Outline of Results, Methodology, and Data Limitations
Phase II Study on Distracted Driving
Study results revealed that distractions are a common component of everyday driving.
|Potential Distraction||% of Subjects||% of Total Driving Time|
|Talking on cell phone||30.0||1.30*|
|Answering cell phone||15.7|
|Dialing cell phone||27.1|
|Eating, drinking, spilling||71.4||1.45|
|Preparing to eat or drink||58.6||3.16|
|Manipulating music/audio controls||91.4||1.35|
|Smoking (includes lighting and extinguishing)||7.1||1.55|
|Reading or writing||40.0||0.67|
|Reaching, leaning, etc.||97.1||3.78*|
|Manipulating vehicle controls||100.0|
|Other internal distraction||67.1|
The methodology developed for the field data collection activities entailed a camera unit, containing three miniature video cameras, that was mounted inside the vehicle just below the vehicle's rear view mirror. Two of the cameras were directed inside at the driver and front seat area of the vehicle, and the third was directed outside the vehicle straight ahead. A recording unit was generally placed in the trunk of the vehicle, and cables discretely run between the units.
The recording equipment was installed in the vehicles of 70 volunteer subjects, who were informed only that the study was being conducted to learn "how traffic and roadway conditions affect driving behavior." They were instructed to "drive normally" and scheduled to return one week later for removal of the equipment.
The resulting videotape data was coded using software (The Observer Video-Pro) specially designed for the coding and analysis of videotaped data. A coding scheme was developed along with selected contextual and outcome variables. A total of three hours of driving data was coded per subject.
The data were analyzed descriptively using the Video-Pro analysis software, and were also converted into SAS data files for further analysis. Given that the longitudinal nature of the data did not meet the assumptions for classic statistical analysis methods, confidence intervals for proportions and linear combinations of proportions were constructed using the bootstrap percentile method.
There are a number of important limitations to this study. The relatively small sample size (70 drivers) and relatively small number of hours analyzed (3 out of 10 hours observed) could limit generalizability.
Difficulty in objectively defining the various driver distraction and contextual/outcome variables also made it hard to achieve high levels of inter-rater reliability when coding the data. Some potentially important variables could not be coded at all.
Cognitive distraction was unable to be captured, which the literature suggests may pose the greatest risk to driving safety. Consequently, the study is not able to provide a definitive answer as to which activities, or which driver distractions, carry the greatest risks of crash involvement.
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