Training interventions are only effective on careful drivers, not careless drivers

Zhang, Tingru; Hajiseyedjavadi, Foroogh; Wang, Yuhua; Samuel, Siby; Qu, Xingda; Fisher, Donald L.

doi:10.1016/j.trf.2018.07.004

Cited by 25 publications

(15 citation statements)

References 34 publications

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“…Thus, the accident variable should only contain culpable crashes in order to obtain a purer measure of the intended construct and subsequently larger effect sizes. These issues might explain why previous research has failed to support the postulated benefits of training on driving performance [ 27 , 28 , 29 , 30 ].…”

Section: Discussionmentioning

confidence: 99%

“…Previous research on the influence of training on traffic accidents is scarce [ 26 ] and its effectiveness has been questioned [ 27 , 28 , 29 ]. Additionally, it is noteworthy that previous research has mainly focused on the influence of target-specific training interventions [ 30 ] rather than periodic training understood as a continuous activity promoted by the organization. Thus, research on the influence of continuous training on road safety is needed.…”

Section: Introductionmentioning

confidence: 99%

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The Influence of Organizational Factors on Road Transport Safety

Gamero

Silla²,

Sainz-González

et al. 2018

IJERPH

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Road transport safety is a major concern across Europe due to the human and socio-economic costs associated with work-related traffic accidents. Traditional approaches have adopted regulatory and technical measures to prevent road accidents leaving aside the organizational factors that might contribute to road transport safety. However, contemporary sociotechnical systems theory acknowledges the need to take into account organizational factors. This study adopts a sociotechnical approach and it examines the relationship between a number of organizational factors (organizational learning and training) and road traffic accidents in the organizations under study. Our sample was composed of 107 road transportation organizations from Spain. Binary logistic regression analyses were carried out to test our hypotheses. Organizational size and type of transport (goods or passengers) were included in the model as control variables. Results showed that in those organizations where organizational learning was supported, the occurrence of traffic accidents was less likely. Unexpectedly, the relationship between training and the occurrence of traffic accidents was not significant. Thus, findings partially supported the formulated hypothesis. Future research should shed light on the relationship between training and traffic accidents taking into account potential intervening variables.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Influence of Organizational Factors on Road Transport Safety

Gamero

Silla²,

Sainz-González

et al. 2018

IJERPH

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show abstract

“…Being able to identify and track potential hazards is an essential skill for safe driving and has not been adequately studied in the driving domain. It is closely related to the attentional skill of hazard anticipation, which is described by Zhang et al (2018) as the act of scanning the driving environment for a hazard that may or may not be presently visible but could pose a threat in the near future.…”

Section: Implications Of This Studymentioning

confidence: 99%

“…Research suggests that one of the factors associated with fatal crashes for young adults (16-24) is poor driving skills such as attention maintenance, hazard anticipation, and hazard mitigation (Zhang et al, 2018). The goal for the current research project is to understand and improve methods of training safe driving skills.…”

mentioning

confidence: 99%

Improving hazard perception and tracking through part-task training

Dumessa

Gugerty

2019

Proceedings of the Human Factors and Ergonomics Society Annual

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Safe driving requires wisely allocating focal attention among multiple changing events and comprehending events that are attended to. Research suggests that attentional skills can be improved by training. In this experiment, we are using a low-fidelity driving simulator to train participants using part-task training on two attentional subskills: identifying (comprehending) and tracking potential hazards; and detecting and avoiding imminent hazards. Following initial familiarization with the driving simulator, each participant will receive training in one of these two attentional subskills. Scene comprehension probes train (and measure) identifying and tracking potential hazards by having participants watch a moving driving scenario and then select the vehicle that behaved hazardously during the scene. In hazard avoidance probes, participants must make driving responses to avoid imminent hazards without hitting nearby vehicles. After the training phase, there is a test phase measuring near transfer, to hazards similar to training, and far transfer, to untrained hazards. We hypothesize that the participants who receive part-task training on identifying and tracking hazards should perform better at scene comprehension probes than the hazard-avoidance training group in both near and far transfer conditions. We also hypothesize that the group trained on avoiding imminent hazards will perform better on hazard avoidance probes in both near and far transfer conditions.

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“…At present, the study of different aspects of driving training is very extensive [3,4,5,6,7,8,9,10,11], such as driving training for special people [7,12,13], accident prevention [6,14,15], driver reaction times [2,16], etc. However, we are not sure whether the training topics are effective or not [4,5].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Driver Training System Based on Real Cars

Duan

Yan

2019

Sensors

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In driver training, the correct observation of the trainees’ operation is the key to ensure the training quality. The operation of the vehicle can be expressed by the vehicle state changes. This paper proposes a driver training model based on a multiple-embedded-sensor net. Six vehicle state parameters are identified as the critical features of the reverse parking machine learning model and represented quantitatively. A multiple-embedded-sensor net-based system mounted on a real vehicle is developed to collect the actual data of the six critical features. The data collected at the same time are bound together and encapsulated into a vector and sequenced by time with a label given by the multiple-embedded-sensor net. All vectors are evaluated by subjective assessment conclusions from experienced driving instructors and the positive ones are used as the training data of the model. The trained model can remind the driver of the next correct operation during training, and can also analyze the improvements after the training. The model has achieved good results in practical application. The experiments prove the validity and reliability of the proposed driver training model.

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Training interventions are only effective on careful drivers, not careless drivers

Cited by 25 publications

References 34 publications

The Influence of Organizational Factors on Road Transport Safety

The Influence of Organizational Factors on Road Transport Safety

Improving hazard perception and tracking through part-task training

An Intelligent Driver Training System Based on Real Cars

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