Abstract
Traffic crashes have posed a serious threat to global health and have had a negative impact on social and economic development. In 2015, 1.25 million people died in traffic crashes around the world, over 90% of which were at least partially caused by human error. This research intends to develop a new driver training tool which can help reduce human error by correcting drivers’ risky driving behaviors. On the basis of the theory of planned behavior (TPB), this research proposed a “perception–norm–execution” (PNE) driving simulator-based training model. To evaluate the effectiveness of this PNE training model, five risky driving behaviors (i.e. improper lane changes, unsafe overtaking, red-light running, speeding, and distracted driving) were selected as training scenarios. A total of 44 participants were recruited to participate in the trainings. Twenty-two participants received the PNE model (PM) training, and the remaining participants received the normal (video-based) model (NM) training. This research used the empirical data collected from a driving simulator to evaluate participants’ driving performance before and after they received the training. It is found that both training models significantly improved driving performance, but the PM training was demonstrated to be more effective than the NM training in terms of correcting drivers’ risky driving behaviors such as improper lane changes, unsafe passing, and running red lights. The research findings can be used to change policies and develop more effective driver education and training programs.
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Acknowledgements
This study was supported by the National Natural Science Foundation of China (Grant no.: 61672067), and the project title was “A Study on the Eco-Driving Behavior Classification Model and Optimization Based on Deep Learning Theory.” This research also received the support from the Open Project of Key Laboratory of Ministry of Public Security for Road Traffic Safety in China: Study on Intervention Method of Illegal Driving Behavior Based on Risk Prediction Education (Project no.: 2016ZDSYSKFKT01). Funding was provided by Beijing Science and Technology (40038001201720).
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Zhao, X., Xu, W., Ma, J. et al. The “PNE” driving simulator-based training model founded on the theory of planned behavior. Cogn Tech Work 21, 287–300 (2019). https://doi.org/10.1007/s10111-018-0517-8
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DOI: https://doi.org/10.1007/s10111-018-0517-8