Classification and Evaluation of Driving Behavior Safety Levels: A Driving Simulation Study
The road traffic safety situation is severe worldwide and exploring driving behavior is a research hotspot since it is the main factor causing road accidents. However, there are few studies investigating how to evaluate real-time traffic safety of driving behavior and the number of driving behavior...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9705507/ |
| _version_ | 1828084140653674496 |
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| author | Kui Yang Christelle Al Haddad George Yannis Constantinos Antoniou |
| author_facet | Kui Yang Christelle Al Haddad George Yannis Constantinos Antoniou |
| author_sort | Kui Yang |
| collection | DOAJ |
| description | The road traffic safety situation is severe worldwide and exploring driving behavior is a research hotspot since it is the main factor causing road accidents. However, there are few studies investigating how to evaluate real-time traffic safety of driving behavior and the number of driving behavior safety levels has not yet been thoroughly explored. This paper aims to propose a framework of real-time driving behavior safety level classification and evaluation, which was validated by a case study of driving simulation experiments. The proposed methodology focuses on determining the optimal aggregation time interval, finding the optimal number of safety levels for driving behavior, classifying the safety levels, and evaluating the driving safety levels in real time. An improved cross-validation mean square error model based on driver behavior vectors was proposed to determine the optimal aggregation time interval, which was found to be 1s. Three clustering techniques were applied, i.e., k-means clustering, hierarchical clustering and model-based clustering. The optimal number of clusters was found to be three. Support vector machines, decision trees and naïve Bayes classifiers were then developed as classification models. The accuracy of the combination of k-means clustering and decision trees proved to be the best with three clusters. |
| first_indexed | 2024-04-11T04:19:20Z |
| format | Article |
| id | doaj.art-4b1a2c3f029548e4bf164c02193be33c |
| institution | Directory Open Access Journal |
| issn | 2687-7813 |
| language | English |
| last_indexed | 2024-04-11T04:19:20Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj.art-4b1a2c3f029548e4bf164c02193be33c2022-12-31T00:02:08ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132022-01-01311112510.1109/OJITS.2022.31494749705507Classification and Evaluation of Driving Behavior Safety Levels: A Driving Simulation StudyKui Yang0https://orcid.org/0000-0002-0299-4729Christelle Al Haddad1https://orcid.org/0000-0003-3619-2748George Yannis2Constantinos Antoniou3https://orcid.org/0000-0003-0203-9542Chair of Transportation Systems Engineering, TUM School of Engineering and Design, Technical University of Munich, Munich, GermanyChair of Transportation Systems Engineering, TUM School of Engineering and Design, Technical University of Munich, Munich, GermanyDepartment of Transportation Planning and Engineering, School of Civil Engineering, National Technical University of Athens, Athens, GreeceChair of Transportation Systems Engineering, TUM School of Engineering and Design, Technical University of Munich, Munich, GermanyThe road traffic safety situation is severe worldwide and exploring driving behavior is a research hotspot since it is the main factor causing road accidents. However, there are few studies investigating how to evaluate real-time traffic safety of driving behavior and the number of driving behavior safety levels has not yet been thoroughly explored. This paper aims to propose a framework of real-time driving behavior safety level classification and evaluation, which was validated by a case study of driving simulation experiments. The proposed methodology focuses on determining the optimal aggregation time interval, finding the optimal number of safety levels for driving behavior, classifying the safety levels, and evaluating the driving safety levels in real time. An improved cross-validation mean square error model based on driver behavior vectors was proposed to determine the optimal aggregation time interval, which was found to be 1s. Three clustering techniques were applied, i.e., k-means clustering, hierarchical clustering and model-based clustering. The optimal number of clusters was found to be three. Support vector machines, decision trees and naïve Bayes classifiers were then developed as classification models. The accuracy of the combination of k-means clustering and decision trees proved to be the best with three clusters.https://ieeexplore.ieee.org/document/9705507/Driving behavior safety levelsdriving simulationclusteringsupport vector machinedecision tree |
| spellingShingle | Kui Yang Christelle Al Haddad George Yannis Constantinos Antoniou Classification and Evaluation of Driving Behavior Safety Levels: A Driving Simulation Study IEEE Open Journal of Intelligent Transportation Systems Driving behavior safety levels driving simulation clustering support vector machine decision tree |
| title | Classification and Evaluation of Driving Behavior Safety Levels: A Driving Simulation Study |
| title_full | Classification and Evaluation of Driving Behavior Safety Levels: A Driving Simulation Study |
| title_fullStr | Classification and Evaluation of Driving Behavior Safety Levels: A Driving Simulation Study |
| title_full_unstemmed | Classification and Evaluation of Driving Behavior Safety Levels: A Driving Simulation Study |
| title_short | Classification and Evaluation of Driving Behavior Safety Levels: A Driving Simulation Study |
| title_sort | classification and evaluation of driving behavior safety levels a driving simulation study |
| topic | Driving behavior safety levels driving simulation clustering support vector machine decision tree |
| url | https://ieeexplore.ieee.org/document/9705507/ |
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