TA2018001 BOS REPORT_PART6.PDF

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iMapData Inc.
8280 Greensboro Dr
McLean, VA 22102
10

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iMapData Inc.
8280 Greensboro Dr
McLean, VA 22102
11

Bdlboards by Grid Cell
I 21 to 37
I 16 to 20
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ID 6 to 10
□ Ito 5
□ 0
iMapData Inc.
8280 Greensboro Dr
McLean, VA 22102
12

Conclusion
IMapData has taken the current property value of all land parcels In Tampa and then
disaggregated those parcels into parcels with outdoor advertising and parcels without outdoor
advertising.
IMapData found that the presence of outdoor advertising does not negatively impact property
values. IMapData also found that in an overwhelming number of areas of Tampa, that the
presence of outdoor advertising positively impacted property values—and, in particular,
commercial property values.
Regardless of their concentration, billboards tend to make a consistent, positive impact on
parcel values. A study of billboard clusters suggested that billboards are supporting the small
and medium sized enterprises that contribute so strongly to local economies.
IMapData Inc.
8280 Greensboro Dr
McLean, VA 22102 
13

Methodology
The methodologies employed in this study encompass a number of sophisticated data-analysis
and mapping techniques. All of these relatively high technology tools were subordinated to the
underlying methodological principle shaping the study that all findings came from the bottom
up, not top down. Thus the study's methodology and its finding are entirely empirical; said
differently, this is the opposite of a theoretical study.
The study followed the below interrelated methodological steps:
1. Obtained a database of all parcels in the city;
2. Geocoded each Billboard to a parcel;
3. Mapped the values of all the parcels using the residential and commercial property
values per land parcel as provided in the second quarter of fiscal year 2012 by Tampa's
Department of Planning and Management;
4. Disaggregated parcels by parcels with billboards and without billboards;
5. Disaggregated parcels by zoning code;
6. Calculated property values per area in terms of acres and square feet;
7. Measured the density of billboards per parcel and per square mile of city.
iMapData Inc.
8280 Greensboro Dr
McLean, VA 22102 
14

About iMapData
For 30 years, industry leader IMapData has helped public and private sector organizations make
swift, data-driven decisions: decisions based on a wealth of relevant, up-to-date
Information. This valuable intelligence is displayed in an easy to analyze, geographic format,
with rich data linking and data analysis functionality and sophisticated reporting capability.
Wiliiam Lilley III, Chairman, Co-founder
William Lilley III served as Director of the U.S. Council on Wage and Price Stability and as
Staff Director of the Budget Committee for the U.S. House of Representatives. Prior to
founding IMapData he was a senior corporate official of the New York-based media
company CBS Inc.
He received his Ph.D. from Yale University where he taught political and economic
history for eight years. He has written widely on how government policies affect local
economic activity, on the economics of the professional sports business and on the
socio-economic makeup of U.S. state and local political constituencies.
Laurence J. DeFranco, President, CEO, Co-founder
Laurence J. DeFranco is an expert In the field of geo-economlcs which merges the
disciplines of economics, geography and computer science. He has written, testified and
spoken widely on the effects of economic, regulatory, and legislative policy on
businesses - especially in a geographic context.
He previously founded and headed Program Flow, Inc., the computer software, research
and consulting firm that was the predecessor to IMapData. Before that, he worked for
CBS Inc. as head of the New Technologies Task Force.
William Lilley and Laurence DeFranco have collaborated on several noteworthy publications,
including an award-winning five volume series on American politics and demographics.
IMapData Inc.
8280 Greensboro Dr
McLean, VA 22102 
15

EXHIBIT4

Evaluation of the MAG Safety and Elderly
Mobility Sign Project
Robert Gray, Ph.D.
Associate Professor
Arizona State University
&
Brooke Neuman
Graduate Student
Arizona State University
September 2010
IVIAF3ICOPA
ASSOCIATION of
1%. OOVERNMENTS

Table of Contents
Executive Summary 
4
1. Background 
7
2. Methods 
10
2.1 Participants 
10
2.2 Apparatus 
10
2.3 Procedure 
12
2.4 Data Analysis 
14
3. Results 
16
3.1 Sign Recognition Distance 
16
3.2 Number of Turn Errors 
17
3.3 Lane Change Distance 
18
3.4 Intersection Approach Speed 
19
3.5 Lane Variability & 
Collisions 
20
3.6 Questionnaire Data 
21
4. Conclusions 
23
Sources Referenced 
26
Appendices 
27
A. Data Analyses 
27
Evaluation ofthe MAG'Safety and Elderly Mobility Sign Project

List of Figures
Figure 1: Blooming from Standard and Clearview Font 
7
Figure 2: Driving simulator 
11
F igure 3: Example of Clearview Font overhead sign image 
11
Figure 4: Example ofStandard Highway Font (Series 200) overhead sign image 
12
Figure 5: Overhead view of the driving environment 
13
Figure 6: Mean sign recognition distance 
17
Figure 7: Mean number of turn errors 
18
Figure 8: Mean lane change distance 
19
Figure 9: Mean intersection approach speed 
20
Figure 10: Mean difficulty ratings 
21
Evaluation ofthe MAG Safety and Elderly Mobility SlgnFroject

List of Tables
Table 1: Street sign names 
14
Evdliidtioh oftfie~MA'G Safety and Elderly Mobility Sign Project

Evaluation of the MAG Safety and Elderly Mobility Sign Project
Executive Summary
In 2007, the Transportation Safety Committee and the Elder Mobility Stakeholders Group
of the Maricopa Association of Governments jointly launched a road safety project for installing
new Clearview font street name signs, designed for better legibility, based on the FHWA
Guidelines and Recommendations to Accommodate Older Drivers and Pedestiians, <YEAR>.
The types of signs addressed by the project included: street name signs, advance street name
signs, and internally illuminated signs, with all of them using Clearview font sizes. The project
paid the full cost of producing the new signs with the local agencies being responsible for all
installation costs, the project also provided local agencies that have sign fabrication shops, the
necessary software for producing signs with Clearview font.
This study was performed, by a research team from the Arizona State University, to
evaluate the effect of installing the new Clearview street name and advance street name signs on
the safety and the mobility of older drivers. The objective of the study was to develop a soimd
analytical approach to quantify the mobility and safety impacts of the new signs, with Clearview
font, installed at various intersections in the MAG region. Although Clearview font has been
shown to improve simple detection and legibility, no studies had been conducted to directly
measure the effect of Clearview font on driving performance. Improved legibility is not always
predictive of performance in more complex driving tasks and of driving safety in general (Wood
& 
Owens, 2005). The primary goal of this study was to investigate the effect of Clearview font
signs on safety and mobility in a simulated driving and navigation environment.
~^vdludiidri of the MAG Safety and Elderly Mobility Si^ Project

In this study, 36 drivers ranging in age from 56-70 years were asked to navigate through
a virtual city in a driving simulator. Their driving performance was compared for Clearview and
Standard font overhead and advance intersection signs in simulated day and nighttime driving
conditions. Consistent with previous research (Hawkins et al. 1999, Carlson et al., 2001), the
study found that the distance at which drivers could accurately recognize street names was
consistently and significantly greater for Clearview font signs. The increase in sign recognition
distance associated with Clearview font ranged between 8 - 34ft across the drivers in this study
with an average increase of 14ft. Expanding on previous research in this area, the study team
also found that the usage of the Clearview font was associated with consistent and statistically
significant improvements in several measures of driving safety. With Clearview signs, drivers in
our study made 52% fewer turn errors, changed lanes for an upcoming left turn at a significantly
greater distance (by 5.2 ft on average) from the intersection (indicative of better anticipation and
plarming) and drove at a speed closer to the designated speed limit (change in speed of 3.2 mph
on average). The study also observed fewer collisions with other vehicles when Clearview signs
were used. All of these variables are indicators of improved safety and mobility for elderly
drivers.
Interestingly, drivers' subjective evaluations of the effectiveness of Clearview signs did
not match perfectly with the results for driving performance. Clearview signs were rated as
significantly easier to read (ratings were 5% higher on average) but the magnitude of the effect
was much smaller than the effect sizes for the driving performance variables and for sign
recognition distance. Furthermore, 33% of the drivers in our study indicated that the Standard
font sign was easier to read than the Clearview sign when asked to make a forced choice between
the two signs. This occurred even though 100% of the participants in the study drove more safely
Evaluation ofthe MAG Safety and Elderly Mobility Sign Project 
5

in the Clearview conditions. Therefore, the measured improvements in driving safety are much
greater than one might predict from making a passive judgment about the signs. This will be an
important point to emphasize when seeking further funding and support for Clearview sign
adoption.
Given the significant improvements in driving safety and mobility found in this study it is
recommended that the Maricopa Association of Governments continue to encourage the adoption
of Clearview font for street name signs.
Evaluation ofthe MAG Safety and Elderly Mobility Sign Project

1. Background
The Clearview alphabet was developed by Meeker & 
Associates in 1995 to improve the
legibility of roadway signage. The visual structure of Clearview font differs from standard
fonts used on street signs in two ways; the lower case lettering is taller and the lettering allows
for more open space in the interior shape of the letters. The Clearview font's wider open
spaces allow for irradiation without decreasing the distance at which the alphabet is legible
(Garvey 1997). Two of the main goals of developing the new alphabet were to (i) improve
sign legibility for older drivers and (ii) to counter the blooming possibilities when signs
fabricated from bright microprismatic sheeting are illuminated by headlights. Visual acuity
and motor response time are known to diminish with increasing age. When reading roadway
signs, especially at night, the vision of older vehicle operators often suffers from a
phenomenon known as irradiation, halation, or overglow. Irradiation becomes a problem if a
stroke on the lettering is so bright that it visually bleeds into the character's open spaces. This
creates a blobbing effect that reduces legibility. Improvements to reflective material on
roadway signs have increased the occurrence of irradiation. Clearview font was designed to
reduce the magnitude of irradiation or blooming as illustrated in Figure 1.
Ccitrtr
C*i»y«r
Figure 1: Blooming from Standard (left) and Clearview Font (right)
Evaluation ofthe MAG Safety aricFElderly Mobility SignTroJect
T-

Research has shown that Clearview font can improve the legibility of roadway signs both
in daytime and nighttime conditions. Hawkins et al (1999) compared Clearview and Series E
(Modified) font signs under day and night conditions for drivers over 65 years of age. Drivers
were required to read sign names aloud and the recognition distance for each sign was recorded.
Clearview signs outperfonned Series E signs in all cases; however, the difference in recognition
distance was only statistically significant for overhead signs in daytime conditions. Overall,
Clearview signs showed a modest but consistent improvement in recognition distance, ranging
from 3-8% in magnitude. Carlson et al (2001) compared Clearview and Series E (Modified) font
signs fabricated using microprismatic sheeting and nighttime driving conditions. In this study,
young (18-34), middle aged (35-54) and elderly (>55) were compared and recognition distance
was again used as the performance measure. For advance street name signs, mean legibility
distance was 32ft (5%) greater for Clearview signs with the magnitude of the improvement
ranging between 18-58ft. The effect of Clearview signs was greater for older drivers (the
changes in recognition distance were 5.8, 4.6 and 9.3 % 
for the 3 age groups respectively). For
overhead mounted signs, the mean legibility distance was 40ft (6.7%) greater for Clearview
signs with the magnitude of the effect ranging from 26-54ft. Again larger benefits were
observed for older drivers (changes in recognition distance were 2.3, 3.5 and 6.8% for the 3 age
groups).
A major limitation of this previous research is that it does not measure the effect of
Clearview signs on active driving performance. Continuously and passively monitoring a road
sign while sitting in a moving vehicle is a highly unrealistic task. In real navigation situations
drivers must perform sign recognition in conjunction with several other important driving tasks
including collision avoidance, lane keeping, watching for pedestrians entering the road,
Evaluation oftfieMAG Safety and Elderly Mobility Sign Project 
?

monitoring speed etc. As recognized by Carlson et al (2001) drivers must sample the
information presented on a road sign intermittently (i.e., between sampling other information
form the road, vehicle dashboard, etc). Therefore, measures of sign recognition performance in
the unnatural situation where the driver's only task is to read the sign may not predict
performance under more natural, multi-tasking conditions. Furthermore, even if Clearview font
makes signs more legible and easier to read there is some previous research which suggests that
this may not actually impact driving safety. The ability of a driver to see clearly (i.e., measures
of acuity such as Snellen acuity or contrast acuity) can only explain a small amount of the
variance (<10%) in driving accidents (Higgins and Wood, 2005; Wood and Owens, 2005).
Therefore, an evaluation of the direct effects of Clearview font on driving performance is crucial
to inform future efforts to install additional Clearview street name signs with in the MAG region.
Evaluation ofthe MAG Safety and Elderly Mobility Sign Project

2. Methods
2.1 Participants
Thirty-six participants (20 female and 16 male) completed the study. Participants ranged
in age from 56-70 years (M=62.2, SE=0.56). An additional 5 participants could not complete the
study due to motion sickness and therefore their data were discarded. All participants were
compensated $20 for their participation.
2.2 Apparatus
Driving simulator. The driving simulator was composed of two main components: (a) a
steering wheel mounted on a table top and pedals (Wingman Formula Force GP, Logitech^^ and
(b) three 19" Dell™ LCD monitors. The monitors were viewed from a distance of 57cm. The
three monitors were positioned side-by-side as shown in Figure 2 to create a driving scene that
subtended a total of 130® H x 30° V of visual angle. The visual scene was rendered and updated
by DriveSafety™ driving simulator software running on four PC's (Dell Optiplex GX270) at a
rate of 60 Hz.
Evaluation ofthe MAG Safety and Elderly Mobility Sigh Project 
tO

Figure 2: Driving simulator
Road signs. Road sign images were generated using FlexiSign™ software and imported
into the driving simulation. Both overhead intersection and advance street signs were used.
Advance signs were placed at a distance of 150ft from the intersection and were always placed
on the right side of the road. The two road sign fonts were Clearview (as shown for the overhead
sign in Figure 3) and Series 200 Standard Highway Font (as shown for the overhead sign in
Figure 4). Both overhead and advance signs were presented in tlie same font.
Clearwater Rd
Figure 3: Example of Clearview Font overhead sign image.
Evaluation ofthe MAG

Clearwater or
Figure 4: Example of Standard Highway Font (Series 200) overhead sign image.
2.3 Procedure
Participants were asked to navigate through a virtual city in the driving simulator. The
city, shown in a top-down view in Figure 5, consisted of 4-lane roads with signaled intersections.
The surrounding environment (e.g., buildings, etc) were highly similar for each intersection so as
to provide no 'landmark' cues to the location in the virtual city. Each intersection was marked
with an overhead road sign. Drivers were instructed that the speed limit was 35 mph. A
speedometer was presented via a heads-up display on the center monitor. Other random traffic
was present along the roadway. The route that the driver followed on each run was indicated by
an auditory in-car navigation system. Each route consisted of 6 tums. An example route is
shown in Figure 5.
Evaludtion
12

Figure 5 - 
Overhead view of the driving environment
Pre-recorded auditory messages were given to drivers such as, "Next turn: Left on
Washington Street". 
Messages were presented roughly 10 sec after the completion of the
previous turn. Participants were also given the option of hearing the navigation instruction again
at any time by asking the experimenter.
Participants were instructed to press the blue 'x' button on the steering wheel (see Figure
2) as soon as they could read the road sign at each intersection. They were then asked to say the
name aloud and the experimenter recorded whether or not the name was accurately reported.
The distance fi-om the intersection at which the button was pressed was recorded by the
simulation software.
Evaluation of the MAG Safety and Elderly Mobility Sign Project
13"

Each participant completed 4 drives (Day/CIearview, Day/Standard, Night/Clearview,
and Night/Standard). The street names for each drive are shown in Table 1. These names were
chosen to have a moderate level of confusability. The order of these drives was counterbalanced
across participants.
Participants also completed two types of questionnaires. Following each drive they were
asked to rate the difficulty of reading the road signs in the condition they just completed on a
scale of 1-5. The following categories were assigned to each number: 1 ("effortless to read"), 2
("easy to read"), 3("about average level of reading difficulty), 4("somewhat difficult to read),
and 5("very difficult to read"). Following the completion of all drives they were shovm
examples of the two fonts (e.g., Figures 3 and 4) and were asked t make a forced choice as to
which was easier to read.
Table 1: Street sign names
Track 1
Track 2
Tracks
Track 4
Sterling Dr
Hampton Dr
Drummer Rd
Pleasant Rd
Shumway Ave
Madison Dr
Clearwater Dr
Hardwood Dr
Princeton Dr
Montana Dr
Clearview Dr
Hampton Dr
Hampton Dr
Newcastle Rd
Aurora Ave
Alderwood Ave
Montana Rd
Washington St
Amandor Rd
Raymond Ave
Newport St
Whispering St
Madero Rd
Aurora Rd
Rochester Rd
Crescent Rd
Freestone Rd
Fountain Dr
Peterson Rd
Cleaiwater Rd
Emerald Rd
Lavender Dr
Redwood St
Claibome Dr
Crismon Dr
Inverness Dt
Pheasant Rd
Grandview Dr
Crescent St
Larkspur Dr
Pineridge Dr
Creekwood Dr
2.4 Data Analysis
Evaluation ofthe MAG Safety and Elderly Mobility Sigh Project
14

Several different dependent measures were taken to quantify the effect of road signage on
driver perception and performance. To allow for comparison with previous research, the
recognition distance for all road signs was analyzed. To extend previous research we also
analyzed several performance variables including turn errors (instances in which the driver
either made a turn on an incorrect street or missed a turn), lane change distance (the distance
from the intersection for which the driver changed lanes when a left turn was required), lane
position variance, intersection approach speed and collision with other vehicles. Finally, the
subjective reading difficulty ratings were also analyzed. All of these variables were analyzed
statistically using separate 2x2 repeated measures ANOVAs with Sign Font and Time of Day as
factors. Detailed results from these analyses are provided in Appendix A.
Evaluation ofthe MAG Safety and Elderly Mobility Sign Project 
15

3. Results
S.l Sign Recognition Distance
Figure 6 plots the mean simulated distance from the advance sign at which participants
pressed the steering wheel button. Only responses for which participants correctly named the
street are included (across all participants 12% of responses were discarded due to naming
errors). Consistent with previous research (Hawkins et al. 1999), recognition distances in both
day and night conditions were larger for Clearview font than for standard font and the magnitude
of this difference was greater for simulated nighttime conditions. On average, recognition
distance was 12.1 ft (8.0%) larger for daytime and 15.4 ft (10.9%) for nighttime. The statistical
analysis revealed significant main effects of Font [F(l, 35)=182.2, p<0.001] and Time of Day
[F(l, 35)=217.1, p<0.01] and a significant Font x Time of Day Interaction [F(l, 35)=7.7,
p<0.01].
Evaluation ofthe MAG Safety and Elderly Mobility Sign Project 
16

I Clearview
□ Standard
160
•E 
150
« 145
Day
Night
Time of Day
Figure 6 - Mean sign recognition and legibility distances
3.2 Number of Turn Errors
Figure 7 plots the mean number of turn errors per condition. These errors include both
instances when the driver failed to execute a turn indicated by the navigation system and when
they made a turn that was not instructed. Although the number of turn errors was relatively
small (<1 per condition), drivers did make fewer errors with the Clearview font tlian with the
Standard font. The magnitude of reduction was not substantially different for day and night
conditions (0.45 vs 0.35 reduction in tum errors respectively). The statistical analysis revealed a
significant main effects of Font [F(I, 35)=32.4, p<0.001]. Neither the main effect of Time of
Day nor the Font x Time of Day interaction were statistically significant.
Evaluation of the MAG Safety and Elderly Mobility Si^ Project
17

11
0.9 -
0.8 -
0.7 -
0.6 -
0.5 -
0.4 -
0.3 -
0.2 -
0.1 -
0
I Clearview
□ standard
Day
Night
Time of Day
Figure 7- Mean Number of Turn Errors
3.3 Lane Change Distance
Figure 8 shows the mean distance from the intersection at which drivers changed from
the right lane to left lane when executing a left tum. Lane changes were executed further from
the intersection for Clearview font than for Standard font. The magnitude of difference between
the two fonts was slightly larger under simulated night conditions: Clearview font resulted in an
increase in lane changed distance of 1.4ft (3.1%) for day and 2.1ft (5.6%) for night. The
statistical analysis revealed significant main effects of Font [F(l, 35)=59.7, p<0.001] and Time
of Day [F(l, 35)=31.0, p<0.001]. The Font x Time of Day Interaction was not significant
indicating that there was no statistical difference in the effect of font between day and night.
Evaluation ofthe MAG Safety and Elderly Mobility Sign Project
18

110
I Clearview
□ Standard
£ 100
Day
Night
Time of Day
Figure 8- Mean Lane Change Distance
3.4 Intersection Approach Speed
Figure 9 plots the mean driving speed measured at a distance of 200ft from the intersection.
Note that the speed limit in the simulation was 35 mph as indicated by the horizontal line. In the
conditions with the Standard font driving speed was slower (and well below the speed) limit as
compared to conditions with Clearview font indicating that drivers were having a more difficult
time reading signs in the former case. The magnitude of speed difference was larger in day (-
3.7mph, 11.5%) than in night (-2.1%, 6.8%) conditions. The statistical analysis revealed
significant main effects of Font [F(l, 35)=19.9, p<0.001] and Time of Day [F(l, 35)^30.0,
p<0.001]. The Font x Time of Day Interaction was not significant indicating that there was no
statistical difference in the effect of font between day and night.
Evaluation ofthe MAG Safety and Elderly Mobility Sign Project
T9-

I Clearview
□ Standard
Day
Night
Time of Day
Figure 9- Mean Intersection Approach Speed
3.5 Lane Variability & Collisions
We also analyzed lane position variability (i.e., the extent to which the driver was
weaving within their lane) and the number of collisions. There were no significant differences
found for these variables between any of the conditions. 
Mean lane position variability data
were as follows: Day/Clearview: 0.9, Day/Standard: 0.92, Night/Clearview: 1.2, Night/Standard:
1.1. Mean number of collisions data were as follows; Day/Clearview: 0.1, Day/Standard: 0.22,
Night/Clearview: 0.31, Night/Standard: 0.36.
3.6 Questionnaire Data
Figure 10 shows the mean reading difficulty rating (out of 5) for each of the conditions.
On average drivers rated Clearview font as slightly easier to read than Standard font. The
Evaluation 
nti/T Flriprlv Mnhilifv StiPri PfhTp.rt 
20

magnitude of the differences in ratings were 0.22 (5.5%) for daytime and 0.17 (4.4%) for
nighttime. The statistical analysis revealed marginally significant effects of Font [F(l, 35)=3.9,
pa:0.05] and Time of Day [F(l, 35)=5.2, ps:0.04]. The Font x Time of Day Interaction was not
significant indicating that there was no statistical difference in the effect of font between day and
night.
I Ciearview
□ Standard
Day
Night
Time of Day
Figure 10- Mean Difficulty Rating
For tlie forced choice question asked at the end of the study 21/32 (66%) participants
indicated that the Ciearview font sign was easier to read.
Evaluation ofthe MAG Safety and Elderly Mobility Sign Project
21

4. Conclusions
Consistent with previous research on Clearview font signage (Hawkins et al. 1999;
Carlson et al 2001) we found consistent but modest improvements in sign recognition
performance for Clearview font as compared to Standard font. The 8-10% increase in sign
recognition distance for Clearview signs found in our driving simulator study was similar to that
reported in the previous studies using instrumented vehicles and real signs. This indicates that
that the driving simulation used in our study has good external validity. For a driver traveling at
35 mph (the speed limit in our simulated urban environment), the legibility improvement of 14ft
found in our study would equate to an extra 0.35 seconds to read the sign.
Expanding on previous research we also found consistent effects of Clearview font on
measures of driving performance and safety. When Clearview signs were used, drivers in our
study made significantly fewer turn errors, changed lanes for an upcoming left turn at a
significantly greater distance from the intersection and drove at a speed closer to the designated
speed limit. All of these factors would be expected to improve safety and mobility under real
driving conditions. When drivers miss a turn they often panic and execute a dangerous driving
maneuver such as a rapid U-tum. Furthermore, for elderly drivers a high frequency of missed
turns can reduce driving confidence resulting in less driving and decreased mobility (Ball et al,
1993). Switching lanes earlier for an upcoming turn is indicative of better anticipation and
planning by the driver and would make it less likely that they would have to make a sudden lane
change when they are close to the intersection (Groeger, 2000). Such sudden lane changes
increase the probability of missing a vehicle in the driver's blind spot and having a side swipe
accident. Finally, driving closer to the speed limit improves driving safety by decreasing the
chance that an impatient driver will attempt to make a dangerous maneuver to get around the
Evaluation ofthe MAG Safety and Elderly Mobility Sign Project 
22

slower moving driver. Driving too far below the posted speed limit has been linked with
accidents in elderly drivers (Ball et al, 1993). Note that there were also fewer collisions in the
Clearview conditions although this difference was not statistically significant.
As discussed above, the impact of Clearview font on sign legibility is expected to be
greatest under night driving conditions due to the reduction of potential blooming effects (see
Figure 1). Differences between the effectiveness of Clearview font under day and night
conditions were somewhat inconsistent in our driving simulator study. Only for sign recognition
distance did we find that the effect of Clearview font was significantly greater under nighttime
conditions than daytime conditions. For all driving perfonnance variables, the improvement in
safety associated with the use of Clearview signs was similar in magnitude (and not statistically
different) for simulated day and night conditions. This is perhaps not surprising given that we
could not accurately simulate microprismatic sheeting and blooming effects in our driving
simulation. A more advanced simulation that allowed for more complex illumination algorithms
and sign imagery would be needed to properly evaluate the effect of Clearview font under
nighttime conditions. However, the findings of our study also suggest that the improvements in
sign legibility associated with the usage of Clearview font are not solely due to the reduction of
blooming effects at night.
Drivers' subjective impressions of sign readability (assessed through post-driving
questionnaires) were consistent with the quantitative effects found for driving performance.
However, the effect magnitude for ratings was much smaller. While large, highly-significant
improvements on driving performance and recognition distance were found for Clearview font,
drivers' ratings of sign readability were only marginally better for Clearview font as compared to
Standard font. For example, the number of turn errors decreased by 40-62% in the Clearview
Evaluation ofthe MAG Safety and Elderly Mobility Sign Project 
23

conditions while the ratings of sign readability were only 4-6% greater. In addition, about two
thirds of drivers indicated that the Clearview sign was easier to read when forced to make a
choice. This occurred even though all participants in our study showed driving safety
improvements in the Clearview conditions. This mismatch between performance data and
subjective judgments is consistent with several previous studies on human performance (e.g..
Gray et al., 2006) and can be explained by the theory that perception for conscious judgment
involves different areas of the brain than perception for the control of action (Goodale & 
Milner,
1992). According to this theory, when participants are reading the signs while driving they are
using the dorsal areas of their brain while when they are asked to make a passive judgment they
are using the ventral areas. Therefore, it is not surprising that the effects of the two are different.
In practical terms this is a very important point to emphasis: the measured improvements in
driving safety are much greater than one might predict from making a passive judgment about
the signs. Even if someone indicates something to the effect "I don't see any difference between
the signs" we would still expect a significant improvement in driving safety and mobility.
Given the consistent improvements in driving safety and mobility it is recommend that
the Maricopa Association of Governments continue to encourage member agencies to expand
their adoption of Clearview road signs.
Evaluation ofthe MAG Safety and Elderly Mobility Sign Project 
24

Sources Referenced:
Ball, K., C. Owsley, et al. (1993). Visual-attention problems as a predictor of vehicle crashes
in older dnwtrs. Investigative Ophthalmology & Visual Science, 34, 3110-3123.
Garvey, P. M., Pietrucha, M. T., & 
Meeker, D. (1997). Legibility of guide signs. Transportation
Research Record 1605, TRB, National Research Counil, Washington, D. C., pp 73-79.
Goodale, M. A. & 
Milner, A. D. 
(1992). Separate visual pathways for perception and
action. Trends in Neuroscience 15, 20-5.
Gray, R., Regan, D. Castaneda, B., & 
Sieffert, R. (2006). Role of feedback in the accuracy of
perceived direction of motion in depth and control of interceptive action. Vision
Research, 46, 1676-1694.
Groeger, J. A. (2000). Understanding driving: Applying cognitive psychology to a complex
everyday task. Philadelphia, PA, Psychology Press.
Hawkins, H. G., Wooldridge, M. D., Kelly, A. B., Picha, D. L., & 
Greene, F. K. 
(1999).
Legibility comparison of three freeway guide sign alphabets. FHWA/TX-99/1276-lF,
College Station, Texas, May 1999.
Biggins, K. E. and Wood, J. M. (2005) Predicting components of closed road driving
performance from vision tests. Optom. Vis. Sci. 82, 647-656.
Carlson, P. J. (2001). Evaluation of clearview alphabet with microprismatic retroreflective
sheeting. Report 4049-1, College Station, Texas, October 2001.
Wood, J. M. and D. A. Owens (2005). Standard measures of visual acuity do not predict drivers'
recognition performance under day or night conditions. Optometry and Vision Science,
82, 698-705.
Evaluation of the MAG Safety and Elderly Mobility Sign Project 
25

Recognition Distance
Appendix - 
Data Analyses
Wtthin-Subjects Factors
Measure:MEASURE 1
Timeof
Dependent
Day 
Font
Variable
-
1
 1
Day_CV
2
Day_st
-
2
 1
Night_CV
2
Night_St
Tests of Within-Subjects Effects
Measure:MEASURE 1
Type ill Sum of
Source
Squares
df
Mean Square
F
Sig.
TimeofDay
Sphericity Assumed
471.614
1
471.614
182.158
.000
Greenhouse-Geisser
471.614
1.000
471.614
182.158
.000
Huynh-Feldt
471.614
1.000
471.614
182.158
.000
Lower-bound
471.614
1.000
471.614
182.158
.000
Error(TimeofDay)
Sphericity Assumed
90.616
35
2.589
Greenhouse-Geisser
90.616
35.000
2.589
Huynh-Feidt
90.616
35.000
2.589
Lower-bound
90.616
35.000
2.589
Font
Sphericity Assumed
1309.234
1
1309.234
217.093
.000
Greenhouse-Geisser
1309.234
1.000
1309.234
217.093
.000
Huynh-Feidt
1309.234
1.000
1309.234
217.093
.000
Lower-bound
1309.234
1.000
1309.234
217.093
.000
Error(Font)
Sphericity Assumed
211.076
35
6.031
Evaluation of the MAG Safety and Elderly Mobility Sign Project
26

Greenhouse-Geisser
211.076
35.000
6.031
Huynh-Feldt
211.076
35.000
6.031
Lower-bound
211076
35.000
6.031
TimeoflDay * Font
Sphericity Assumed
18.347
1
18.347
7.702
.009
Greenhouse-Geisser
18.347
1.000
18.347
7.702
.009
Huynh-Feldt
18.347
1.000
18.347
7.702
.009
Lower-bound
18.347
1.000
18.347
7.702
.009
Error(TimeofDay*Font)
Sphericity Assumed
83.373
35
2.382
Greenhouse-Geisser
83.373
35.000
2.382
Huynh-Feldt
83.373
35.000
2.382
Lower-bound
83.373
35.000
2.382
Titrn Errors
Within-Subjects Factors
Measure:MEASURE 1
Timeof
Dependent
Day 
Font
Variable
1
 1
Day_CV
2
Day_st
2
 1
Night_CV
-
2
Night_St
Measure:WIEASURE 1
Tests of Within-Subjects Effects
Evaluation ofthe MAG Safety and Elderly Mobility Sign Project
27

Type III Sum of
Source
Squares
df
Mean Square
F
Sig.
TimeofDay
Sphericity Assumed
.694
1
.694
1.000
.324
Greenhouse-Geisser
.694
1.000
.694
1.000
.324
Huynh-Feldt
.694
1.000
.694
1.000
.324
Lower-bound
.694
1.000
.694
1.000
.324
EiTor{TimeofDay)
Sphericity Assumed
24.306
35
.694
Greenhouse-Geisser
24.306
35.000
.694
Huynh-Feldt
24.306
35.000
.694
Lower-bound
24.306
35.000
.694
*
Font
Sphericity Assumed
6.250
1
6.250
32.407
.000
Greenhouse-Geisser
6.250
1.000
6.250
32.407
.000
•
Huynh-Feldt
6.250
1.000
6.250
32.407
.000
Lower-bound
6.250
1.000
6.250
32.407
.000
-
Error(Font)
Sphericity Assumed
6.750
35
.193
Greenhouse-Geisser
6.750
35.000
.193
-
Huynh-Feldt
6.750
35.000
.193
Lower-bound
6.750
35.000
.193
-
TimeofDay * Font
Sphericity Assumed
.250
1
.250
1.129
.295
Greenhouse-Geisser
.250
1.000
.250
1.129
.295
-
Huynh-Feldt
.250
1.000
.250
1.129
.295
Lower-bound
.250
1.000
.250
1.129
.295
Error(TimeofDay*Font)
Sphericity Assumed
7.750
35
.221
Greenhouse-Geisser
7.750
35.000
.221
mm
Huynh-Feldt
7.750
35.000
.221
Lower-bound
7.750
35.000
.221
Lane Change Distance
Evaluation of the MAG Safety and Elderly Mobility Sign Project
28

Within-Subjects Factors
Measure:MEASURE 1
Timeof
Day 
Font
Dependent
Variable
1
 1
2
Day_CV
Day_st
2
 1
2
Night_CV
Night_St
Tests of Within-Subjects Effects
Measure:l\/IEASURE 1
Type III Sum of
Source
Squares
df
Mean Square
F
Sig.
TimeofDay
Sphericity Assumed
1342.001
1
1342.001
59.698
.000
Greenhouse-Geisser
1342.001
1.000
1342.001
59.698
.000
Huynh-Feldt
1342.001
1.000
1342.001
59.698
.000
Lower-bound
1342.001
1.000
1342.001
59.698
.000
Error{TimeofDay)
Sphericity Assumed
786.794
35
22.480
Greenhouse-Geisser
786.794
35.000
22.480
Huynh-Feldt
786.794
35.000
22.480
Lower-bound
786.794
35.000
22.480
Font
Sphericity Assumed
104.380
1
104.380
30.946
.000
Greenhouse-Geisser
104.380
1.000
104.380
30.946
.000
Huynh-Feldt
104.380
1.000
104.380
30.946
.000
Lower-bound
104.380
1.000
104.380
30.946
.000
Error(Font)
Sphericity Assumed
118.055
35
3.373
Greenhouse-Geisser
118.055
35.000
3.373
Huynh-Feldt
118.055
35.000
3.373
Lower-bound
118.055
35.000
3.373
TimeofDay * Font
Sphericity Assumed
4.271
1
4.271
1.045
.314
Evaluation of the MAG Safety and Elderly Mobility Sign Project
29

Greenhouse-Geisser
4.271
1.000
4.271
1.045
.314
Huynh-Feldt
4.271
1.000
4.271
1.045
.314
Lower-bound
4.271
1.000
4.271
1.045
.314
Error(TimeofDay'Font) 
Sphericity Assumed
143.074
35
4.088
Greenhouse-Geisser
143.074
35.000
4.088
Huynh-Feldt
143.074
35.000
4.088
Lower-bound
143.074
35.000
4.088
Intersection Approach Speed
Within-Subjects Factors
Measure:MEASURE 1
Timeof
Day 
Font
Dependent
Variable
1
 1
2
Day_CV
Day_st
2
 1
2
Night_CV
Night_St
Tests of Within-Subjects Effects
Measure:MEASURE 1
Type III Sum of
Source
Squares
df
Mean Square
F
SIg.
TimeofDay
Sphericity Assumed
162.988
1
162.988
19.903
.000
Greenhouse-Geisser
162.988
1.000
162.988
19.903
.000
Huynh-Feldt
162.988
1.000
162.988
19.903
.000
Lower-bound
162.988
1.000
162.988
19.903
.000
Error{TimeofDay)
Sphericity Assumed
286.612
35
8.189
Greenhouse-Geisser
286.612
35.000
8.189
Huynh-Feldt
286.612
35.000
8.189
Evaluation of the MAG Safety and Elderly Mobility Sign Project
30

Lower-bound
286.612
35.000
8.189
M
Font
Sphericity Assumed
316.840
1
316.840
30.038
.000
Greenhouse-Geisser
316.840
1.000
316.840
30.038
.000
Huynh-Feldt
316.840
1.000
316.840
30.038
.000
Lower-bound
316.840
1.000
316.840
30.038
.000
Error(Font)
Sphericity Assumed
369.180
35
10.548
Greenhouse-Geisser
369.180
35.000
10.548
Huynh-Feldt
369.180
35.000
10.548
Lower-bound
369.180
35.000
10.548
TimeofDay * Font
Sphericity Assumed
24.668
1
24.668
4.271
.046
mm
Greenhouse-Geisser
24.668
1.000
24.668
4.271
.046
Huynh-Feidt
24.668
1.000
24.668
4.271
.046
Lower-bound
24.668
1.000
24.668
4.271
.046
Error(TimeofDay*Font)
Sphericity Assumed
202.142
35
5.775
Mi
Greenhouse-Geisser
202.142
35.000
5.775
Huynh-Feldt
202.142
35.000
5.775
-
Lower-bound
202.142
35.000
5.775
Reading Difficulty Ratings
Within-Subjects Factors
Measure:MEASURE 1
-
Timeof
Dependent
Day 
Font
Variable
m
1
 1
Day_CV
2
Day_st
2
 1
Night_CV
2
Night_St
Tests of Within-Subjects Effects
Evaluation of the MAG Safety and Elderly Mobility Sign Project
31

Measure:MEASURE 1
Type III Sum of
Source
Squares
df
Mean Square
F
Sig.
II
TlmeofDay
Sphericity Assumed
2.250
1
2.250
5.164
.029
Greenhouse-Geisser
2.250
1.000
2.250
5.164
.029
Huynh-Feldt
2.250
1.000
2.250
5.164
.029
Lower-bound
2.250
1.000
2.250
5.164
.029
EiTor(TimeofDay)
Sphericity Assumed
15.250
35
.436
Greenhouse-Geisser
15.250
35.000
.436
Huynh-Feldt
15.250
35.000
.436
tm
Lower-bound
15.250
35.000
.436
Font
Sphericity Assumed
1.361
1
1.361
3.924
.055
■*
Greenhouse-Geisser
1.361
1.000
1.361
3.924
.055
Huynh-Feldt
1.361
1.000
1.361
3.924
.055
-
Lower-bound
1.361
1.000
1.361
3.924
.055
Error(Font)
Sphericity Assumed
12.139
35
.347
-
Greenhouse-Geisser
12.139
35.000
.347
Huynh-Feldt
12.139
35.000
.347
Lower-bound
12.139
35.000
.347
TlmeofDay * Font
Sphericity Assumed
.028
1
.028
.103
.751
Greenhouse-Geisser
.028
1.000
.028
.103
.751
Huynh-Feldt
.028
1.000
.028
.103
.751
Lower-bound
.028
1.000
.028
.103
.751
Error(TimeofDay*Font)
Sphericity Assumed
9.472
35
.271
m
Greenhouse-Geisser
9.472
35.000
.271
Huynh-Feldt
9.472
35.000
.271
Lower-bound
9.472
35.000
.271
Evaluation ofthe MAG Safety and Elderly Mobility Sign Project
32

EXHIBITS

DRIVER VISUAL BEHAVIOR IN THE PRESENCE OF COMMERCIAL
ELECTRONIC VARIABLE MESSAGE SIGNS (CEVMS)
SEPTEMBER 2012
U^. Department of Transportation 
FHWA-HEP-
Federal Highway
Administration

FOREWORD
The advent of eiectronic billboard technologies, in particular the digital Light-Emitting Diode
(LED) 
billboard, has necessitated a reevaluation of current legislation and regulation for
controlling outdoor advertising. In this case, one of the concerns is possible driver distraction. In
the context of the present report, outdoor advertising signs employing this new advertising
technology are referred to as Commercial Electronic Variable Message Signs (CEVMS). They
are also commonly referred to as Digital Billboards and Electronic Billboards.
The present report documents the results of a study conducted to investigate the effects of
CEVMS used for outdoor advertising on driver visual behavior in a roadway driving
environment. The report consists of a brief review of the relevant published literature related to
billboards and visual distraction, the rationale for the Federal Highway Administration research
study, the methods by which the study was conducted, and the results of the study, which used an
eye tracking system to measure driver glances while driving on roadways in the presence of
CEVMS, 
standard billboards, and other roadside elements. The report should be of interest to
highway engineers, traffic engineers, highway safety specialists, the outdoor advertising
industry, environmental advocates, Federal policymakers, and State and local regulators of
outdoor advertising.
Monique R. Evans
Director, Office of Safety
Research and Development
Nelson Castellanos
Director, Office of Real Estate
Services
Notice
This document is disseminated under the sponsorship of the U.S. Department of Transportation
in the interest of information exchange. The U.S. Government assumes no liability for the use
of the information contained in this document. This report does not constitute a standard,
specification, or regulation.
The U.S. Government does not endorse products or manufacturers. Trademarks or
manufacturers' names appear in this report only because they are considered essential to the
objective of the document.
Quality Assurance Statement
The Federal Highway Administration (FHWA) 
provides high-quality information to serve
government, industry, and the public in a manner that promotes public understanding. Standards
and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its
infonnation. The FHWA periodically reviews quality issues and adjusts its programs and
processes to ensure continuous quality improvement.

TECHNICAL DOCUMENTATION PAGE
1. Report No.
FHWA-HRT-
2. Government Accession No.
3. Recipient's Catalog No.
4. Title and Subtitle
Driver Visual Behavior in the Presence of Commercial Electronic Variable
Message Signs (CEVMS)
5. Report Date
6. Performing Organization Code
7. Author(s)
William A. Perez, Mary Anne Bertola, Jason F. Kennedy, and John A.
Molino
8. Performing Organization Report No.
9. Performing Organization Name and Address
SAIC
6300 Georgetown Pike
McLean, VA22101
10. Work Unit No. (TRAIS)
11. Contract or Grant No.
12. Sponsoring Agency Name and Address
OfSce of Real Estate Services
Federal Highway Administration
1200 New Jersey Avenue SE
Washington, DC 20590
13. Type ofReport and Period Covered
14. Sponsoring Agency Code
15. Supplementary Notes
The Contracting Officer's Technical Representatives (COTR) were Christopher Monk and Thomas Granda.
16. Abstract
This study was conducted to investigate the effect of CEVMS on driver visual behavior in a roadway driving
environment. An instrumented vehicle with an eye tracking system was used. Roads containing CEVMS, 
standard
billboards, and control areas with no off-premise advertising were selected. Data were collected on arterials and
freeways in the day and nighttime. Field studies were conducted in two cities where the same methodology was used
but there were differences in the roadway visual environment. The gazes to the road ahead were high across the
conditions; however, the CEVMS and billboard conditions resulted in a lower probability of gazes as compared to the
control conditions (roadways not containing off-premise advertising) with the exception of arterials in Richmond where
none of the conditions differed from each other. Examination of where drivers gazed in the CEVMS and standard
billboard conditions showed that gazes away from the road ahead were not primarily to the billboards. Average and
maximum fixations to CEVMS and standard billboards were similar across all conditions. However, four long dwell
times were found (sequential and multiple fixations) that were greater than 2,000 ms. One was to a CEVMS on a
freeway in the day time, two were to the same standard billboard on a freeway once in the day and once at night; and
one was to a standard billboard on an arterial at night. In Richmond, the results showed that drivers gazed more at
CEVMS than at standard billboards at night; however, in Reading the drivers were equally likely to gaze towards
CEVMS or standard billboards in day and night. The results of the study are consistent with research and theory on the
control of gaze behavior in natural environments. The demands of the driving task tend to affect the driver's self-
regulation of gaze behavior.
17. Key Words
Driver visual behavior, visual environment, billboards, eye tracking
system, commercial electronic variable message signs, CEVMS, 
visual
complexity
18. Distribution Statement
No restrictions.
19. Security Classif. (of this report)
Unclassified
20. Security Classif. (of this page)
Unclassified
21. No. of Pages
22. Price
Form DOTF 1700.7(8-72)
Reproduction of completed page authorized

SI* (MODERN METRIC) CONVERSION FACTORS 
|
APPROXIMATE CONVERSIONS TO SI UNITS 
1
Symbol
When You Know
Multiply By
To Find
Symbol
LENGTH
In
inches
25.4
millimeters
mm
fl
feet
0.305
meters
m
yd
yards
0.914
meters
m
mi
miles
1.61
kilometers
km
AREA
in^
square inches
645.2
square millimeters
mm^
ft^
square feet
0.093
square meters
m^
yd^
square yard
0.836
square meters
m^
ac
acres
0.405
hectares
ha
mP
square miles
2.59
square kilometers
km^
VOLUME
fl oz
fluid ounces
29.57
milliliters
mL
gal
gallons
3.785
liters
L
cubic feet
0.028
cubic meters
m^
yd^
cubic yards
0.765
cubic meters
m^
NOTE: volumes greater than 1000 I shall be shown in m
MASS
oz
ounces
28.35
grams
9
lb
pounds
0.454
kilograms
kg
T
short tons (2000 lb)
0.907
megagrams (or "metric ton")
Mg(or"t")
TEMPERATURE (exact degrees)
"F
Fahrenheit
5 
(F-32)/9
Celsius
"C
or (F-32)/1.8
ILLUMINATION
fc
foot-candles
10.76
lux
Ix
fl
foot-Lamberts
3.426
candela/m^
cd/m^
FORCE and PRESSURE or STRESS
Ibf
poundforce
4.45
newtons
N
Ibf/in^
poundforce per square inch 
6.89
kilopascals
kPa
1
 APPROXIMATE CONVERSIONS FROM SI UNITS 
|
Symbol
When You Know
Multiply By
To Find
Symbol
LENGTH
mm
millimeters
0.039
inches
in
m
meters
3.28
feet
ft
m
meters
1.09
yards
yd
km
kilometers
0.621
miles
mi
AREA
mm^
square millimeters
0.0016
square inches
in'
square meters
10.764
square feet
fP
m'
square meters
1.195
square yards
ycP
ha
hectares
2.47
acres
ac
km^
square kilometers
0.386
square miles
mi'
VOLUME
mL
milliliters
0.034
fluid ounces
fl oz
L
liters
0.264
gallons
gal
cubic meters
35.314
cubic feet
fl^
m'
cubic meters
1.307
cubic yards
ycf
MASS
9
grams
0.035
ounces
oz
kg
kilograms
2.202
pounds
lb
Mg (or "0
megagrams (or "metric ton") 
1.103
short tons (2000 lb)
T
TEMPERATURE (exact degrees)
0/H
O
C^sius
1.8C+32
Fahrenheit
"F
ILLUMINATION
Ix
lux
0.0929
foot-candles
fc
cd/m'
candela/m^
0.2919
foot-Lamberts
fl
FORCt and PRESSURE or STRESS
N
newtons
0.225
poundforce
Ibf
kPa
kilopascals
0.145
poundforce per square inch
Ibf/in'
'SI is the symbo! for the International SystHn of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380.
(Revised March 2003^

TABLE OF CONTENTS
EXECUTIVE SUMMARY
INTRODUCTION
BACKGROUND 
^5
Post-Hoc Crash Studies^ 
5
Field Investigations 
6
Laboratory Studies 
8
Summary 
9
STUDY APPROACH 
9
Research Questions 
12
EXPERIMENTAL APPROACH 
13
EXPERIMENTAL DESIGN OVERVIEW 
14
Site Selection 
14
READING 
16
METHOD 
16
Selection of Data Collection Zone Limits 
16
Advertising Conditions 
16
Photometric Measurement of Signs 
19
Visual Complexity 
_20
Participants 
^21
Procedures 
^21
DATA REDUCTION 
^23
Eye Tracking Measures 
^23
Other Measures 
25
RESULTS 
^26
Photometric Measurements 
^26
Visual Complexity 
27
Effects of Billboards on Gazes to the Road Ahead 
28
Fixations to CEVMS and Standard Billboards 
30
Comparison of Gazes to CEVMS and Standard Billboards 
36
Observation of Driver Behavior 
36
Level of Service 
36
DISCUSSION OF READING RESULTS 
37
RICHMOND 
40
METHOD 
40
Selection of DCZ Limits 
^40
Advertising Type 
40
Photometric Measurement of Signs 
^42
Visual Complexity 
_42
Participants 
^43
Procedures^ 
43
DATA REDUCTION 
^44
Eye Tracking Measures^ 
44
111

Other Measures 
^44
RESULTS 
^44
Photometric Measurement of Signs 
44
Visual Complexity 
^45
Effects of Billboards on Gazes to the Road Ahead 
45
Fixations to CEVMS and Standard Billboards 
^47
Comparison of Gazes to CEVMS and Standard Billboards 
50
Observation of Driver Behavior 
^51
Level of Service 
^51
DISCUSSION OF RICHMOND RESULTS 
^51
GENERAL DISCUSSION 
^53
CONCLUSIONS 
53
Do CEVMS attract drivers' attention away from the forward roadway and other driving
relevant stimuli? 
53
Do glances to CEVMS occur that would suggest a decrease in safety? 
54
Do drivers look at CEVMS more than at standard billboards? 
54
SUMMARY 
55
LIMITATIONS OF THE RESEARCH 
55
REFERENCES 
57
IV

LIST OF FIGURES
Figure 1. Eye tracking system camera placement. 
13
Figure 2. FHWA's field research vehicle. 
^14
Figure 3. DCZ with a target CEVMS on a freeway. 
,17
Figure 4. DCZ with a target CEVMS on an arterial. 
,18
Figure 5. DCZ with a target standard billboard on a freeway. 
18
Figure 6. DCZ with a target standard billboard on an arterial. 
18
Figure 7. DCZ for the control condition on a freeway. 
19
Figure 8. DCZ for the control condition on an arterial. 
^19
Figure 9. Screen capture showing static ROIs on a scene video output. 
^23
Figure 10. Mean feature congestion as a function of advertising condition and road
type (standard errors for the mean are included in the graph). 
21
Figure 11. Distribution of fixation duration for CEVMS in the daytime and nighttime. 
30
Figure 12. Distribution of fixation duration for standard billboards in the daytime and
nighttime. 
^31
Figure 13. Distribution of fixation duration for road ahead (i.e., top and bottom road
ahead ROIs) in the daytime and nighttime. 
31
Figure 14. Heat map for the start of a DCZ for a standard billboard at night on an
arterial. 
r>^
Figure 15. Heat map for the middle of a DCZ for a standard billboard at night on an
arterial. 
33
Figure 16. Heat map near the end of a DCZ for a standard billboard at night on an
arterial. 
33
Figure 17. Heat map for start of a DCZ for a standard billboard at night on a freeway. 
34
Figure 18. Heat map for middle of a DCZ for a standard billboard at night on a
freeway.^ 
^34
Figure 19. Heat map near the end of a DCZ for a standard billboard at night on a
freeway.^ 
^34
Figure 20. Heat map for the start of a DCZ for a standard billboard in the daytime on
a freeway. 
^35
Figure 21. Heat map near the middle of a DCZ for a standard billboard in the daytime
on a freeway.^ 
35
Figure 22. Heat map near the end of DCZ for standard billboard in the daytime on a
freeway. 
^35
Figure 23. Heat map at the end of DCZ for standard billboard in the daytime on a
freeway. 
^35
Figure 24. Example of identified salient areas in a road scene based on bottom-up
analysis. 
^38
Figure 25. Example of a CEVMS DCZ on a freeway. 
41
Figure 26. Example of CEVMS DCZ an arterial. 
^41
Figure 27. Example of a standard billboard DCZ on a freeway. 
^41
Figure 28. Example of a standard billboard DCZ on an arterial. 
42
Figure 29. Example of a control DCZ on a freeway. 
^42
Figure 30. Example of a control DCZ on an arterial. 
^42

Figure 31. Mean feature congestion as a function of advertising condition and road
type. 
45
Figure 32. Fixation duration for CEVMS in the day and at night. 
47
Figure 33. Fixation duration for standard billboards in the day and at night. 
^48
Figure 34. Fixation duration for the road ahead in the day and at night. 
^48
Figure 35. Heat map for first fixation to CEVMS with long dwell time. 
49
Figure 36. Heat map for later fixations to CEVMS with long dwell time. 
50
Figure 37. Heat map at end of fixations to CEVMS with long dwell time. 
50
VI

LIST OF TABLES
Table 1. Distribution of CEVMS by roadway classification for various cities. 
15
Table 2. Inventory of target billboards with relevant parameters. 
17
Table 3. Summary of luminance (cd/m^) and contrast (Weber ratio) measurements. 
21
Table 4. The probability of gazing at the road ahead as a function of advertising
condition and road type. 
^28
Table 5. Probability of gazing at ROIs for the three advertising conditions on arterials
and freeways. 
29
Table 6. Level of service as a function of advertising type, road type, and time of day. 
37
Table 7. Inventory of target billboards in Richmond with relevant parameters. 
40
Table 8. Summary of luminance (cd/m^) and contrast (Weber ratio) measurements. 
^44
Table 9. The probability of gazing at the road ahead as a function of advertising
condition and road type. 
^46
Table 10. Probability of gazing at ROIs for the three advertising conditions on
arterials and freeways. 
^46
Table 11. Estimated level of service as a function of advertising condition, road type,
and time of day. 
^51
Vll

LIST OF ACRONYMS AND SYMBOLS
M
CEVMS
Commercial Electronic Variable Message Sign
EB
Empirical Bayes
-
DCZ
Data Collection Zone
ROI
Region of Interest
LED
Light-Emitting Diode
••
IR
Infra-Red
CCD
Charge-Coupled Device
mi
MAPPS
Multiple-Analysis of Psychophysical and Performance Signals
GEE
Generalized Estimating Equations
FHWA
Federal Highway Administration
DOT
Department of Transportation
Vlll

EXECUTIVE SUMMARY
This study examines where drivers look when driving past commercial electronic variable
message signs (CEVMS), standard billboards, or no off-premise advertising. The results and
conclusions are presented in response to the three research questions listed below:
1. Do CEVMS attract drivers' attention away from the forward roadway and other driving-
relevant stimuli?
2. Do glances to CEVMS occur that would suggest a decrease in safety?
3. Do drivers look at CEVMS more than at standard billboards?
This study follows a Federal Highway Administration (FHWA) 
review of the literature on the
possible distracting and safety effects of off-premise advertising and CEVMS in particular. The
review considered laboratory studies, driving simulator studies, field research vehicle studies,
and crash studies. The published literature indicated that there was no consistent evidence
showing a safety or distraction effect due to off-premise advertising. However, the review also
enumerated potential limitations in the previous research that may have resulted in the finding of
no distraction effects for off-premise advertising. The study team recommended that additional
research be conducted using instrumented vehicle research methods with eye tracking
technology.
The eyes are constantly moving and they fixate (focus on a specific object or area), perform
saccades (eye movements to change the point of fixation), and engage in pursuit movements
(track moving objects). It is during fixations that we take in detailed information about the
environment. Eye tracking allows one to determine to what degree off-premise advertising may
divert attention away from the forward roadway. A fmding that areas containing CEVMS result
in significantly more gazes to the billboards at a cost of not gazing toward the forward roadway
would suggest a potential safety risk. In addition to measuring the degree to which CEVMS may
distract from the forward roadway, an eye tracking device would allow an examination of the
duration of fixations and dwell times (multiple sequential fixations) to CEVMS and standard
billboards. Previous research conducted by the National Highway Traffic Safety Administration
(NHTSA) 
led to the conclusion that taking your eyes off the road for 2 seconds or more presents
a safety risk. Measuring fixations and dwell times to CEVMS and standard billboards would also
allow a determination as to the degree to which these advertising signs lead to potentially unsafe
gaze behavior.
Most of the literature concerning eye gaze behavior in dynamic environments suggests that task
demands tend to override visual salience (an object that stands out because of its physical
properties) in determining attention allocation. When extended to driving, it would be expected
that visual attention will be directed toward task-relevant areas and objects (e.g., the roadway,
other vehicles, speed limit signs) and that other salient objects, such as billboards, would not
necessarily capture attention. However, driving is a somewhat automatic process and conditions
generally do not require constant, undivided attention. As a result, salient stimuli, such as
CEVMS, might capture driver attention and produce an unwanted increase in driver distraction.
The present study addresses this concern.
1

This study used an instrumented vehicle with an eye tracking system to measure where drivers
were looking when driving past CEVMS and standard billboards. The CEVMS and standard
billboards were measured with respect to luminance, location, size, and other relevant variables
to characterize these visual stimuli extensively. Unlike previous studies on digital billboards, the
present study examined CEVMS as deployed in two United States cities. These billboards did
not contain dynamic video or other dynamic elements, but changed content approximately every
8 to 10 seconds. The eye tracking system had nearly a 2-degree level of resolution that provided
significantly more accuracy in determining what objects the drivers were looking at compared to
an earlier naturalistic driving study. This study assessed two data collection efforts that employed
the same methodology in two cities.
In each city, the study examined eye glance behavior to four CEVMS, two on arterials and two
on freeways. There were an equal number of signs on the left and right side of the road for
arterials and freeways. The standard billboards were selected for comparison with CEVMS such
that one standard billboard environment matched as closely as possible that of each of the
CEVMS. Two control locations were selected that did not contain off-premise advertising, one
on an arterial and the other on a freeway. This resulted in 10 data collection zones in each city
that were approximately 1,000 feet in length (the distance from the start of the data collection
zone to the point that the CEVMS or standard billboard disappeared from the data collection
video).
In Reading, Pennsylvania, 14 participants drove at night and 17 drove during the day. In
Richmond, Virginia, 10 participants drove at night and 14 drove during the day. Calibration of
the eye tracking system, practice drive, and the data collection drive took approximately 2 hours
per participant to accomplish.
The following is a summary of the study results and conclusions presented in reference to the
three research questions the study aimed to address.
Do CEVMS attract drivers' attention away from the forward roadway and other driving
relevant stimuli?
® On average, the drivers in this study devoted between 73 and 85 percent of their visual
attention to the road ahead for both CEVMS and standard billboards. This range is
consistent with earlier field research studies. In the present study, the presence of
CEVMS did not appear to be related to a decrease in looking toward the road ahead.
Do glances to CEVMS occur that would suggest a decrease in safety?
• The average fixation duration to CEVMS was 379 ms and to standard billboards it was
335 ms across the two cities. The average fixation durations to CEVMS and standard
billboards were similar to the average fixation duration to the road ahead.
• The longest fixation to a CEVMS was 1,335 ms and to a standard billboard it was
1,284 ms. The current widely accepted threshold for durations of glances away from the
road ahead that result in higher crash risk is 2,000 ms. This value comes from a NHTSA

naturalistic driving study that showed a significant increase in crash odds when glances
away from the road ahead were 2,000 ms or longer.
• Four dwell times (aggregate of consecutive fixations to the same object) greater than
2,000 ms were observed across the two studies. Three were to standard billboards and
one was to a CEVMS. The long dwell time to the CEVMS occurred in the daytime to a
billboard viewable from a freeway. Review of the video data for these four long dwell
times showed that the signs were not far from the forward view while participant's gaze
dwelled on them. Therefore, the drivers still had access to information about what was in
front of them through peripheral vision.
• The results did not provide evidence indicating that CEVMS, as deployed and tested in
the two selected cities, were associated with unacceptably long glances away from the
road. When dwell times longer than the currently accepted threshold of 2,000 ms
occurred, the road ahead was still in the driver's field of view. This was the case for both
CEVMS and standard billboards.
Do drivers look at CEVMS more than at standard billboards?
• When comparing the probability of a gaze at a CEVMS versus a standard billboard, the
drivers in this study were generally more likely to gaze at CEVMS than at standard
billboards. However, some variability occurred between the two locations and between
the types of roadway (arterial or freeway).
•
 In Reading, when considering the proportion of time spent looking at billboards, the
participants looked more often at CEVMS than at standard billboards when on arterials
(63 percent to CEVMS and 37 percent to a standard billboard), whereas they looked more
often at standard billboards when on freeways (33 percent to CEVMS and 67 percent to a
standard billboard). In Richmond, the drivers looked at CEVMS more than standard
billboards no matter the type of road they were on, but as in Reading, the preference for
gazing at CEVMS was greater on arterials (68 percent to CEVMS and 32 percent to
standard billboards) than on freeways (55 percent to CEVMS and 45 percent to standard
billboards). When a gaze was to an off-premise advertising sign, the drivers were
generally more likely to gaze at a CEVMS than at a standard billboard.
•
 In Richmond, the drivers showed a preference for gazing at CEVMS versus standard
billboards at night, but in Reading the time of day did not affect gaze behavior. In
Richmond, drivers gazed at CEVMS 71 percent and at standard billboards 29 percent at
night. On the other hand, in the day the drivers gazed at CEVMS 52 percent and at
standard billboards 48 percent.
©
 In Reading, the average gaze dwell time for CEVMS was 981 ms and for standard
billboards it was 1,386 ms. The difference in these average dwell times was not
statistically significant. In contrast, the average dwell times to CEVMS and standard
billboards were significantly different in Richmond (1,096 ms and 674 ms, respectively).

The present data suggest that the drivers in this study directed the majority of their visual
attention to areas of the roadway that were relevant to the task at hand (e.g., the driving task).
Furthermore, it is possible, and likely, that in the time that the drivers looked away from the
forward roadway, they may have elected to glance at other objects in the surrounding
environment (in the absence of billboards) that were not relevant to the driving task. When
billboards were present, the drivers in this study sometimes looked at them, but not such that
overall attention to the forward roadway decreased.
It also should be noted that, like other studies in the available literature, this study adds to the
knowledge base on the issues examined, but does not present definitive answers to the research
questions investigated.

INTRODUCTION
"The primary responsibility ofthe driver is to operate a motor vehicle safely. The task ofdriving
requiresfull attention and focus. Drivers should resist engaging in any activity that takes their
eyes and attention off of the road for more than a couple ofseconds. In some circumstances even
a second or two can make all the difference in a driver being able to avoid a crash." — US
Department of Transportation^'^
The advent of electronic billboard technologies, in particular the digital Light-Emitting Diode
(LED) billboard, has prompted a reevaluation of regulations for controlling outdoor advertising.
An attractive quality of these LED billboards, which are hereafter referred to as Commercial
Electronic Variable Message Signs (CEVMS), is that advertisements can change almost
instantly. Furthermore, outdoor advertising companies can make these changes from a central
remote office. Of concern is whether or not CEVMS may attract drivers' attention away from the
primary task (driving) in a way that compromises safety.
The current Federal Highway Administration (FHWA) 
guidance recommends that CEVMS
should not change content more frequently than once every 8 seconds.^^^ However, according to
Scenic America, the basis of the safety concern is that the ".. .distinguishing trait..." of a
CEVMS "... is that it can vary while a driver watches it, in a setting in which that variation is
likely to attract the drivers' attention away from the roadway."^^^This study was conducted to
provide the FHWA with data to determine if CEVMS capture visual attention differently than
standard off-premise advertising billboards.
BACKGROUND
A 2009 review of the literature by Molino et al. for the FHWA failed to find convincing
empirical evidence that CEVMS, as currently implemented, constitutes a safety risk greater than
that of conventional vinyl billboards.^'*^ A great deal of work has been focused in this area, but
the findings of these studies have been mixed.^"^'^^ A summary of the key past findings is
presented here, but the reader is referred to Molino et al. for a comprehensive review of studies
prior to 2008.^'^'
Post-Hoc Crash Studies
Post-hoc crash studies use reviews of police traffic collision reports or statistical summaries of
such reports in an effort to understand the causes of crashes that have taken place in the vicinity
of some change to the roadside environment. In the present case, the change of concern is the
introduction of CEVMS to the roadside or the replacement of conventional billboards with
CEVMS.
The literature review conducted by Molino et al. did not find compelling evidence for a
distraction effect attributable to CEVMS.^'^^ The authors concluded that all post-hoc crash studies
are subject to certain weaknesses, most of which are difficult to overcome. For example, the vast
majority of crashes are never reported to police; thus, such studies are likely to underreport
crashes. Also, when crashes are caused by factors such as driver distraction or inattention, the
involved driver may be unwilling or unable to report these factors to a police investigator.

Another weakness is that police, under time pressure, are rarely able to investigate the true root
causes of crashes unless they involve serious injury, death, or extensive property damage.
Furthermore, to have confidence in the results, such studies need to collect comparable data
before and after the change, and, in the after phase, at equivalent but unaffected roadway
sections. Since crashes are infrequent events, data collection needs to span extended periods of
time both before and after introduction of the change. Few studies are able to obtain such
extensive data.
Two recent studies by Tantala and Tantala examined the relationship between the presence of
CEVMS and crash statistics in Richmond, Virginia, and Reading, Pennsylvania.^^' ^ For the
Richmond area, 7 years of crash data at 10 locations with CEVMS were included in the analyses.
The study used a before-after methodology where most sites originally contained vinyl billboards
(before) that were converted to CEVMS (after). The quantity of crash data was not the same for
all locations and ranged from 1 year before/after to 3 years before/after. The study employed the
Empirical Bayes (EB) method to analyze the data.^^^ The results indicated that the total number
of crashes observed was consistent with what would be statistically expected with or without the
introduction of CEVMS. The analysis approach for Reading locations was much the same as for
Richmond other than there were 20 rather than 10 CEVMS and 8 years of crash statistics. The
EB method showed results for Reading that were very similar to those of Richmond.
The studies by Tantala and Tantala appear to address many of the concerns from Molino et al.
regarding the weaknesses and issues associated with crash studies.^"^'^'^^ For example, they
include crash comparisons for locations within multiple distances of each CEVMS to address
concerns about the visual range used in previous analyses. They used EB analysis techniques to
correct for regression-to-mean bias. Also, the EB method would better reflect crash rate changes
due to changes in average daily traffic and the interactions of these with the roadway features
that were coded in the model. The studies followed approaches that are commonly used in post-
hoc crash studies, though the results would have been strengthened by including before-after
results for non-CEVMS locations as a control group.
Field Investigations
Field investigations include unobtrusive observation, naturalistic driving studies, on-road
instrumented vehicle investigations, test track experiments, driver interviews, surveys, and
questionnaires. The following focuses on relevant studies that employed naturalistic driving and
on-road instrumented vehicle research methods.
Lee, McElheny, and Gibbons undertook an on-road instrumented vehicle study on Interstate and
local roads near Cleveland, Ohio.^^^ The study looked at driver glance behavior in the vicinity of
digital billboards, conventional billboards, comparison sites (sites with buildings and other signs,
including digital signs), and control sites (those without similar signage). The results showed that
there were no differences in the overall glance patterns (percent eyes-on-road and overall number
of glances) between the different sites. Drivers also did not glance more frequently in the
direction of digital billboards than in the direction of otlier event types (conventional billboards,
comparison events, and baseline events) but drivers did take longer glances in the direction of
digital billboards and comparison sites than in the direction of conventional billboards and
baseline sites. However, the mean glance length toward the digital billboards was less than

1,000 ms. It is important to note that this study employed a video-based approach for examining
drivers' visual behavior, which has an accuracy of no better than 20 degrees/'^^ While this
technique is likely to be effective in assessing gross eye movements and looks that are away
from the road ahead, it may not have sufficient resolution to discriminate what specific object the
driver is looking at outside of the vehicle.
Beijer, Smiley, and Eizenman evaluated driver glances toward four different types of roadside
advertising signs on roads in the Toronto, Canada, area.*-^ The four types of signs were: (a)
billboard signs with static advertisements; (b) billboard advertisements placed on vertical rollers
that could rotate to show one of three advertisements in succession; (c) scrolling text signs with a
minor active component, which usually consisted of a small strip of lights that formed words
scrolling across the screen or, in some cases, a larger area capable of displaying text but not
video; and (d) signs with video images that had a color screen capable of displaying both moving
text and moving images. The study employed an on-road instrumented vehicle with a head-
mounted eye tracking device. The researchers found no significant differences in average glance
duration or the maximum glance duration for the various sign types; however, the number of
glances was significantly lower for billboard signs than for the roller bar, scrolling text, and
video signs.
Smiley, Smahel, and Eizenman conducted a field driving study that employed an eye tracking
system that recorded drivers' eye movements as participants drove past video signs located at
three downtown intersections and along an urban expressway.^'^^ The study route included static
billboards and video advertising. The results of the study showed that on average 76 percent of
glances were to the road ahead. Glances at advertising, including static billboards and video
signs, constituted 1.2 percent of total glances. The mean glance durations for advertising signs
were between 500 ms and 750 ms, although there were a few glances of about 1,400 ms in
duration. Video signs were not more likely than static commercial signs to be looked at when
headways were short; in fact, the reverse was the case. Furthermore, the number of glances per
individual video sign was small, and statistically significant differences in looking behavior were
not found.
Kettwich, Kartsen, Klinger, and Lemmer conducted a field study where drivers' gaze behavior
was measured with an eye tracking system.^^^^ Sixteen participants drove an 11.5 mile (18.5 km)
route comprised of highways, arterial roads, main roads, and one-way streets in Karlsruhe,
Germany. The route contained advertising pillars, event posters, company logos, and video
screens. Mean gaze duration for the four types of advertising was computed for periods when the
vehicle was in motion and when it was stopped. Gaze duration while driving for all types of
advertisements was under 1,000 ms. On the other hand, while the vehicle was stopped, the mean
gaze duration for video screen advertisements was 2,750 ms. The study showed a significant
difference between gaze duration while driving and while stationary: gaze duration was affected
by the task at hand. That is, drivers tended to gaze longer while the car was stopped and there
were few driving task demands.
The previously mentioned studies estimated the duration of glances to advertising and computed
mean values of less than 1,000 ms. Klauer et al., in his analysis of the 100-Car Naturalistic
Driving Study, concluded that glances away from the roadway for any purpose lasting more than
2,000 ms increase near-crash/crash risk by at least two times that of normal, baseline driving.^'"^^

Klauer et al. also indicated that short, brief glances away from the forward roadway for the
purpose of scanning the driving environment are safe and actually decrease near-crash/crash
risk/'"^^ Using devices in a vehicle that draw visual attention away from the forward roadway for
more than 2,000 ms (e.g., texting) is incompatible with safe driving. However, for external
stimuli, especially those near the roadway, the evaluation of eye glances with respect to safety is
less clear since peripheral vision would allow the driver to still have visual access to the forward
roadway.
Laboratory Studies
Laboratory investigations related to roadway safety can be classified into several categories:
driving simulations, non-driving-simulator laboratory testing, and focus groups. The review of
relevant laboratory studies by Molino et al. did not show conclusive evidence regarding the
distracting effects of CEVMS.*''^^ Moreover, the authors concluded that present driving simulators
do not have sufficient visual dynamic range, image resolution, and contrast ratio capability to
produce the compelling visual effect of a bright, photo-realistic LED-based CEVMS against a
natural background scene. The following is a discussion of a driving simulator study conducted
after the publication of Molino et al.^''^ The study focused on the effects of advertising on driver
visual behavior.
Chattington, Reed, Basacik, Flint, and Parkes conducted a driving simulator study in the United
Kingdom (UK) 
to evaluate the effects of static and video advertising on driver glance
behavior.^^^^ The researchers examined the effects of advertisement position relative to the road
(left, right, center on an overhead gantry, and in all three locations simultaneously), type of
advertisement (static or video), and exposure duration of the advertisement. (The paper does not
provide these durations in terms of time or distance. The exposure duration had to do with the
amount of time or distance that the sign would be visible to the driver.) For the advertisements
presented on the left side of the road (recall that drivers travel in the left lane in the UK), mean
glance durations for static and video advertisements were significantly longer (approximately
650 to 750 ms) when drivers experienced long advertisement exposure as opposed to medium
and short exposures. Drivers looked more at video advertisements (about 2 percent on average of
the total duration recorded) than at static advertisements (about 0.75 percent on average). In
addition, the location of the advertisements had an effect on glance behavior. When
advertisements were located in the center of the road or in all three positions simultaneously, the
glance durations were about 1,000 ms and were significantly longer than for signs placed on the
right or left side of the road. For advertisements placed on the left side of the road, there was a
significant difference in glance duration between static (about 400 ms) and video (about 800 ms).
Advertisement position also had an effect on the proportion of time that a driver spent looking at
an advertisement. The percentage of time looking at advertisements was greatest when signs
were placed in all three locations, followed by center location signs, then the left location signs,
and finally the right location signs. Drivers looked more at the video advertisements relative to
the static advertisements when they were placed in all three locations, placed on the left, and
placed on the right side of the road. The center placement did not show a significant difference in
percent of time spent looking between static and video.

Summary
The results from these key studies offer some insight into whether CEVMS pose a visual
distraction threat. However, these same studies also reveal some inconsistent findings and
potential methodological issues that are addressed in the current study. The studies conducted by
Smiley et al. showed drivers glanced forward at the roadway about 76 percent of the time in the
presence of video and dynamic signs where a few long glances of approximately 1,400 ms were
observed.^'^^ However, the video and dynamic signs used in these studies portray moving objects
that are not present in CEVMS as deployed in the United States. In another field study
employing eye tracking, Kettwich et al. found that gaze duration while driving for all types of
advertisements that they evaluated was less than 1,000 ms; however, when the vehicle was
stopped, mean gaze duration for advertising was as high as 2,750 ms.^^^^ Collectively, these
studies did not demonstrate that the advertising signs detracted from drivers' glances forward at
the roadway in a substantive manner while the vehicle was moving.
In contrast, the simulator study by Chattington et al. demonstrated that dynamic signs showing
moving video or other dynamic elements may draw attention away from the roadway.^'^^
Furthermore, the location of the advertising sign on the road is an important factor in drawing
drivers' visual attention. Advertisements with moving video placed in the center of the roadway
on an overhead gantry or in all three positions (right, left, and in the center) simultaneously are
very likely to draw glances from drivers.
Finally, in a study that examined CEVMS as deployed in the United States, Lee et al. did not
show any significant effects of CEVMS on driver glance behavior.^^^ However, the methodology
that was used likely did not employ sufficient sensitivity to determine at what specific object in
the environment a driver was looking.
None of these studies combined all necessary factors to address the current CEVMS situation in
the United States. Those studies that used eye tracking on real roads had animated and video-
based signs, which are not reflective of current off-premise CEVMS practice in the United
States.
STUDY APPROACH
Based on an extensive review of the literature, Molino et al. concluded that the most effective
method to use in an evaluation of the effects of CEVMS on driver visual behavior was the
instrumented field vehicle method that incorporated an eye tracking system.^"*^ The present study
employed such an instrumented field vehicle with an eye tracking system and examined the
degree to which CEVMS attract drivers' attention away from the forward roadway.
The following presents a brief overview and discussion of studies using eye tracking
methodology with complex visual stimuli, especially in natural environments (walking, driving,
etc.). The review by Molino et al. recommended the use of this type of technology and method;
however, a discussion laying out technical and theoretical issues underlying the use of eye
tracking methods was not presented.^'^^ This background is important for the interpretation of the
results of the studies conducted here.

Standard and digital billboards are often salient stimuli in the driving environment, which may
make them conspicuous. Cole and Hughes define attention conspicuity as the extent to which a
stimulus is sufficiently prominent in the driving environment to capture attention. Further, Cole
and Hughes state that attention conspicuity is a function of size, color, brightness, contrast
relative to surroundings, and dynamic components such as movement and change.^'^^ It is clear
that under certain circumstances image salience or conspicuity can provide a good explanation of
how humans orient their attention.
At any given moment a large number of stimuli reach our senses, but only a limited number of
them are selected for further processing. In general, attention can be focused on a stimulus
because it is important for achieving some goal, or because the properties of the stimulus can
attract the attention of the observer independent of their intentions (e.g., a car horn may elicit an
orienting response). When the focus of attention is goal directed, it is referred to as top-down.
When the focus of attention is principally a function of stimulus attributes, it is referred to as
bottom-up.^'
In general, billboards (either standard or CEVMS) 
are not relevant to the driving task but are
presumably designed to be salient stimuli in the environment where they may draw a driver's
attention. The question is to what degree CEVMS draw a driver's attention away from driving-
relevant stimuli (e.g., road ahead, mirrors, and speedometer) and is this different from a standard
billboard? In his review of the literature Wachtel leads one to consider CEVMS as stimuli in the
environment where attention to them would be drawn in a bottom-up manner; that is, the salience
of the billboards would make them stand out relative to other stimuli in the environment and
drivers would reflexively look at these signs.^'^^ Wachtel's conclusions were in reference to
research by Theeuwees who employed simple letter stimulus arrays in a laboratory task.^^*^^
Research using simple visual stimuli in a laboratory environment are very useful for testing
different theories of perception, but often lack direct application to tasks such as driving. The
following discusses research using complex visual stimuli and tasks that are more relevant to
natural vision as experienced in the driving task.
A recent review of stimulus salience and eye guidance by Tatler et al. shows that most of the
evidence for the capture of attention by the conspicuity of stimuli comes from research in which
the stimulus is a simple visual search array or in which the target is uniquely defined by simple
visual features.
In other words, these are laboratory studies that use letters, arrays of letters, or
simple geometric patterns as the stimuli. Pure salience-based models are capable of predicting
eye movement endpoint in simple displays, but are less successful for more complex scenes that
contain task-relevant and task-irrelevant salient areas.^^^'^^^
Research by Henderson et al. using photographs of actual scenes showed that subjects looked at
non-salient scene regions containing a search target and rarely looked at salient non-task-relevant
regions of the scenes.^^'*^ Salience of the stimulus alone was not a good predictor of where
participants looked. Additional research by Henderson using photographs of real world scenes
also showed that subjects fixated on regions of the pictures that provided task-relevant
information rather than visually salient regions with no task-relevant information. However,
Henderson acknowledges that static pictures have many shortcomings when used as surrogates
for real environments.^^^^
10

Land's review of eye movements in dynamic environments concluded that the eyes are proactive
and typically seek out information required in the second before each new activity
commences/^^^ Specific tasks (e.g., driving) have characteristic but flexible patterns of eye
movement that accompany them, and these patterns are similar between individuals. Land
concluded that the eyes rarely visit objects that are irrelevant to the task, and the conspicuity of
objects is less important than the objects' roles in the task. In a subsequent review of eye
movement and natural behavior, Land concluded that in a task that requires fixation on a
sequence of specific objects, the capture of gaze by irrelevant salient objects would, in general,
be an obtrusive nuisance.^^^^
The literature examining gaze control under natural behavior suggests that it is principally top-
down driven, or intentional.^^'^'^^'^^'^^'^''^^^ However, top-down processing does not explain all
gaze control or eye movements. For example, imagine driving down a two-lane country road and
a deer jumps into the road. It is most likely that you will attend and react to this deer. Unplanned
or unexpected stimuli capture our attention as we engage in complex natural tasks. Research by
Jovancevic-Misic and Hayhoe showed that human gaze patterns are sensitive to the probabilistic
nature of the environment.^^^^ In this study, participants' eye movement behavior was observed
while walking among other pedestrians. The other pedestrians were confederates and were either
safe, risky, or rogue pedestrians. When the study began, the risky pedestrian took a collision
course with the participant 50 percent of the time, and the rogue pedestrian always assumed a
collision course as he approached the participant, whereas the safe pedestrian never took a
collision course. Midway through the study the rogue and safe pedestrians exchanged roles but
the risky pedestrian role remained the same. The participants were not informed about the
behavior of the other pedestrians. Participants were asked to follow a circular path for several
laps and to avoid other pedestrians. The study showed that the participants modified their gaze
behavior in response to the change in the other pedestrians' behavior. Jovancevic-Misic
concluded that participants learned new priorities for gaze allocation within a few encounters and
looked both sooner and longer at potentially dangerous pedestrians.^^^^
Gaze behavior in natural environments is affected by expectations that are derived through long-
tenn learning. Using a virtual driving environment, Shinoda et al. asked participants to look for
stop signs while driving an urban route.^^^^ Approximately 45 percent of the fixations fell in the
general area of intersections during the simulated drive, and participants were more likely to
detect stop signs placed near intersections than those placed in the middle of a block. Over time,
drivers have leamed that stop signs are more likely to appear near intersections and, as a result,
drivers prioritize their allocation of gazes to these areas of the roadway.
The Tatler et al. review of the literature concludes that in natural vision, a consistent set of
principles underlies eye guidance. These principles include relevance or reward potential,
uncertainty about the state of the environment, and leamed models of the environment.^^
Salience of environmental stimuli alone typically does not explain most eye gaze behavior in
naturalistic environments.
In sum, most of the literature conceming eye gaze behavior in dynamic environments suggests
that task demands tend to override visual salience in determining attention allocation. When
extended to driving, it would be expected that visual attention will be directed toward task-
relevant areas and objects (e.g., the roadway, other vehicles, speed limit signs, etc.) and other
11

salient objects, such as billboards, will not necessarily capture attention. However, driving is a
somewhat automatic process and conditions generally do not require constant undivided
attention. As a result, salient stimuli, such as CEVMS, might capture driver attention and provide
an unwarranted increase in driver distraction. The present study addresses this concern.
Research Questions
The present research evaluated the effects of CEVMS on driver visual behavior under actual
roadway conditions in the daytime and at night. Roads containing CEVMS, standard billboards,
and areas not containing off-premise advertising were selected. The CEVMS and standard
billboards were measured with respect to luminance, location, size, and other relevant visual
characteristics. The present study examined CEVMS as deployed in two United States cities.
Unlike previous studies, the signs did not contain dynamic video or other dynamic elements. In
addition, the eye tracking system used in this study has approximately a 2-degree level of
resolution. This provided significantly more accuracy in determining what objects the drivers
were looking at than in previous on-road studies examining looking behavior (recall that Lee et
al. used video recordings of drivers' faces that, at best, examined gross eye movements).^^^
Two studies are reported. Each study was conducted in a different city. The two studies
employed the same methodology. The studies' primary research questions were:
1. Do CEVMS attract drivers' attention away from the forward roadway and other driving
relevant stimuli?
2. Do glances to CEVMS occur that would suggest a decrease in safety?
3. Do drivers look at CEVMS more than at standard billboards?
12

EXPERIMENTAL APPROACH
The study used a field research vehicle equipped with a non-intrusive eye tracking system. The
vehicle was a 2007 Jeep® Grand Cherokee Sport Utility Vehicle. The eye tracking system used
(SmartEye® vehicle-mounted infrared (IR) eye-movement measuring system) is shown in
figure 1.^^*^^ The system consists of two IR light sources and three face cameras mounted on the
dashboard of the vehicle. The cameras and light sources are small in size, and are not attached to
the driver in any manner. The face cameras are synchronized to the IR light sources and are used
to determine the head position and gaze direction of the driver.
1
Figure 1. Eye tracking system camera placement.
As a part of this eye tracking system, the vehicle was outfitted with a three-camera panoramic
scene monitoring system for capturing the forward driving scene. The scene cameras were
mounted on the roof of the vehicle directly above the driver's head position. The three cameras
together provided an 80-degree wide by 40-degree high field of forward view. The scene
cameras captured the forward view area available to the driver through the left side of the
windshield and a portion of the right side of the windshield. The area visible to the driver
through the rightmost area of the windshield was not captured by the scene cameras.
The vehicle was also outfitted with equipment to record GPS position, vehicle speed, and vehicle
acceleration. The equipment also recorded events entered by an experimenter and synchronized
those events with the eye tracking and vehicle data. The research vehicle is pictured in figure 2.
13

Figure 2. FHWA's field research vehicle.
EXPERIMENTAL DESIGN OVERVIEW
The approach entailed the use of the instrumented vehicle in which drivers navigated routes in
cities that presented CEVMS and standard billboards as well as areas without off-premise
advertising. The participants were instructed to drive the routes as they normally would. The
drivers were not informed that the study was about outdoor advertising, but rather that it was
about examining drivers' glance behavior as they followed route guidance directions.
Site Selection
More than 40 cities were evaluated in the selection of the test sites. Locations with CEVMS
displays were identified using a variety of resources that included State department of
transportation contacts, advertising company Web sites, and a popular geographic information
system. A matrix was developed that listed the number of CEVMS in each city. For each site, the
number of CEVMS along limited access and arterial roadways was determined.
One criterion for site selection was whether the location had practical routes that pass by a
number of CEVMS as well as standard off-premise billboards and could be driven in about
30 minutes. Other considerations included access to vehicle maintenance personnel/facilities,
proximity to research facilities, and ease of participant recruitment. Two cities were selected:
Reading, and Richmond.
Table 1 presents the 16 cities that were included on the final list of potential study sites.
14

Table 1. Distribution of CEVMS by roadway classification for various cities.
State
Area
Limited Access
Arterial
Other
Total
VA
Richmond
4
7
0
11
PA
Reading
7
11
0
18
VA
Roanoke
0
11
0
11
PA
Pittsburgh
0
0
15
15
TX
San Antonio
7
2
6
15
WI
Milwaukee
14
2
0
16
-
AZ
Phoenix
10
6
0
16
MN
St. Paul/Minneapolis
8
5
3
16
TN
Nashville
7
10
0
17
FL
Tampa-St. Petersburg
7
11
0
18
NM
Albuquerque
0
19
1
20
PA
Scranton-Wilkes Barre
7
14
1
22
OH
Columbus
1
22
0
23
GA
Atlanta
13
11
0
24
-
IL
Chicago
22
2
1
25
CA
Los Angeles
3
71
4
78
(1) Other includes roadways classified as both limited access and arterial or instances where the road
classification was unknown. Source: www.lamar.com and www.clearchannel.com
In both test cities, the following independent variables were evaluated:
• The type of advertising. This included CEVMS, standard billboards, and no off-premise
advertising. (It should be noted that in areas with no off-premise advertising, it was still
possible to encounter on-premise advertising; e.g., for gas stations, restaurants, and other
miscellaneous stores and shops.)
• Time of day. This included driving in the daytime and at night.
•
 The functional class of roadways in which off-premise advertising signs were
located. Roads were classified as either freeway or arterial. It was observed that the
different road classes were correlated with the presence of other visual information that
could affect the driver's glance behavior. For example, the visual environment on
arterials may be more complex or cluttered than on freeways because of the close
proximity of buildings, driveways, and on-premise advertising, etc.
15

READING
The first on-road study was conducted in Reading. This study examined the type of advertising
(CEVMS, standard billboard, or no off-premise advertising), time of day (day or night) and road
type (freeway or arterial) as independent variables. Eye tracking was used to assess where
participants gazed and for how long while driving. The luminance and contrast of the advertising
signs were measured to characterize the billboards in the current study.
METHOD
Selection of Data Collection Zone Limits
Data collection zones (DCZ) were defined on the routes that participants drove where detailed
analyses of the eye tracking data were planned. The DCZ were identified that contained a
CEVMS, a standard billboard, or no off-premise advertising.
The rationale for selecting the DCZ limits took into account the geometry of the roadway (e.g.,
road curvature or obstructions that blocked view of billboards) and the capabilities of the eye
tracking system (2 degrees of resolution). At a distance of 960 ft (292.61 m), the average
billboard in Reading was 12.8 ft (3.90 m) by 36.9 ft (11.25 m) 
and would subtend a horizontal
visual angle of 2.20 degrees and a vertical visual angle of 0.76 degrees, and thus glances to the
billboard would just be resolvable by an eye tracking system with 2 degrees of accuracy.
Therefore 960 ft was chosen as the maximum distance from billboards at which a DCZ would
begin. If the target billboard was not visible from 960 ft (292.61 m) 
due to roadway geometry or
other visual obstructions, such as trees or an overpass, the DCZ was shortened to a distance that
prevented these objects from interfering with the driver's vision of the billboard. In DCZs with
target off-premise billboards, the end of the DCZ was marked when the target billboard left the
view of the scene camera. If the area contained no off-premise advertising, the end of the DCZ
was defined by a physical landmark leaving the view of the eye tracking systems' scene camera.
Table 2 shows the data collection zone limits used in this study.
Advertising Conditions
The type of advertising present in DCZs was examined as an independent variable. DCZs fell
into one of the following categories, which are listed in the second column of table 2:
•
 CEVMS. These were DCZs that contained one target CEVMS. Two CEVMS DCZs were
located on freeways and two were located on arterials. Figure 3 and figure 4 show
examples of CEVMS DCZs with the CEVMS highlighted in the pictures.
•
 Standard billboard. These were DCZs that contained one target standard billboard. Two
standard billboard DCZs were located on freeways and two were located on arterials.
Figure 5 and figure 6 show examples of standard billboard DCZs; the standard billboards
are highlighted in the pictures.
16

No off-premise advertising conditions. These DCZs contained no off-premise
advertising. One of these DCZs was on a freeway (see figure 7) and the other was on an
arterial (see figure 8).
Table 2. Inventory of target billboards with relevant parameters.
DCZ
A dvertising
Type
Copy
Dimensions
(ft)
Side of
Road
Setback
from Road
(ft)
Other
Standard
Billboards
Approach
Length (ft)
Type of
Roadway
1
CONTROL
N/A
N/A
N/A
N/A
786
Freeway
6
CONTROL
N/A
N/A
N/A
N/A
308
Arterial
3
CEVMS
10'6"x22'9"
L
12
0
375
Arterial
5
CEVMS
14'0"x48'0"
L
133
1
853
Freeway
9
CEVMS
10'6"x22'9"
R
43
0
537
Arterial
10
CEVMS
14'0"x48'0"
R
133
1
991
Freeway
2
Standard
14'0"x48'0"
L
20
0
644
Arterial
7
Standard
14'0" X 48'0"
R
35
1
774
Freeway
8
Standard
10'6"x22'9"
R
40
1
833
Arterial
4
Standard
14'0"x48'0"
L
10
0
770
Freeway
*N/A indicates that there were no off-premise advertising in these areas and these values are undefined
Figure 3. DCZ with a target CEVMS on a freeway.
17

Figure 4. DCZ with a target CEVMS on an arterial.
Figure 5. DCZ with a target standard billboard on a freeway.
Figure 6. DCZ with a target standard billboard on an arterial.
18

. 
Vl.i '
Figure 7. DCZ for the control condition on a freeway.
Figure 8. DCZ for the control condition on an arterial.
Photometric Measurement of Signs
Two primary metrics were used to describe the photometric characteristics of a sample of the
CEVMS and standard billboards present at each location: luminance (cd/m") and contrast (Weber
contrast ratio).
Photometric Equipment
Luminance was measured with a Radiant Imaging ProMetric 1600 Charge-Coupled Device
(CCD) photometer with both a 50 mm and a 300 mm lenses. The CCD photometer provided a
method of capturing the luminance of an entire scene at one time.
The photometric sensors were mounted in a vehicle of similar size to the eye tracking research
vehicle. The photometer was located in the experimental vehicle as close to the driver's position
as possible and was connected to a laptop computer that stored data as the images were acquired.
Measurement Methodology
Images of the billboards were acquired using the photometer manufacturer's software. The
software provided the mean luminance of each billboard message. To prevent overexposure of
19

images in daylight, neutral density filters were manually affixed to the photometer lens and the
luminance values were scaled appropriately. Standard billboards were typically measured only
once; however, for CEVMS multiple measures were taken to account for changing content.
Photometric measurements were taken during day and night. Measurements were taken by
centering the billboard in the photometer's field of view with approximately the equivalent of the
width of the billboard on each side and the equivalent of the billboard height above and below
the sign. The areas outside of the billboards were included to enable contrast calculations.
Standard billboards were assessed at a mean distance of 284 ft (ranging from 570 ft to 43 ft). The
CEVMS were assessed at a mean distance of 479 ft (ranging from 972 ft to 220 ft). To include
the background regions of appropriate size, the close measurement distances required the use of
the 50 mm lens whereas measurements made from longer distances required the 300 mm lens. A
significant determinant of the measurement locations was the availability of accessible and safe
places from which to measure.
The Weber contrast ratio was used because it characterizes a billboard as having negative or
positive contrast when compared to its background area.^^^^ A negative contrast indicates the
background areas have a higher mean luminance than the target billboard. A positive contrast
indicates the target billboard has a higher mean luminance than the background. Overall, the
absolute value of a contrast ratio simply indicates a difference in luminance between an item and
its background. From a perceptual perspective luminance and contrast are directly related to the
perception of brightness. For example, two signs with equal luminance may be perceived
differently with respect to brightness because of differences in contrast.
Visual Complexity
Regan, Young, Lee and Gordon presented a taxonomic description of the various sources of
driver distraction.^^'^ Potential sources of distraction were discussed in terms of: things brought
into the vehicle; vehicle systems; vehicle occupants; moving objects or animals in the vehicle;
internalized activity; and external objects, events, or activities. The external objects may include
buildings, construction zones, billboards, road signs, vehicles, and so on. Focusing on the
potential for information outside the vehicle to attract (or distract) the driver's attention,
Horberry and Edquist developed a taxonomy for out-of-the-vehicle visual information. This
suggested taxonomy includes four groupings of visual information: built roadway, situational
entities, natural environment, and built environment.^^^^ These two taxonomies provide an
organizational structure for conducting research; however, they do not currently provide a
systematic or quantitative way of classifying the level of clutter or visual complexity present in a
visual scene.
The method proposed by Rozenholtz, Li, and Nakano provides quantitative and perhaps reliable
measures of visual clutter.^^"^^ Their approach measures the feature congestion in a visual image.
The implementation of the feature congestion measure involves four stages: (1) compute local
feature covariance at multiple scales and compute the volume of the local covariance ellipsoid,
(2) combine clutter across scale, (3) combine clutter across feature types, and (4) pool over space
to get a single measure of clutter for each input image. The implementation that was used
employed color, orientation and luminance contrast as features. Presumably, less cluttered
20

images can be visually coded more efficiently than cluttered images. For example, visual clutter
can cause decreased recognition performance and greater difficulty in performing visual
search.^^^^
Participants
In the present study participants were recruited at public libraries in the Reading area. A table
was set up so that recruiters could discuss the requirements of the experiment with candidates.
Individuals who expressed interest in participating were asked to complete a pre-screening form,
a record of informed consent, and a department of motor vehicles form consenting to release of
their driving record.
All participants were between 18 and 64 years of age and held a valid driver's license. The
driving record for each volunteer was evaluated to eliminate drivers with excessive violations.
The criteria for excluding drivers were as follows: (a) more than one violation in the preceding
year; (b) more than three recorded violations; and (c) any driving while intoxicated violation.
Forty-three individuals were recruited to participate. Of these, five did not complete the drive
because the eye tracker could not be calibrated to track their eye movements accurately. Data
from an additional seven participants were excluded as the result of equipment failures (e.g.,
loose camera). In the end, usable data was collected from 31 participants (12 males, M = 46
years; 19 females, M = 
'17 years). Fourteen participants drove at night and 17 drove during the
day.
Procedures
Data were collected from two participants per day (beginning at approximately 12:45 p.m. and
7:00 p.m.). Data collection began on September 18, 2009, and was completed on October 26,
2009.
Pre-Data Collection Activities
Participants were greeted by two researchers and asked to complete a fitness to drive
questionnaire. This questionnaire focused on drivers' self-reports of alertness and use of
substances that might impair driving (e.g., alcohol). All volunteers appeared fit.
Next, the participant and both researchers moved to the eye tracking calibration location and the
test vehicle. The calibration procedure took approximately 20 minutes. Calibration of the eye
tracking system entailed development of a profile for each participant. This was accomplished by
taking multiple photographs of the participant's face as they slowly rotate their head from side to
side. The saved photographs include points on the face for subsequent real-time head and eye
tracking. Marked coordinates on the face photographs were edited by the experimenter as needed
to improve the real-time face tracking. The procedure also included gaze calibration in which
participants gazed at nine points on a wall. These points had been carefully plotted on the wall
and correspond to the points in the eye tracking system's world model. Gaze calibration relates
the individual participant's gaze vectors to known points in the real world. The eye tracking
system uses two pulsating infrared sources mounted on the dashboard to create two comeal glints
that are used to calculate gaze direction vectors. The glints were captured at 60 Hz. A second set
21

of cameras (scene cameras), fixed on top of the car close to tlie driver's viewpoint, were used to
produce a video scene of the area ahead. The scene cameras recorded at 25 Hz. A parallax
correction algorithm compensated for the distance between the driver's viewpoint and the scene
cameras so that later processing could use the gaze vectors to show where in the forward scene
the driver was gazing.
If it was not possible to calibrate the eye tracking system to a participant, the participant was
dismissed and paid for their time. Causes of calibration failure included reflections from eye
glasses, participant height (which put their eyes outside the range of the system), and eyelids that
obscure a portion of the pupil.
Practice
After eye-tracker calibration, a short practice drive was made. Participants were shown a map of
the route and written tum-by-tum directions prior to beginning the practice drive. Throughout the
drive, verbal directions were provided by a GPS device.
During the practice drive, a researcher in the rear seat of the vehicle monitored the accuracy of
eye tracking. If the system was tracking poorly, additional calibration was performed. If the
calibration could not be improved, the participant was paid for their time and dismissed.
Data Collection
Participants drove two test routes (referred to as route A and B). Each route required 25 to 30
minutes to complete and included both freeway and arterial segments. Route A was 13 miles
long and contained 6 DCZs. Route B was 16 miles long and contained 4 DCZs. Combined,
participants drove in a total of 10 DCZs. Similar to the practice drive, participants were shown a
map of the route and written tum-by-tum directions. A GPS device provided tum-by-tum
guidance during the drive. Roughly one half of the participants drove route A first and the
remaining participants began with route B. A 5 minute break followed the completion of the first
route.
During the drives, a researcher in the front passenger seat assisted the driver when additional
route guidance was required. The researcher was also tasked with recording near misses and
driver errors if these occurred. The researcher in the rear seat monitored the performance of the
eye tracker. If the eye tracker performance became unacceptable (i.e., loss of calibration), then
the researcher in the rear asked the participant to park in a safe location so that the eye tracker
could be recalibrated. This recalibration typically took a minute or two to accomplish.
Debriefing
After driving both routes, the participants provided comments regarding their drives. The
comments were in reference to the use of a navigation system. No questions were asked about
billboards. The participants were given $120.00 in cash for their participation.
22

DATA REDUCTION
Eye Tracking Measures
The Multiple-Analysis of Psychophysical and Performance Signals (MAPPS'^'^ software was
used to reduce the eye tracking data.^^^^ The software integrates the video output from the scene
cameras with the output from the eye tracking software (e.g., gaze vectors). The analysis
software provides an interface in which the gaze vectors determined by the eye tracker can be
related to areas or objects in the scene camera view of the world. Analysts can indicate regions of
interest (ROIs) in the scene camera views and the analysis software then assigns gaze vectors to
the ROIs.
Figure 9 shows a screen capture from the analysis software in which static ROIs have been
identified. These static ROIs slice up the scene camera views into six areas. The software also
allows for the construction of dynamic ROIs. These are ROIs that move in the video because of
own-vehicle movement (e.g., a sign changes position on the display as it is approached by the
driver) or because the object moves over time independent of own-vehicle movement (e.g.,
pedestrian walking along the road, vehicle entering or exiting the road).
Static ROIs need only be entered once for the scenario being analyzed whereas dynamic ROIs
need to be entered several times for a given DCZ depending on how the object moves along the
video scene; however, not every frame needs to be coded with a dynamic ROI since the software
interpolates across frames using the 60-Hz data to compute eye movement statistics.
Figure 9. Screen capture showing static ROIs on a scene video output.
The following ROIs were defined with the analysis software:
Static ROIs
These ROIs were entered once into the software for each participant. The static ROIs for the
windshield were divided into top and bottom to have more resolution during the coding process.
The subsequent analyses in the report combines the top and bottom portion of these ROIs since it
appeared that this additional level of resolution was not needed in order to address research
questions:
• Road ahead: bottom portion (approximately 2/3) of the area of the forward roadway
(center camera).
23