk-Quantification Method for Car-Following Behavior Considering Driving-Style Propensity
To systematically study the influence of a propensity for a particular driving style on car-following risk, a safety potential field risk-quantification method that considers driving style is proposed. First, we classify driving behaviors and construct a field-based safety potential car-following mo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/5/1746 |
| _version_ | 1797264829693558784 |
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| author | Kedong Wang Dayi Qu Yufeng Yang Shouchen Dai Tao Wang |
| author_facet | Kedong Wang Dayi Qu Yufeng Yang Shouchen Dai Tao Wang |
| author_sort | Kedong Wang |
| collection | DOAJ |
| description | To systematically study the influence of a propensity for a particular driving style on car-following risk, a safety potential field risk-quantification method that considers driving style is proposed. First, we classify driving behaviors and construct a field-based safety potential car-following model via analogy with intermolecular interactions; second, we establish a risk-quantification model by considering driving style, risk exposure, and risk severity and classify the consequent risk into four levels, high risk, medium risk, low risk, and safe, using the fuzzy C-means algorithm. Finally, we predict the car-following risk using the LightGBM algorithm in real time. The experimental results show that the LightGBM algorithm can recognize up to 86% of medium–high risk levels compared to the Decision Tree and Random Forest Algorithms. It can achieve effective prediction of car-following risk, which provides sufficient warning information to drivers and helps improve the overall safety of vehicle operation. |
| first_indexed | 2024-04-25T00:35:07Z |
| format | Article |
| id | doaj.art-222e0c15faca42d4affd41bca9e9aada |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-25T00:35:07Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-222e0c15faca42d4affd41bca9e9aada2024-03-12T16:38:46ZengMDPI AGApplied Sciences2076-34172024-02-01145174610.3390/app14051746Risk-Quantification Method for Car-Following Behavior Considering Driving-Style PropensityKedong Wang0Dayi Qu1Yufeng Yang2Shouchen Dai3Tao Wang4School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, ChinaSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, ChinaSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, ChinaSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, ChinaSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, ChinaTo systematically study the influence of a propensity for a particular driving style on car-following risk, a safety potential field risk-quantification method that considers driving style is proposed. First, we classify driving behaviors and construct a field-based safety potential car-following model via analogy with intermolecular interactions; second, we establish a risk-quantification model by considering driving style, risk exposure, and risk severity and classify the consequent risk into four levels, high risk, medium risk, low risk, and safe, using the fuzzy C-means algorithm. Finally, we predict the car-following risk using the LightGBM algorithm in real time. The experimental results show that the LightGBM algorithm can recognize up to 86% of medium–high risk levels compared to the Decision Tree and Random Forest Algorithms. It can achieve effective prediction of car-following risk, which provides sufficient warning information to drivers and helps improve the overall safety of vehicle operation.https://www.mdpi.com/2076-3417/14/5/1746traffic safetydriving style propensitysafety potential fieldcar followingrisk quantification |
| spellingShingle | Kedong Wang Dayi Qu Yufeng Yang Shouchen Dai Tao Wang Risk-Quantification Method for Car-Following Behavior Considering Driving-Style Propensity Applied Sciences traffic safety driving style propensity safety potential field car following risk quantification |
| title | Risk-Quantification Method for Car-Following Behavior Considering Driving-Style Propensity |
| title_full | Risk-Quantification Method for Car-Following Behavior Considering Driving-Style Propensity |
| title_fullStr | Risk-Quantification Method for Car-Following Behavior Considering Driving-Style Propensity |
| title_full_unstemmed | Risk-Quantification Method for Car-Following Behavior Considering Driving-Style Propensity |
| title_short | Risk-Quantification Method for Car-Following Behavior Considering Driving-Style Propensity |
| title_sort | risk quantification method for car following behavior considering driving style propensity |
| topic | traffic safety driving style propensity safety potential field car following risk quantification |
| url | https://www.mdpi.com/2076-3417/14/5/1746 |
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