Influence of Texting on Driver Glance Patterns and Vehicular Lane Position on
Horizontal Curves
Presented by:
Makenzie EllettResearch Assistant Oregon State UniversitySchool of Civil and Construction Engineering
March 20th, 2015
Forsyth
2
Background – Cell Phone Use
• The first cell phone, 1983• 1985: 340,000 subscribers• 2000: 100 million subscribers• 94% of people in the US aged 16+
owned a mobile device in 2013
• The first text message, 1992• “Merry Christmas” • 1997, USA: 40,000 text messages/day• 2012, USA: 6 billion text
messages/day • Most popular cell phone feature
(CTIA, 2013)
Number of Cell Phone Subscribers in the United States
1980 1985 1990 1995 2000 2005 2010 20150
50100150200250300350
Year
Num
ber
of S
ubsc
ribe
rs
(in
mill
ions
)
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
01234567
Year
Num
ber
of T
ext
Mes
sage
s (b
il-lio
ns)
Number of Text Messages Sent Per Day in the United States
3
Background – Driving Task
Driving Task Hierarchy (Lunenfeld and Alexander)
4
Background – Distracted Driving
Distraction Types (NHTSA)
5
Background – Safety of Texting and Driving
• Risk of crash increases by 23.24 times (Olson et al., 2009) • Conversing on a hand-held mobile
phone increases crash risk 1.04 times• Texting is the most dangerous activity
while driving
• National Survey on Distracted Driving Attitudes and Behaviors• 32.9% believe there is no difference in
their driving• 92.2% feel at least “somewhat
uncomfortable” when riding with a driver who texts
Percentage of Population Observed Manipulating Hand-
Held Devices (NOPUS)
Driver Type 2010 2011
All Drivers 0.90% 1.30%
Age 25-69 0.80% 1.10%
Age 16-24 1.50% 3.70%
6
Background – Legality of Texting while Driving
Laws Regarding Texting While Driving By State
LEGENDNo Ban
Total Ban (Primary Law)Total Ban (Secondary
Law)Partial Ban (School Bus & Novice Drivers)Partial Ban (Novice Drivers
Only)
7
Literature Review – Glance Patterns
• The longer a driver’s eyes are away from the roadway, the greater the odds ratios of a crash incident
• For an “incident” to occur, driver glances of 1.1 sec. were observed (Klauer et al., 2006)
• Texting defined as a “complex, tertiary task” (Olson et al., 2009)
Odds Ratios Associated with Eyes Off of the Forward Roadway (Klauer et al., 2006)
Total Eyes off Forward Roadway Odds Ratios
Lower Control Limit Upper Control Limit
Time (seconds) (LCL) (UCL)
t ≤ 0.5 1.13 0.67 1.92
0.5 < t ≤ 1.0 1.12 0.79 1.59
1.0 < t ≤ 1.5 1.14 0.79 1.65
1.5 < t ≤ 2.0 1.41 0.98 2.04
t > 2.0 2.27 1.79 2.86
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Literature Review – Lateral Position• As distraction levels increase, the vehicle’s standard deviation of
lateral position (SDLP) also increases
• 70% increase in lane position variability compared to baseline (Hosking et al., 2006)
• Lane excursions increase when texting (Reed et al., 2008)• Reading: 8 to 18• Writing: 4 to 42
Measuring Standard Deviation of Lateral Position (Verster et al., 2011)
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Methodology - Research Hypotheses
H0: There is no difference in the duration of driver fixations on a mobile phone while completing a text messaging task between four horizontal curves.
? ? ?Curve 1
Curve 2
Curve 3
Curve 4
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Methodology - Research Hypotheses
H0: There is no difference in the lateral position of a vehicle between baseline driving and driving while completing a text messaging task between four horizontal curves.
? ? ?Curve 1
Curve 2
Curve 3
Curve 4
(Larmoyeux & Bone)
11
Methodology - Research Hypotheses
H0: There is no difference in the lateral position of a vehicle before, during, or after the text messaging task between four horizontal curves.
? ? ?Curve 1
Curve 2
Curve 3
Curve 4
12
Methodology – Dependent Variables
• Glance frequency towards mobile phone
• Duration of glances towards mobile phone
• Percentage of time on curve subject’s eyes are on the mobile phone
• SDLP of vehicle throughout curve
13
Methodology - OSU Driving Simulator
14
Methodology - OSU Eye Tracker
Head Mounted Goggles Data Acquisition Unit
15
Methodology – Test Track
(Not to Scale)
16
Methodology – Scenario
Example of Billboard Image
CURVE IMAGE
1 Cow
2 Cat
3 Eagle
4 Dog
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Methodology – Participants
• Data obtained from Joshua Swake, MS 13’ Thesis• Texting while driving was used as a distractor for the original study• Original research studied driver behavior in work zones
• Original Study: 36 participants• Current Study: 18 participants
• Control Group: 4 subjects (did not text)• Treatment Group: 14 subjects (responded to texting cues)
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Results - Data Collection
Result Data Collection Method Reduction of Data
Driver Glance Patterns
Mobile Eye XG Videos
Researcher Observation
Lateral Position of Vehicle
OSU Driving Simulator CSV Files
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Results - Analysis
• Paired T-test• R-studio• Adjusted for multiple comparisons with
the Benjamini and Yekutieli adjustment• Statistically significant p-values < 0.05• 95% confidence intervals
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Results – Average Duration of Driver Fixations
Average duration of driver fixations
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Results – Average Duration of Driver Fixations
Average duration of driver fixations
Average Duration of Driver Fixations (sec)Curve
Paired T-test
Curve 1 Curve 2 Curve 3 Curve 4 P-value Significant
1.078 1.091 1.090 1.146
1 v 2 0.7311 No
1 v 3 0.6817 No
1 v 4 0.8329 No
2 v 3 0.9922 No
2 v 4 0.5374 No
3 v 4 0.3525 No
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Results – Maximum Duration of Driver Fixations
Maximum duration of driver fixations
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Results – Maximum Duration of Driver Fixations
Statistical summary comparing maximum duration of fixations between curves
Maximum Duration of Driver Fixations (sec)Curve
Paired T-test
Curve 1 Curve 2 Curve 3 Curve 4 P-value Significant
4.04 2.54 2.61 2.87
1 v 2 0.1953 No
1 v 3 0.2701 No
1 v 4 0.1983 No
2 v 3 0.5397 No
2 v 4 0.4081 No
3 v 4 0.7664 No
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Results – Percentage of Time with Eyes Off Roadway
Percentage of time with eyes off roadway
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Results – Average Percentage of Time with Eyes Off Roadway
Statistical summary of average percentage of time with eyes off roadway
Average Percentage of Eyes off Forward RoadwayCurve
Paired T-test
Curve 1 Curve 2 Curve 3 Curve 4 P-value Significant
30.2 20.1 27 24.7
1 v 2 0.06212 Suggestive
1 v 3 0.5885 No
1 v 4 0.2548 No
2 v 3 0.06371 Suggestive
2 v 4 0.05607 Suggestive
3 v 4 0.5473 No
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Results – Average Overall SDLP
Overall SDLP for control condition
Overall SDLP for treatment condition
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Results – Average Overall SDLP
Average overall SDLP for control and treatment conditions
Curve 1 Curve 2 Curve 3 Curve 40.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
2.00
ControlTreatment
SDLP
(ft
)
28
Results – Average Overall SDLP
Statistical summary of average overall SDLP for control condition
Average SDLP of Control ConditionCurve
Paired T-test
Curve 1 Curve 2 Curve 3 Curve 4 P-value Significant
1 1.19 1.06 1.05
1 v 2 0.26 No
1 v 3 0.60 No
1 v 4 0.12 No
2 v 3 0.49 No
2 v 4 0.36 No
3 v 4 0.94 No
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Results – Average Overall SDLP
Statistical summary of average overall SDLP for treatment condition
Average SDLP of Treatment ConditionCurve
Paired T-test
Curve 1 Curve 2 Curve 3 Curve 4 P-value Significant
1.77 1.29 1.25 1.26
1 v 2 0.10 No
1 v 3 0.16 No
1 v 4 0.13 No
2 v 3 0.80 No
2 v 4 0.78 No
3 v 4 0.94 No
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Results – Average Overall SDLP Comparison
255 260 265 270 275 280 285 290 2950
5
10
15
20
25Control Subject - Curve 1
Video Time (s)
Lane
Pos
itio
n (f
t)
230 235 240 245 250 255 260 2650
5
10
15
20
25
Treatment Subject - Curve 1
Video Time (s)
Lane
Pos
itio
n (f
t)Comparison of control and treatment subjects’ SDLP
31
Results – Average Interval SDLP
SDLP for before interval SDLP for during interval
SDLP for after interval
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Results – Average Interval SDLP
Average overall SDLP for control and treatment conditions
1 2 3 40.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
Before
During
After
Curve
SDLP
(ft
)
33
Results – Average Interval SDLP
Average overall SDLP for control and treatment conditions
Statistical summary of average SDLP for before interval
Average SDLP of before PeriodCurve
Paired T-test
Curve 1 Curve 2 Curve 3 Curve 4 P-value Significant
0.6880 0.5473 0.5652 0.6646
1 v 2 0.1678 No
1 v 3 0.2738 No
1 v 4 0.7468 No
2 v 3 0.9487 No
2 v 4 0.2346 No
3 v 4 0.1675 No
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Results – Average Interval SDLP
Statistical summary of average SDLP for during interval
Average SDLP of during PeriodCurve
Paired T-test
Curve 1 Curve 2 Curve 3 Curve 4 P-value Significant
1.1948 1.0280 1.1671 1.0826
1 v 2 0.5976 No
1 v 3 0.7777 No
1 v 4 0.5857 No
2 v 3 0.3269 No
2 v 4 0.7846 No
3 v 4 0.6845 No
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Results – Average Interval SDLP
Statistical summary of average SDLP for after interval
Average SDLP of after PeriodCurve
Paired T-test
Curve 1 Curve 2 Curve 3 Curve 4 P-value Significant
1.0918 0.9341 0.8377 0.8057
1 v 2 0.7251 No
1 v 3 0.6745 No
1 v 4 0.4962 No
2 v 3 0.6771 No
2 v 4 0.2655 No
3 v 4 0.8039 No
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Conclusions – Duration and Frequency of Fixations
H0: There is no difference in the duration of driver fixations on a mobile phone while completing a text messaging task between four horizontal curves.
H0 is not rejected
• No statistically significant differences were found between the fixation durations
• No statistically significant difference was found between the maximum fixation durations
37
Conclusions –SDLP of Treatment and Control Groups
H0: There is no difference in the lateral position of a vehicle between baseline driving and driving while completing a text messaging task between four horizontal curves.
H0 is not rejected
• No statistical difference was found in the average SDLP of the treatment group
• No statistical difference was found in the average SDLP of the control group• Treatment group exhibited increased SDLP compared to control group on
all four curves
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Conclusions – SDLP of Before, During, & After Intervals
H0: There is no difference in the lateral position of the vehicle before, during, or after the text messaging task between four horizontal curves.
H0 is not rejected
• No statistically significant difference was found in the average SDLP of the before intervals
• No statistically significant difference was found in the average SDLP of the during intervals
• No statistically significant difference was found in the average SDLP of the after intervals
• Average SDLP was least for before interval on all four curves• Average SDLP was greatest for during interval on all four curves• Average SDLP was noticeably increased during after interval, compared to before
interval
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• A larger, more diverse sample size could result in more specific conclusions relating the effects of age, gender, and driving experience
• A larger sample size could result in statistical conclusions being drawn between the control and treatment groups
• Analysis on the addition of ambient traffic
• Varying the text messaging cues by category, complexity, or prompt-type to see their effects on driver behavior
• Direct comparison of SDLP and glance patterns of texting on horizontal curves and tangent sections
Future Work
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• Dr. David Hurwitz
• Justin Neill
• Joshua Swake
• OSU Transportation Department
• OSU Honors College
Acknowledgements
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QUESTIONS?
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