Research on countermeasure of taxi driver's aggressive driving behavior based on text features extraction

Sizhe Li; Wenyi Yan; Wenjie Liao

doi:10.1117/12.3050981

20 January 2025 Research on countermeasure of taxi driver's aggressive driving behavior based on text features extraction

Sizhe Li, Wenyi Yan, Wenjie Liao

Author Affiliations +

Proceedings Volume 13422, Fourth International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2024); 134221Q (2025) https://doi.org/10.1117/12.3050981
Event: Fourth International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2024), 2024, Xi'an, China

Abstract

Aggressive driving is a traffic safety issue that people are concerned about. To explore the causes of aggressive driving, manual analysis methods are applied currently. However, this method may lack of objectiveness and accuracy. To address this, this paper will discusses how to identify and improve the aggressive driving behavior of taxi drivers by text features extraction (TF-IDF). It adopts a four-stage experimental process: firstly, classify and describe different road conditions, then conduct a seminar and analyze the content of the seminar in detail by displaying driving behavior cases. Thirdly, use text feature extraction technology to identify aggressive driving behavior patterns and induce preventive measures; Finally, report analysis results and give suggestions. In the process of text analysis, keywords are extracted by natural language processing technology, and lexical networks are constructed by using the co-occurrence frequency method. The results show that aggressive driving behavior is mainly concentrated in urban traffic scenes and key traffic nodes, and is closely related to drivers' subjective factors and external traffic environment. Therefore, the study puts forward a series of targeted suggestions, aiming at improving the level of road traffic safety by improving driver safety awareness and optimizing driving behavior. It demonstrates the application potential of text feature extraction in traffic behavior research and provides a scientific basis for traffic management and policy formulation.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Sizhe Li, Wenyi Yan, and Wenjie Liao "Research on countermeasure of taxi driver's aggressive driving behavior based on text features extraction", Proc. SPIE 13422, Fourth International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2024), 134221Q (20 January 2025); https://doi.org/10.1117/12.3050981

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KEYWORDS

Roads

Safety

Feature extraction

Analytical research

Correlation coefficients

Data mining

Reflection

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