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Citation |
Abd Rahman NI, Md Dawal SZ, Yusoff N. Transp. Res. F Traffic Psychol. Behav. 2020; 69: 265-285. |
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Copyright |
(Copyright © 2020, Elsevier Publishing) |
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DOI |
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PMID |
unavailable |
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Abstract |
The World Health Organization stated that the global ageing population is increasing rapidly as well as the case of road accidents involving ageing drivers. This study presents the driving mental workload and performance model of ageing drivers in the context of real-time road driving. Twenty paid participants (ten males) with a mean age of 57.8 years old (SD = 2.7) and mean driving experience of 29.6 years (SD = 8.5) took part in driving experiments with three complexity levels of situation: simple situation (SS), moderately complex situation (MCS), and very complex situation (VCS). The subjective ratings using NASA Task Load Index (NASA-TLX), physiological measure using electroencephalogram, number of traffic violations (NTVs), speed variability, and reaction time of peripheral detection task were measured. The driving experiments reveal the following: (1) The subjective workload ratings on mean physical demand score were the highest. (2) The electroencephalogram results show that situation complexity had significant effects on theta relative power and alpha relative power of two channel locations (3) The highest mean NTVs was in VCS. (4) The mean speed variability in the MCS was significantly lower than that of in the SS and VCS. (5) The maximum reaction time was recorded in VCS while the minimum reaction time was recorded in the MCS. The overall driving performance score regression models were developed based on the strong correlation and linear relationship between mental workload and driving performance elements. The models may benefit as a reference for designers, manufacturers, developers, and policymakers in designing better driving environment for ageing drivers by integrating safety and transportation, thus, optimizing and sustaining the driving performance of ageing drivers. Language: en |
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Keywords |
Ageing drivers; Driving errors; Electroencephalogram; Mental workload; NASA-TLX; Reaction time; Speed variability |