CHAPTER 5: OBJECTIVE 4, WHAT IS THE RELATIONSHIP BETWEEN MEASURES OBTAINED FROM PRE-TEST BATTERIES (E.G., A LIFE STRESS TEST) AND THE FREQUENCY OF ENGAGEMENT IN DISTRACTING BEHAVIORS WHILE DRIVING? DOES THERE APPEAR TO BE ANY CORRELATION BETWEEN WILLINGNESS TO ENGAGE IN DISTRACTING BEHAVIORS AND MEASURES OBTAINED FROM PRE-TEST BATTERIES?
For this analysis, correlations were conducted using the frequency of involvement in inattention-related baseline epochs and each driver’s composite score or relevant response for 9 of the 11 questionnaires and performance-based tests that were administered to the drivers (Table 5.1). A baseline epoch was deemed to be “inattention-related” if the driver engaged in a secondary task or was marked as drowsy at any point during the 6-second segment. The debrief form and the health assessment questionnaires were not included as they were not designed for this type of analysis.
Table 5.1. Description of questionnaire and computer-based tests used for 100-Car Study.
|
Name of Testing Procedure |
Type of Test |
Time test was administered |
Brief description |
|---|---|---|---|---|
1. |
Driver demographic information |
Paper/pencil |
In-processing |
General information on drivers age, gender, etc. |
2. |
Driving History |
Paper/pencil |
In-processing |
General information on recent traffic violations and recent collisions |
3. |
Health assessment questionnaire |
Paper/pencil |
In-processing |
List of variety of illnesses/medical conditions/or any prescriptions that may affect driving performance. |
4. |
Dula Dangerous Driving Index |
Paper/pencil |
In-processing |
One score that describes driver's tendencies toward aggressive driving. |
5. |
Sleep Hygiene |
Paper/pencil |
In-processing |
List of questions that provide information about driver's general sleep habits/substance use/sleep disorders |
6. |
Driver Stress Inventory |
Paper/Pencil |
In-processing |
One score that describes the perceived stress levels drivers experience during their daily commutes |
7. |
Life Stress Inventory |
Paper/pencil |
In-processing/Out-processing |
One score that describes drivers stress levels based upon the occurrence of major life events |
8. |
Useful Field-of-View |
Computer-based test |
In-processing |
Assessment of driver's central vision and processing speed, divided and selective attention. |
9. |
WayPoint |
Computer-based test |
In-processing |
Assessment of the speed of information processing and vigilance. |
10. |
NEO-FFI |
Paper/pencil |
In-processing |
Personality test |
11. |
General debrief questionnaire |
Paper/pencil |
Out-processing |
List of questions ranging from seatbelt use, driving under the influence, and administration of experiment. |
For the analyses in this chapter, crashes and near-crashes only will be used (incidents will be excluded from the analyses). In Chapter 6, Objective 2 of the 100-Car Study Final Report, the analyses indicated that the kinematic signatures of both crashes and near-crashes were nearly identical; whereas the kinematic signatures of incidents were more variable. Given this result and to increase statistical power, the data from both crashes and near-crashes will be used in the comparison of questionnaire data to the frequency of involvement in inattention-related crashes and near-crashes.
Note that inattention-related crashes and near-crashes or inattention-related baseline epochs are defined as those events that involve the driver engaging in complex, moderate, or simple secondary tasks or driving while drowsy. Please note that in Chapter 2, driving-related inattention to the forward roadway was determined to possess a protective effect and therefore was removed from the definition of driving inattention. Non-specific eyeglance away from the forward roadway was also shown to not be significantly different from normal, baseline driving; therefore, these events were also removed from the analysis.
Figure 5.1 shows the distribution of the number of inattention-related baseline epochs that each driver was involved. Note that seven primary drivers were not involved in any inattention-related baseline epochs. The mean frequency of inattention-related baseline involvement is 87.2, the median frequency is 62, and the range of frequency counts is 0 to 322 baseline inattention epochs.
Figure 5.1. The frequency distribution of the number of inattention-related baseline epochs that each driver was involved (N = 101). Note: Subjects were sorted by frequency of involvement to allow the reader to see the range of values.
A Spearman correlation between the frequency of involvement in inattention-related crash and near-crash events and baseline epochs was performed. The results indicated a strong correlation with an R-value of 0.72, p = 0.0001. This suggests that drivers who are frequently engaging in inattention-related tasks, as shown by the baseline data, are also those that are more frequently involved in crashes and near-crashes. This also suggests that the better, safer drivers engage in secondary tasks and/or drive drowsy less often than do those drivers who were involved in multiple crashes and near-crashes.
Correlations were conducted using representative survey questions, composite scores from the test batteries, and scores from the computer-based tests and frequency of involvement in inattention-related baseline epochs. Table 5.2 presents the corresponding correlation coefficients and probability values for those test scores that were statistically significant. Note that Driver Age and Driving Experience obtained the highest correlation coefficient at -0.4 while the rest of the coefficients were very weak with R values under 0.3.
Table 5.2. The significant correlations between test battery, survey, and performance-based test scores to the frequency of inattention-related baseline epochs (N = 101).
Name of Testing Procedure |
Question/Score |
Correlation Coefficient |
Probability Value |
|---|---|---|---|
Driver demographic information |
Driver Age |
-0.41 |
<0.0001 |
Years of driving experience |
-0.44 |
<0.0001 |
|
Dula Dangerous Driving Index |
DDDI |
0.29 |
0.004 |
Risky Driving |
0.26 |
0.01 |
|
Sleep Hygiene |
Daytime Sleepiness |
0.22 |
0.03 |
Driver Stress Inventory |
Aggression |
0.23 |
0.02 |
Thrill-Seeking |
0.26 |
0.01 |
|
NEO-FFI |
Extroversion |
-0.21 |
0.03 |
Agreeableness |
-0.27 |
0.007 |
|
Conscientiousness |
-0.22 |
0.03 |
|
Waypoint |
Channel |
0.34 |
0.0014 |
Correlations were also conducted using the frequency of driver involvement in inattention-related crashes and near-crashes to the relevant responses from the surveys, test batteries, and performance-based tests. This analysis is different from the one conducted in Chapter 4, Objective 3 in that the drivers are no longer separated into “high involvement” and “low involvement” drivers. Table 5.3 presents only those correlations that were statistically significant. Note that some of the correlations no longer were significant, i.e., Dula Dangerous Driving, Driver Stress Inventory, and Waypoint. Also note that some of the correlations, while still significant, were slightly weaker for the crashes and near-crashes, i.e., Driver Age and Driving Experience.
Table 5.3. The significant correlations between test battery, survey, and performance-based test scores to the frequency of inattention-related crash and near-crash events (N = 101).
Name of Testing Procedure |
Question/Score |
Correlation Coefficient |
Probability Value |
|---|---|---|---|
Driver Demographic Information |
Driver Age |
-0.29 |
<0.004 |
Years of driving experience |
-0.31 |
<0.001 |
|
Sleep Hygiene |
Daytime Sleepiness |
0.20 |
0.05 |
NEO-FFI |
Extroversion |
-0.23 |
0.02 |
Agreeableness |
-0.26 |
0.007 |
|
Conscientiousness |
-0.20 |
0.03 |
These results suggest a clear relationship between engagement in secondary tasks or driving while drowsy to selected survey responses and test battery scores. According to Keppel and Wickens (2004), correlation coefficients of 0.4 to 0.2 represent small effect sizes as they account for 4 to 16 percent of the variance among these values. While these relationships or associations are small, the fact that these relationships are obtaining statistical significance given the high variability among drivers is a result that should not be overlooked. These results, taken with the results from Chapter 4, Objective 3 indicate that driver demographic data, driving history data, sleep hygiene data and the NEO Five-Factor Inventory all demonstrate linear relationships to driving performance. Apart from age and driving experience, it is unfortunately unknown how this information could be used to predict which drivers will be high-risk drivers (i.e., those who demonstrate tendencies to drive while they are engaging in secondary tasks or drowsy).
The high correlation of 0.72 between the frequency of driver’s involvement in inattention-related crashes and near-crashes and baseline epochs suggests that those drivers who frequently engage in inattention-related activities are also frequently involved in crashes and near-crashes. Those drivers who are not engaging in inattention-related tasks frequently are not frequently involved in inattention-related crashes and near-crashes. Therefore, if an inattention mitigation device was developed, the highly inattentive drivers could possibly benefit from such a device.