Risky behaviors, psychological failures and kinematics in vehicle-to-powered two-wheeler accidents: Results from in-depth Chinese crash data

Wu, Xinghua; Peng, Yong; Yi, Shengen; Wang, Honggang; Yu, Weifeng

doi:10.1016/j.aap.2021.106150

Cited by 18 publications

(5 citation statements)

References 26 publications

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“…In the field of traffic safety, significant research effort has been devoted for using the structured data elements of traffic crash reports [1][2][3][4][5][6][7][8]. Although several recent studies have attempted to utilize the unstructured text-based traffic crash description data to identify the situation and circumstances of traffic crashes, the utility of these existing methods remains limited to counting the frequency of certain keywords [12]- [15].…”

Section: Literature Reviewmentioning

confidence: 99%

“…raffic safety researchers and engineers frequently use traffic crash data as the core dataset describing the causes and situations of traffic crashes, which are otherwise rarely known after the event. Many traffic safety studies have analyzed traffic crash data for vehicle-to-vehicle crashes as well as vehicle-to-pedestrian crashes, motorcycle crashes, and illegal behavior [1][2][3][4][5][6][7][8]. In most countries, a traffic crash report should be written after a reported crash by a responsible person whose qualifications are defined by the respective law and traffic regulations [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Text Classification Modeling Approach on Imbalanced-Unstructured Traffic Accident Descriptions Data

Seo,

Park,

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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The unstructured-textual crash descriptions recorded by police officers is rarely utilized, despite containing detailed information on traffic situations. This lack of utilization is mainly due to the difficulty in analyzing text data, as there is currently no innovative methodology for extracting meaningful information from it. Given limitations and challenges in analyzing traffic crash descriptions, this study developed a methodology to classify significant words in unstructured data that describe traffic crash scenarios into standardized data. Ultimately, a natural language processing technique, specifically a bidirectional encoder representation from transformer (BERT), was used to extract meaningful information from crash descriptions. This BERT-based model effectively extracts information on the exact collision point and the pre-crash vehicle maneuver from crash descriptions. Its practical approach allows for the interpretation of traffic crash descriptions and outperforms other natural language processing models. Importantly, this method of extracting crash scene information from traffic crash descriptions can aid in better comprehending the unique characteristics of traffic crashes. This comprehension can ultimately aid in the development of appropriate countermeasures, leading to the prevention of future traffic crashes.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Text Classification Modeling Approach on Imbalanced-Unstructured Traffic Accident Descriptions Data

Seo,

Park,

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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“…A U-turn is a task often performed by drivers on the road to reach their planned destination. Rear-end accidents are more likely to occur when a vehicle is turning or swerving than when changing lanes 31 . Wrong turns, u-turns, or lane changes are the main causes of rear-end and tailgating accidents 32,33 .…”

Section: Experimental Scenariosmentioning

confidence: 99%

Hazard perception in driving style based on virtual reality technology

Wang

et al. 2022

Preprint

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The aim of this paper is to directly investigate the influence of driving style on hazards and to test the hazard perception of drivers with driving style variability in different scenarios. To achieve these goals, we use a VR system for simulation. In the experiment, we compare the hazard perception abilities of drivers with four driving styles (dangerous, angry, anxious, and cautious) in three different hazard conditions (lane change, pedestrian presence phenomenon, and U-turn). Participants ’ driving decisions and hazard perception behaviors were recorded simultaneously. The results show that (1) the VR technology provides a realistic representation of the hazard situation, and the participants were able to react more realistically. (2) Dangerous drivers had the weakest hazard perception, cautious drivers had the strongest hazard perception, and there was little difference in hazard perception of anxious and angry drivers. (3) Lane changing was the most hazard-prone scenario, and drivers ’ hazard perception was the weakest in this scenario, leading to an increased rate of traffic accidents. (4) Male drivers had stronger hazard perceptions than female drivers. The results of this study have important implications for road safety as targeted training can improve hazard perception based on driving style, and the study provides a new paradigm for detecting hazard perception ability.

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“…It's well-known that drivers play a crucial role in the driving process, which requires substantial cognitive effort and attention (Lemercier and Cellier, 2008) from the operator's brain (Chisholm et al, 2008). According to the Statistics of the World Health Organization (WHO), 1.35 million people die from road traffic accidents every year (Tan et al, 2021;Wang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

The Application of Electroencephalogram in Driving Safety: Current Status and Future Prospects

Peng

Lin

et al. 2022

Front. Psychol.

Self Cite

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The driver is one of the most important factors in the safety of the transportation system. The driver’s perceptual characteristics are closely related to driving behavior, while electroencephalogram (EEG) as the gold standard for evaluating human perception is non-deceptive. It is essential to study driving characteristics by analyzing the driver’s brain activity pattern, effectively acquiring driver perceptual characteristics, creating a direct connection between the driver’s brain and external devices, and realizing information interchange. This paper first introduces the theories related to EEG, then reviews the applications of EEG in scenarios such as fatigue driving, distracted driving, and emotional driving. The limitations of existing research have been identified and the prospect of EEG application in future brain-computer interface automotive assisted driving systems have been proposed. This review provides guidance for researchers to use EEG to improve driving safety. It also offers valuable suggestions for future research.

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Risky behaviors, psychological failures and kinematics in vehicle-to-powered two-wheeler accidents: Results from in-depth Chinese crash data

Cited by 18 publications

References 26 publications

Text Classification Modeling Approach on Imbalanced-Unstructured Traffic Accident Descriptions Data

Text Classification Modeling Approach on Imbalanced-Unstructured Traffic Accident Descriptions Data

Hazard perception in driving style based on virtual reality technology

The Application of Electroencephalogram in Driving Safety: Current Status and Future Prospects

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