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Driver performance and attention allocation in use of logo signs on freeway exit ramps
Applied Ergonomics 65 (2017) 70e80
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Applied Ergonomics journal homepage: www.elsevier.com/locate/apergo
Driver performance and attention allocation in use of logo signs on freeway exit ramps Maryam Zahabi a, Patricia Machado b, Mei Ying Lau a, Yulin Deng a, Carl Pankok Jr. a, Joseph Hummer c, William Rasdorf b, David B. Kaber a, * a b c
Edward P. Fitts Department of Industrial & Systems Engineering, North Carolina State University, United States Department of Civil, Construction, and Environmental Engineering, North Carolina State University, United States Staff Engineer, Mobility and Safety Division, North Carolina Department of Transportation, United States
a r t i c l e i n f o
a b s t r a c t
Article history: Received 14 August 2016 Received in revised form 29 January 2017 Accepted 1 June 2017
The objective of this research was to quantify the effects of driver age, ramp signage configuration, including number of panels, logo format and sign familiarity, on driver performance and attention allocation when exiting freeways. Sixty drivers participated in a simulator study and analysis of variance models were used to assess response effects of the controlled manipulations. Results revealed elderly drivers to demonstrate worse performance and conservative control strategies as compared to middleaged and young drivers. Elderly drivers also exhibited lower off-road fixation frequency and shorter offroad glance durations compared to middle-aged and young drivers. In general, drivers adopted a more conservative strategy when exposed to nine-panel signs as compared to six-panel signs and were more accurate in target detection when searching six-panels vs. nine and with familiar vs. unfamiliar logos. These findings provide an applicable guide for agency design of freeway ramp signage accounting for driver demographics. © 2017 Elsevier Ltd. All rights reserved.
Keywords: Roadway logo signs Driving simulation Exit ramp Driver distraction Highway safety
1. Introduction 1.1. Sign usage on freeways Several research studies have examined the effects of roadway sign characteristics and familiarity on driver behavior and attention allocation in freeway driving. These studies can be categorized as: (1) observational, (2) presentation-based experiments, and (3) controlled driving simulations. Observational studies indicate little difference in driving performance among specific service signs with different numbers of business panels (e.g., six vs. nine logos). Carter and Wang (2007) video-recorded vehicle activity on various stretches of North Carolina freeways and identified instances of unusual driver behavior (e.g., braking, drifting, and line encroachment). They found the number of unusual behaviors at nine-panel logo sign locations to be comparable to occurrences at six-panel sign locations. However, the lack of control in observational studies leaves some doubt as to the true effects of increased logo
* Corresponding author. Dept. ISE, North Carolina State University, 400 Daniels Hall, Raleigh, NC 27695-7906, United States. E-mail address: [email protected] (D.B. Kaber). http://dx.doi.org/10.1016/j.apergo.2017.06.001 0003-6870/© 2017 Elsevier Ltd. All rights reserved.
panels on driver behavior. Presentation-based experiments, which present sign images to participants (absent of any driving task or simulation requirements), have focused on the effect of variations in signage type on target business panel detection accuracy. Hummer and Maripalli (2008) conducted a slide-based experiment presenting realistic logo signs to subjects and asking them to identify whether a specific brand was included. Results showed overall accuracy for six-panel signs to be significantly greater than mixed-use and ninepanel signs. In another study, Dagnall et al. (2013) found that response times increased as the number of panels on a logo sign increased from four to six to nine. It was found that participants were generally able to recall 3e4 businesses regardless of the number of panels on the sign. They also reported that within the number of panels presented to viewers, response time was shorter for text logos than for pictorial logos, although the mean difference was only 0.1 s. In another presentation-based study on the effect of signage format, it was reported that pictorial signs enhanced viewer ability to recall signs and resulted in higher target search accuracy than text-only signs (Liu, 2005). In another presentationbased study of the effect of logo sign familiarity, Hawkins and Rose (2005) found search accuracy to increase when a target business
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logo was familiar (as rated by an experimenter) compared to when it was unfamiliar. Returning to the Hummer and Maripalli (2008) study, they reported participants were much more accurate in searching for unfamiliar vs. familiar logos. The authors postulated that increased search performance could have been due to participant scanning for a novel item on a board populated with mostly familiar images. While these presentation-based experiments integrated a great degree of experimental control, there is an absence of resource competition between sign search and driving tasks in such studies and, therefore, results may not be generalizable to real driving performance. Finally, in a controlled driving simulation experiment, Zhang et al. (2013) compared driving performance and visual attention allocation to six-panel, overflow combination, and nine-panel logo signs. They found no significant differences in signal detection, response time, maximum off-road glance duration, lane deviation, or reaction time to a hazard among the three sign types. In another study, Kaber et al. (2015) found no significant differences in signal detection, off-road fixation frequency, longest off-road glance duration, lane maintenance deviations, or speed control when exposed to six-panel logo signs, nine-panel signs, or green mileage guide signs. Related to the format of signs, Hummer (1989) reported that driving performance (measured as speed deviation, lane maintenance, and acceleration control) significantly decreased for text panels compared to pictorial logos. Driving simulation experiments are most similar to real-world driving, as participants are required to allocate resources to the driving task in addition to sign search tasks, etc. 1.2. Sign usage on freeway exit ramps A study of interchange collisions in Northern Virginia found that a majority of crashes occurred when drivers were exiting the freeway (McCart et al., 2004). They attributed this finding to the difficult task of slowing a vehicle from high-speed freeway travel while negotiating a (sometimes sharp) turn and navigating to a destination, simultaneously. In an assessment of roundabout signage design, where drivers are posed with similar vehicle control and perceptual requirements as at a freeway interchange (i.e., braking, negotiating a turn, and navigating), it was found that lane selection accuracy decreased significantly as the number of items on signs increased (Inman et al., 2006). Interchange logo signs typically include distances and directions (i.e., a left or right arrow) beneath logos in order to provide further information about business locations. Related to this, Cottrell and Edara (2011) found that adding distance information to freeway logo signs decreased mean legibility distance but did not affect the number of crashes in a “before-after” analysis. However, these signs were not located on interchanges; they were located roadside on freeways. Unfortunately, there is no existing research on sign usage at interchanges, which is surprising given previous reports that 18% of all interstate crashes occur at interchanges, despite the fact that interchanges constitute less than 5% of total freeway mileage (Firestine et al., 1989). 1.3. Driver age The relationship between driver age and crash rates has been shown in several studies. National Highway Traffic Safety Administration (NHTSA) reported that automobile crashes increase around age 65 and the fatality rate per million miles of travel for drivers over 65 years of age was found to be 17 times that of the 25e65 age group (NHTSA, 1997). In a more recent study, Tefft (2012) found that crash rates were highest for drivers between the age of 16e17, decreased until ages 60e69 but increased after
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age of 70. Regarding the effect of age on driver performance and attention allocation, Dingus et al. (1989) found that participants older than 50 years took significantly longer to complete tasks, had longer glance times to in-car instruments, and committed significantly more errors than participants younger than 50 years. Much more recently, Edquist et al. (2011) found that drivers 65 years and older were the slowest to change lanes in a lane changing task, followed by firstyear drivers under 25 years of age. Related to this work, Hummer (1989) found that drivers over 50 years of age exhibited poorer speed maintenance and poorer acceleration control in logo sign use as compared with drivers younger than 50 years of age. Finally, and most recently, Dagnall et al. (2013) reported that participants older than 50 years of age took longer to identify a sign in a search task than younger drivers, regardless of the number of panels on the logo sign. Although crash reports have shown higher numbers of crashes for elderly drivers, the majority of prior driving simulations experiments have been limited to young and middle-age drivers (e.g. 18e58 yrs, Zhang et al., 2013; 25e59 yrs, Kaber et al., 2015). On this basis, there remains a need to investigate a broader driver sample, as the results of prior studies on the role of signage in driver behavior may not be generalizable to the elderly driving population. 1.4. Problem statement Although simulator studies suggest that specific service signs with nine logo panels have little effect on driving performance and attention allocation as compared to six-panel signs (e.g., Zhang et al., 2013; Kaber et al., 2015), these studies have mainly focused on signage on freeways. Furthermore, young and middle-age driver groups have been the focus of study. There are inconsistent results regarding the effect of logo format and driver familiarity with business logos on driving behavior, likely due to a wide range of experimental conditions (e.g., Hummer and Maripalli, 2008; Dagnall et al., 2013). On this basis, and given the high number of crashes at interchanges, the objectives of this study were to answer the following research questions regarding freeway exit ramp driving behavior: - How does service sign logo count (six-panel vs. nine panel) affect driver target detection accuracy, attention allocation and vehicle control? - How does logo sign format (text vs. pictorial) affect driver target detection accuracy, attention allocation and vehicle control? - How does logo target familiarity affect driver target detection accuracy, attention allocation and vehicle control? - How does driver age affect target detection accuracy, attention allocation and vehicle control when exposed to logo signs?
1.5. Hypotheses Related to the above research questions, and based on the literature review, we formulated a series of research hypotheses for investigation in a driving simulation study. The hypotheses are detailed in Table 1. 2. Methodology 2.1. Participants Sixty participants were uniformly recruited to each of three age groups of (1) 18e22 yrs, (2) 23e64 yrs, and (3) 65 þ yrs. Gender
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Table 1 Experiment hypotheses (Hypothesis Number in Parenthesis). Detection Accuracy Number of Panels (6 vs. 9)
Accuracy of business target detection will be consistent among six-panel and ninepanel service signs (H1) Logo Format Accuracy of business target detection will (Text vs. Pictorial) be higher for pictorial targets than for text targets (H2) Accuracy of target detection will be higher Target Familiarity for search of familiar targets compared to (Familiar vs. unfamiliar targets (H3) Unfamiliar) Age Group Accuracy of business target detection will (Young, Middle, Elderly) be higher for young driver group followed by middle and elderly age groups (H4)
Attention Allocation
Vehicle Control
Visual attention to six-panel logo signs will be comparable to attention to nine-panel signs (H5) Attention to text-based logo signs will be longer than attention to pictorial-based logo signs (H6)
Vehicle control will be comparable in presence of six-panel and nine-panel logo signs (H9)
Visual attention to logo signs will increase in search of unfamiliar target compared to search for familiar target (H7) Visual attention to logo signs will increase for young driver group followed by middle and elderly age groups (H8)
Vehicle control will be degraded for unfamiliar targets compared to familiar targets (H11)
was balanced (i.e., 10 females and 10 males) within each age group. The age categories were defined using regression analysis on crash rate data (Tefft, 2012), which indicated young (less than 23 yrs) and elderly drivers (greater than 64 yrs) had significantly higher crash rates in comparison to middle-age drivers (between 23 and 64 yrs). These categories are also in-line with the findings of Chen et al. (2007) that drivers between the age of 25 and 60 years of age have lower crash rates in comparison to those who are younger than 25 years or older than 60 years. All participants were required to have a valid North Carolina driver's license and have 20/20 vision (or corrected vision). 2.2. Apparatus 2.2.1. Driving simulator A STISIM Drive M400 driving simulator (System Technology, Inc., Hawthorne, CA) was used in this experiment (see Fig. 1). The simulator setup included three 38-inch high resolution television monitors providing a 135-degree field of view for drivers. A set of full-size driving controls, including accelerator, brake pedal, steering wheel, and turn signals were used to provide drivers with realtime feedback (based on a Ford Taurus vehicle dynamics model). Similar setups have been validated by Santos et al. (2005) and Wang et al. (2010) as providing realistic simulations of real-world driving as well as research findings corresponding with results of on-road observational studies. 2.2.2. Eye tracking system A FaceLAB 5.1 (Seeing Machines, Australia) eye tracking system was used to collect real-time data on driver gaze positions. The system hardware included two cameras and an infrared light
Fig. 1. Driving simulator setup.
Vehicle control will be degraded for text targets compared to pictorial targets (H10)
Vehicle control will be degraded for elderly driver group followed by young and middle age groups (H12)
emitter (see Fig. 2). The cameras detect reflection of the infrared light on the surface of a driver's eyeballs. This reflection is used along with an outline of the iris or pupil to extrapolate direction of driver gaze in the simulation environment. Eye movements were recorded at a frequency of 60 Hz with an accuracy of 0.5 to 1 of rotational error.
2.3. Simulated driving environment The simulated driving environment presented a four-lane, rural Interstate freeway, including two standard diamond interchanges spaced 3 miles apart, each with a single exit Fig. 3. The simulation was designed to represent as accurately as possible a realistic driving environment, following regulations published by the North Carolina Department of Transportation (NCDOT) and the Manual on Uniform Traffic Control Devices (MUTCD; Federal Highway Administration, 2009). Each interchange included 500-ft deceleration and acceleration lanes. There were complete sets of exit gore, guide and service signs before and after interchanges, as required by the MUTCD. Signs preceding each interchange included specific service signs (lodging, food, and gas/attraction categories, as described in Section 2J.03 of the MUTCD) and standard guide signs. Fig. 4 shows the specific sign configurations used in the simulation. Fig. 5 shows an example of service sign configuration presented at the interstate exit ramps, including distance (miles to the specific business) and directional indicators (turn at the end of the ramp). In addition, Figs. 6 and 7 show the layout of signage located before each interchange and on the exit ramp.
Fig. 2. Eye tracking system hardware placed in front of simulator screens.
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Fig. 3. Overall layout of driving scenarios. Plate (a): Six-panel lodging and food signs and six-panel “overflow combination” sign. Plate (b): Nine-panel lodging and food signs and nine-panel “overflow combination” sign.
Fig. 4. Specific service sign configurations on freeway. Plate (a): Exit ramp six-panel lodging and food signs and six-panel “overflow combination” sign. Plate (a): Exit ramp ninepanel lodging and food signs and nine-panel “overflow combination” sign.
2.4. Independent variables Driver age (young, middle-aged, and elderly) served as a grouping variable in this study (with 20 participants in each group). Independent variables related to roadway sign configuration included the number of business panels on specific service signs, familiarity of logo targets for driver search, and logo format. Regarding the number of panels, the experiment compared sixwith nine-panel signs (see Fig. 8 for examples). Logo target familiarity was manipulated by presenting participants with either a nationally-known lodging chain or an unfamiliar independent hotel for search. Each unfamiliar lodging was presented only once across all experiment trials in order to eliminate any participant learning effects. All lodging signs contained 67% familiar logos and 33% unfamiliar logos, based on Hummer and Maripalli's (2008) observation of one unfamiliar target among familiar targets
producing high search accuracy. Finally, logo format was manipulated by asking participants to search for either a text or pictorial attraction sign. All attraction logos used in the experiment were unfamiliar to prevent any confound between logo format and familiarity. 2.5. Experiment design The experiment followed a 3x2x2x2 mixed within- and between-subject design with a full crossing of number of sign panels, lodging target familiarity, and attraction panel format. The order of trials was randomized to prevent potential order effects. Table 2 presents the trial conditions as well as target logo characteristics, including distance and direction, which participants were asked to identify. It is important to note that lodging and attraction targets were tested in separate trials.
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Fig. 5. Specific service sign configurations on freeway exit ramps.
Fig. 6. Layout of guide and specific service signs located before interchange.
2.6. Dependent variables Dependent variables were grouped into three categories, including sign detection accuracy, visual attention allocation, and driving performance. Attention allocation and driving performance measures for lodging signs (the first sign on the ramp) were calculated during time “windows” when the sign became readable for drivers (650 ft ahead of the sign) and ending when the vehicle passed the sign. However, since the distance between two consecutive food and attraction signs on a ramp was limited to 300 ft, attention allocation and driving performance measures for attraction signs were calculated based on time windows beginning 300 ft ahead of the sign (to ensure a driver was looking at the sign of interest) and ending when the vehicle passed the sign.
2.6.1. Accuracy Once drivers exited the freeway at the interchange, they were asked to verbally indicate the distance and direction of a target business (lodging or attraction). Accuracy was defined as the percentage of “correct” driver responses to all target sign exposures. A response was considered “correct” when a participant accurately identified the distance and/or direction to a business target. 2.6.2. Attention allocation The area of the center screen of the driving simulator, in which logo signs appeared during simulation trials, was identified as the area of visual interest (AOI) for visual behavior analysis. Two eye movement measures were derived from the FaceLab system output, including fixation frequency and longest glance duration to the AOI.
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Fig. 7. Layout of exit gore and specific service signs on ramp with other traffic controls.
Fig. 8. Examples of six-panel and nine-panel lodging logo signs on the ramp.
Table 2 Experiment design and targets. Number of Panels
Lodging Target
6
Familiarity
Attraction Target Name
Distance
Direction
Format
Distance
Direction
Familiar
0.4
Right
Text
Name
0.5
Right
6
Unfamiliar
0.2
Left
Pictorial
0.9
Left
9
Familiar
0.3
Left
Text
0.9
Left
9
Unfamiliar
0.6
Right
Pictorial
0.5
Left
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Fixation frequency (a percentage) was determined as the number of fixations to the AOI divided by total number of fixations during the target detection phase. A fixation was defined as any participant gaze with a velocity less than 100 /s for a minimum of 100 ms (Holmqvist et al., 2011). A glance was defined as the total time the focus of attention remained within the AOI, encompassing both fixations and saccades (rapid eye movements). 2.6.3. Driving performance Driver performance measures included speed deviation, lane deviation, and maximum deceleration. Speed deviation was calculated as the average absolute deviation of actual vehicle speed from the posted ramp speed limit (40 mph). Lane deviation was measured as the average absolute deviation of the center of the vehicle from the center of the lane. Maximum deceleration was calculated as the maximum of all deceleration values during the window of target sign observation. 2.7. Procedure Participants were asked to complete an informed consent form, a demographic questionnaire and a baseline Simulator Sickness Questionnaire (SSQ; Kennedy et al., 1993). They were then escorted to the driving simulator and provided with a brief introduction to the equipment, followed by two training trials (one for assessing lane and speed deviations on the freeway and another for assessing maximum deceleration on the ramp). The training trials were identical to experiment trials, save the inclusion of specific service signs. At the end of the training trials, participant performance was assessed to verify that they met established criteria, including average jlane deviationj 1:37 ft, averagejspeed deviationj 1 mph, and jmaximum decelerationj 31:7 ft=s2 . The lane deviation criterion was based on Horrey and Wickens (2004) research. The speed deviation criterion was established based on average speed deviation for five experienced simulator drivers. The maximum deceleration criterion was based on the 85th percentile of recommended deceleration to a complete stop on a freeway off-ramp under dry roadway and clear weather conditions (Campbell et al., 2012). Participants were administered another SSQ after training to ensure no motion sickness symptoms. Participants were provided with a short slide presentation on the signs to appear in test trials. The presentation covered speed limit, distance, specific service, and advance guide signs. This step was followed by calibration of the FaceLab eye tracking system. Participants then completed all eight experiment trials. Before each trial, they were shown the target business logo (lodging or attraction) for search. When drivers passed a lodging or attraction sign containing the target, they were expected to callout the distance and direction. Participants were given a 5-min rest period between trials, and were administered a SSQ every two trials to ensure no motion sickness. They were given additional breaks if symptoms were observed. Participants were compensated $20/hour. The experiment took about 3 h. 2.8. Data analyses Before conducting any inferential statistical tests, a data screening process was conducted to identify outliers due to participants not following instructions and/or technical difficulties with the simulator or eye tracking apparatus. Diagnostics were conducted on all dependent variables to ensure conformance of response data with parametric test assumptions of homoscedasticity and residual normality. Variance homoscedasticity was checked using Bartlett's tests and residual normality was assessed by inspection of normal probability plots and the Shapiro-Wilk
normality test. In case of parametric assumption violations, log, square root, and exponential transformations to the power of lambda (identified by the Box-Cox method) were applied to responses. Contingency table analyses were used to assess the effects of the sign configuration variables on sign detection accuracy with ChiSquare statistics generated based on expected likelihood ratios. Analysis of Variance (ANOVA) models were also developed for driver performance and visual behavior effects tests on the experimental manipulations, with separate analyses for lodging and attraction targets. Age group was included in the ANOVA models as a between-subject variable. Due to the fact that attraction business panels were always presented in groups of three (regardless of the total number of panels on a specific service sign), attraction target analyses only assessed the effect of driver age group, logo format and the interaction on responses; panel count was not a predictor variable. Lodging target analyses assessed the effects of driver age group, number of sign logos, and logo familiarity, as well as their interactions, on the dependent variables. Trial number was included in the statistical models as a co-variate and was removed in the absence of a significant effect on a response. Where appropriate, Tukey's Honest Significant Difference (HSD) post-hoc procedure was applied to identify differences among levels of any significant effects and results are presented in summary tables (below). A significance level of p 0.05 was set as a criterion for the study. 3. Results 3.1. Accuracy Tables 3 and 4 present summaries of the experiment results for target identification accuracy for lodging and attraction signs, respectively. Note that accuracy was ultimately analyzed for both direction and distance identification. With respect to the effect of sign familiarity within age group for lodging target distance identification accuracy, chi-square tests revealed no significant difference among groups for familiar logos (c2 (2) ¼ 2.253, p ¼ 0.1966) but an effect was present for unfamiliar logos (c2 (2) ¼ 7.220, p ¼ 0.0271). Post-hoc tests revealed accuracy to be significantly lower for the elderly group (38%) compared to young (64%) and middle-aged (65%) drivers for unfamiliar logos. Regarding the effect of panel count within age group for lodging target distance identification accuracy, chi-square tests revealed no significant difference among groups for nine-panel signs (c2 (2) ¼ 2.883, p ¼ 0.2366) but there was an effect for six-panel signs (c2 (2) ¼ 12.738, p ¼ 0.0017). Post-hoc tests revealed accuracy to be significantly lower for elderly drivers (69%) compared to young (95%) and middle-aged (95%) groups for six-panel signs. (We provide some explanation of this finding in the Discussion section.) Contingency table analysis also revealed a significant interaction between logo familiarity and the number of panels on distance identification accuracy for lodging signs (c2 (3) ¼ 66.145, p < 0.0001). Table 5 shows that accuracy was lower for unfamiliar targets in nine-panel signs compared to all other panel count and familiarity conditions. On the effect of target familiarity within age group for lodging target direction identification accuracy, Chi-Square tests revealed no significant differences among groups for familiar logos (c2 (2) ¼ 1.906, p ¼ 0.3856) but there was an effect for unfamiliar logos (c2 (2) ¼ 6.839, p ¼ 0.0327). Post-hoc tests revealed accuracy to be significantly lower for elderly drivers (56%) compared to middleaged (83%) drivers for unfamiliar logos. Regarding the effect of panel count within age group for lodging target direction identification accuracy, chi-square tests revealed no significant differences
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Table 3 Summary of accuracy results for lodging targets.
Number of Panels
Target Familiarity
Age Group (Elderly (E), Middle (M), Young (Y))
Direction Identification Accuracy
Distance Identification Accuracy
c2(1) ¼ 16.858
P < 0.0001* (6-Panel>9-Panel) c2(1) ¼ 8.406 P ¼ 0.0037* (Familiar > Unfamiliar) c2(2) ¼ 6.859 P ¼ 0.0324* (E < M)
c2(1) ¼ 32.286 P < 0.0001* (6-Panel>9-Panel) c2(1) ¼ 16.675 P < 0.0001* (Familiar > Unfamiliar) c2(2) ¼ 9.995 P < 0.0084* (E < Y,M)
Direction Identification Accuracy
Distance Identification Accuracy
c2(1) ¼ 0.094
c2(1) ¼ 0.320 P ¼ 0.5719 c2(2) ¼ 45.728 P < 0.0001* (E < Y,M)
Note: * indicates significant p-value.
Table 4 Summary of accuracy results for attraction targets.
Logo Format
P ¼ 0.7586 c2(2) ¼ 38.786 P < 0.0001* (E < Y,M)
Age Group (Elderly (E), Middle (M), Young (Y)) Note: * indicates significant p-value.
Table 5 Interaction effect of number of panels and familiarity on lodging sign distance identification accuracy.
Table 6 Interaction effect of number of panels and familiarity on lodging sign direction identification accuracy.
Number of Panels
Familiarity
Accuracy
Significance Grouping
Number of Panels
Familiarity
Accuracy
Significance Grouping
6 6 9 9
Familiar Unfamiliar Familiar Unfamiliar
85% 86% 76% 27%
A A A B
6 6 9 9
Familiar Unfamiliar Familiar Unfamiliar
87% 93% 86% 50%
A A A B
among groups for nine-panel signs (c2 (2) ¼ 2.883, p ¼ 0.2366) but there was a significant effect for six-panel signs (c2 (2) ¼ 10.773, p ¼ 0.0046). Post-hoc tests showed accuracy to be significantly lower for elderly drivers (77%) compared to young (95%) and middle-aged (98%) drivers for six-panel signs. Contingency table analyses also revealed a significant interaction between logo familiarity and the number of panels on direction identification in lodging signs (c2 (3) ¼ 41.323, p < 0.0001). Table 6 shows accuracy was lower when drivers searched for unfamiliar targets in nine-panel signs as compared to all other signage conditions. 3.2. Attention allocation Tables 7 and 8 present summaries of the experiment results on longest glance duration and fixation frequency responses for lodging and attraction targets, respectively. 3.3. Driving performance Tables 9 and 10 present summaries of experiment results on speed deviation, lane deviation and maximum deceleration in lodging and attraction sign use, respectively. Regarding attraction target analyses, there was a significant interaction between age group and service sign logo format (F (2,170) ¼ 3.87, p ¼ 0.0227, 1-b ¼ 0.69) on the speed deviation response. Tukey's HSD post-hoc test revealed that elderly and young drivers produced greater speed deviations when they were exposed to pictorial logos vs. text panels. However, for middle-aged drivers, greater speed deviations occurred with exposure to text-
based signs (see Fig. 9). There was also a significant interaction between age group and panel count (F (2,148) ¼ 3.54, p ¼ 0.0314, 1-b ¼ 0.65) on the lane deviation response for lodging signs. Tukey's HSD tests revealed middle-aged drivers to produce greater lane deviations when exposed to nine-panel signs compared to six (see Fig. 10). There was no significant difference in lane deviations among elderly and young drivers for six- and nine-panel sign exposures. 4. Discussion It is important to note that the inferences on results presented in this section are limited in potential applicability to rural freeway exit ramp driving under daytime roadway conditions with moderate traffic density. 4.1. Accuracy Hypothesis 1 posited that accuracy would be consistent among six- and nine-panel signs. This hypothesis was not supported by the data. Drivers were more accurate in identifying the direction and distance to target businesses when they were exposed to six-panel signs in comparison to nine. Prior studies found that target detection accuracy was consistent among six-panel and nine-panel signs (Kaber et al., 2015). However, Kaber et al. only required participants to search for a familiar target. In this study, drivers searched for familiar and unfamiliar targets in different trials. Contingency table analyses showed that both sign panel count and familiarity of lodging targets impacted driver detection accuracy with significantly lower accuracy in identifying unfamiliar targets in nine-
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M. Zahabi et al. / Applied Ergonomics 65 (2017) 70e80 Table 7 Summary of results on attention allocation responses for lodging targets.
Number of Panels Target Familiarity Age Group (Elderly (E), Middle (M), Young (Y))
Longest Glance Duration
Fixation Frequency
F(1,140) ¼ 0.01 P ¼ 0.9116 F(1,140) ¼ 0.10 P ¼ 0.7499 F(2,140) ¼ 35.50 P < 0.001* (E < M < Y)
F(1,147) ¼ 0.61 P ¼ 0.4376 P(1,147) ¼ 1.42 P ¼ 0.2364 F(2,147) ¼ 15.96 P < 0.0001* (E < Y,M)
Note: * indicates significant p-value.
Table 8 Summary of results on attention allocation responses for attraction targets.
Logo Format Age Group (Elderly (E), Middle (M), Young (Y))
Longest Glance Duration
Fixation Frequency
F(2,143) ¼ 0.60 P ¼ 0.44 F(2,143) ¼ 107.00 P < 0.0001* (E < M < Y)
F(1,162) ¼ 0.05 P ¼ 0.8280 F(2,162) ¼ 19.90 P < 0.0001* (E < M < Y)
Note: * indicates significant p-value.
Table 9 Summary of results on driving performance responses for lodging signs.
Number of Panels
Target Familiarity Age Group (Elderly (E), Middle (M), Young (Y))
Speed Deviation
Lane Deviation
Maximum Deceleration
F(1,148) ¼ 6.49 P ¼ 0.0118* (9-Panel>6-Panel) F(1,148) ¼ 0.18 P ¼ 0.6747 F(2,148) ¼ 6.71 P ¼ 0.0016* (E > M,Y)
F(1,148) ¼ 1.47 P ¼ 0.2273
F(1,149) ¼ 3.68 P ¼ 0.0569
F(1,148) ¼ 0.0002 P ¼ 0.9898 F(2,148) ¼ 6.14 P ¼ 0.0027* (E > M,Y)
F(1,149) ¼ 0.0467 P ¼ 0.8293 F(2,149) ¼ 3.96 P ¼ 0.0211 (E > M)
Note: * indicates significant p-value.
Table 10 Summary of results on driving performance responses for attraction signs.
Logo Format Age Group (Elderly (E), Middle (M), Young (Y))
Speed Deviation
Lane Deviation
Maximum Deceleration
F(1,170) ¼ 3.82 P ¼ 0.0523 F(2,170) ¼ 35.445 P<0.0001* (E > M > Y)
F(1,176) ¼ 0.26 P ¼ 0.61 F(2,176) ¼ 26.38 P<0.0001* (E > M,Y)
F(1,159) ¼ 1.09 P ¼ 0.30 F(2,159) ¼ 2.62 P ¼ 0.0763
Note: * indicates significant p-value.
panel signs, as compared to all other conditions. However, there was no significant difference in accuracy for six-panels vs. ninepanels when drivers were looking for familiar targets; a finding in-line with Kaber et al. (2015). Hypothesis 2 posited that accuracy would be higher for pictorial targets than for text targets; however, this expectation was also unsupported. Drivers demonstrated comparable detection accuracy when searching pictorial and text-based targets. In this study, comparison of text and pictorial logos was made for attraction signs only, which were unfamiliar targets. From a business perspective, our results suggest that when a target is unfamiliar to a driver, spending more money for a pictorial logo might not be beneficial as it does not significantly improve detection accuracy over plain text. Hypothesis 3 posited that detection accuracy would be higher for searches of familiar targets compared to unfamiliar targets and was supported by the data. This hypothesis was based on Hawkins
and Rose's (2005) study and our results were in-line their findings that detection accuracy was higher for familiar signs. Furthermore, the lowest detection accuracy occurred when drivers were looking for unfamiliar targets in nine-panel logo signs as compared to all other conditions. Hypothesis 4 posited that detection accuracy would be higher for younger drivers followed by middle-aged and elderly groups and the expectation was partially supported by data. The analyses on both lodging and attraction signs revealed elderly drivers to be less accurate in identifying targets in comparison to middle-aged and young drivers. However, it is important to note that when a business target was familiar, elderly drivers demonstrated comparable detection accuracy to middle-aged and young drivers. In addition, when drivers were exposed to nine-panel signs, there was no significant difference in detection accuracy among age groups.
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fixation frequency, as compared to middle-aged and young drivers. Our results suggest elderly drivers adopt more conservative strategies and allocate more attention to the driving task compared to middle-aged and young drivers. These results are also similar to findings of Kaber et al. (2012) that elderly drivers are more vulnerable to increases in roadway complexity as compared to younger drivers. 4.3. Driving performance
Fig. 9. Effect of attraction sign format on speed deviation among different age groups.
Fig. 10. Effect of number of panels on lane deviation among different age groups.
4.2. Attention allocation Hypothesis 5 posited that visual attention to six-panel service signs would be comparable to attention to nine-panel signs and was supported by the data. There was no significant effect of panel count on longest glance duration or fixation frequency. These results are in-line with Kaber et al. (2015) and Hummer and Maripalli (2008) findings that attention allocation for six-panel signs was comparable to nine-panel signs. Hypothesis 6 posited that attention to text-based logos would be greater than attention to pictorial logos but the expectation was not supported by data. There was no significant effect of logo format on longest glance duration or fixation frequency. Again, this finding might be due to the fact that our comparison of text vs. pictorial logos was limited to unfamiliar attraction targets. On this basis, attentional demands required for finding a text-based target were not greater than those for pictorial-based targets. Hypothesis 7 posited that visual attention to logo signs would increase in search for unfamiliar targets compared to familiar targets and was unsupported by the data. There was no significant effect of target familiarity on either attention allocation response. It is possible that drivers were looking for colors to identify targets, which would be similar for both unfamiliar and familiar targets. Hypothesis 8 posited that visual attention to logo signs would be greatest for young drivers followed by middle-aged and elderly groups, and was generally supported by data. Young drivers exhibited significantly longer off-road glance durations compared to middle-aged drivers. In addition, middle-aged drivers had significantly longer glance durations compared to elderly drivers. Furthermore, it was found that elderly drivers had lower off-road
Hypothesis 9 posited that vehicle control would be comparable in the presence of six-panel and nine-panel signs but the expectation was not supported by data. Although maximum deceleration responses for six-panel and nine-panel signs were comparable, drivers demonstrated greater speed deviations when exposed to nine-panel signs in comparison to six. Our findings suggest drivers adopt a more conservative driving strategy with more speed reductions in viewing nine-panel signs (53% speed reductions) vs. six-panel (45% speed reductions) on exit ramps. In addition, middle-aged drivers were found to exhibit greater lane deviations when exposed to nine-panel signs. Kaber et al. (2015) and Zhang et al. (2013) found no operational differences in driver performance when exposed to six-panel logo signs as compared to ninepanel signs; however, those studies were limited to assessing driver performance on freeways (not ramps). Hypothesis 10 posited that vehicle control would degrade with text target search as compared to pictorial target search. This expectation was not supported by the data. There was no difference in speed deviation, lane deviation or maximum deceleration when drivers were searching for text-based targets or pictorial logos. Results revealed that text-based signs did not impose greater attentional demand than pictorial signs; therefore, drivers maintained performance levels. However, there was a significant interaction between age group and logo format on speed deviations. Elderly and young drivers speed deviations were limited when exposed to text-based signs but middle-aged drivers exhibited greater deviations. Hypothesis 11 posited that vehicle control would be degraded in unfamiliar target search compared to familiar target search but was not supported by data. There was no effect of target familiarity on driver performance responses. Results suggest that searching for an unfamiliar logo target does not require more attention than a familiar logo and drivers can simultaneously allocate sufficient attentional resources to driving tasks. Hypothesis 12 posited that vehicle control would be degraded for elderly drivers followed by young and middle-aged drivers. This expectation was partially supported by data. Elderly drivers demonstrated worse driving performance (greater lane deviations) and more conservative control strategies (more speed reductions (56%) and greater maximum deceleration) as compared to middleaged (44% speed reductions) and young drivers (31% speed reductions). These findings are in-line with prior studies revealing that elderly drivers exhibit poorer vehicle control in comparison to younger drivers (e.g. Hummer, 1989). 5. Conclusions The goal of this research was to investigate the effects of driver age, specific service sign content and format, as well as sign familiarity, on driver performance and attention allocation on freeway exit ramps. Results indicate that drivers are more accurate in identifying target businesses in signs when exposed to six-panel vs. nine-panel signs and when searching for familiar vs. unfamiliar targets. In addition, it was found that elderly drivers are generally less accurate in identifying target businesses in signs in comparison
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to middle-aged and young drivers. Regarding attention allocation measures, our results indicate that elderly drivers adopt more conservative behaviors (lower off-road fixation frequencies and shorter glances) and allocate more attention to driving tasks in comparison to middle-aged and young drivers. Regarding driving performance responses, results indicated that drivers adopt a more conservative vehicle control strategy (more speed reductions on ramps) when exposed to nine-panel signs as compared to six-panel signs. Finally, findings suggest that elderly drivers generally exhibit poorer driving performance (greater lane deviations) and more conservative control strategies (more speed reductions and greater maximum deceleration levels) as compared to middle-aged and young drivers. Previously, these observations were colloquial notions for which we now have some objective evidence. It is also important to note that despite a number of our literature-based hypotheses going unsupported by the simulator and eye-tracking data, the experiment sample size and the high numbers of data points on all responses ensured sufficient power of tests (b < 0.35) for all inferential statistical analyses. Therefore, sensitivity was sufficient for all reported tests. 5.1. Limitations One limitation of this study is that all roadway scenarios were presented under daylight conditions and moderate traffic density (~3e4 vehicles per lane per minute or Level of Service A conditions). Furthermore, due to limitations of the driving simulator software, drivers were always asked to take the second exit to the ramp and the simulation was terminated after they passed the crossing street at the interchange. Results may not be applicable to more complex roadway conditions or higher simulated traffic densities. Another limitation of the study is that drivers were fully committed to the driving task and there were no in-vehicle distractions. Our results may not be applicable to situations in which drivers are, for example, engaged in talking on a cell phone or to passengers in a vehicle. Finally, this study was conducted using a fixed-base driving simulator that did not convey motion or kinesthetic cues to drivers. This setup might have limited simulator realism. 5.2. Future work Future research should investigate more complex driving scenarios including adverse weather conditions and traffic congestion. Another future direction might be to assess the effect of on-road signage on driver performance and attention allocation while they are also exposed to in-vehicle distractions and in using a fullmotion simulator system, possibly generating different steering and braking responses. Acknowledgments Funding for this project was provided by NCDOT (Grant No. 2015e46). The authors would like to thank Ron King, Signing Program Engineer, and Kevin Lacy, State Traffic Engineer, for their contributions in formulating the research questions, planning the experiment and reviewing the driving simulation scenario. The opinions expressed are those of the authors and do not necessarily
reflect the views of the NCDOT.
References Campbell, J., Lichty, M., Brown, J., Richard, C., Graving, J., Graham, J., O'Laughlin, M., Torbic, D., Harwood, D., 2012. NCHRP Report 600: Human Factors Guidelines for Road Systems. Transportation Research Board, Washington, D.C. Carter, D., Wang, I., 2007. Evaluating Options to Increase Specific Service (Logo) Signs from Six Businesses to Nine Businesses Per Service. University of North Carolina Highway Safety Research Center. https://connect.ncdot.gov/resources/ safety/Safety%20Evaluation%20Completed%20Projects/Logos_UNC.pdf. Chen, X., Gao, L., Wei, Z., Li, Q., 2007. Correlation between driver's factors and traffic accident rate. J. Beijing Univ. Technol. 33, 697e701. Cottrell Jr., B.H., Edara, P., 2011. Evaluation of adding distance information to freeway-specific service (logo) signs. J. Transp. Eng. 137 (11), 782e788. Dagnall, E.E., Katz, B.J., Bertola, M.A., Shurbutt, J., 2013. Business logo signing evaluation of the number of logo panels on specific service signs. Transp. Res. Rec. 2365, 82e89. Dingus, T.A., Antin, J.F., Hulse, M.C., Wierwille, W.W., 1989. Attentional demand requirements of an automobile moving-map navigation system. Transp. Res. Part A 23 (4), 301e315. Edquist, J., Horberry, T., Hosking, S., Johnston, I., 2011. Effects of advertising billboards during simulated driving. Appl. Ergon. 42, 619e626. Federal Highway Administration, 2009. Manual on Uniform Traffic Control Devices for Streets and Highways. United States Department of Transportation, Washington, DC. http://mutcd.fhwa.dot.gov/pdfs/2009/mutcd2009edition.pdf. Firestine, M., McGee, H., Toeg, P., 1989. Improving Truck Safety at Interchanges: Summary Report. Report No. FHWA IP-08-024. D.C.: Federal Highway Administration, Washington. Hawkins Jr., H.G., Rose, E.R., 2005. Effects of adding dual-logo panels to specific service signs: a human factors study. Transp. Res. Rec. 1918, 108e115. Holmqvist, K., Nystrom, M., Andersson, R., Dewhurst, R., Jarodzka, H., van de Weijer, J., 2011. Eye Tracking: a Comprehensive Guide to Methods and Measures. Oxford University Press, New York. Horrey, W.J., Wickens, C.D., 2004. Driving and side task performance: the effects of display clutter, separation, and modality. Human Factors. J. Hum. Factors Ergonomics Soc. 46 (4), 611e624. Hummer, J.E., 1989. Supplemental interchange signing and driver control behavior. Transp. Res. Rec. 1213, 56e63. Hummer, J.E., Maripalli, U.K., 2008. Laboratory test of driver responses to nine-panel logo signs. Transp. Res. Rec. 2056, 52e59. Inman, V.W., Katz, B.J., Hanscom, F.R., 2006. Navigation signing for roundabaouts. Transp. Res. Rec. 1973, 18e26. Kaber, D., Pankok Jr., C., Corbett, B., Ma, W., Hummer, J., Rasdorf, W., 2015. Driver behavior in use of guide and logo signs under distraction and complex roadway conditions. Appl. Ergon. 47, 99e106. Kaber, D., Zhang, Y., Jin, S., Mosaly, P., Garner, M., 2012. Effects of hazard exposure and roadway complexity on young and older driver situation awareness and performance. Transp. Res. Part F 15, 600e611. Kennedy, R.S., Lane, N.E., Berbaum, K.S., Lilienthal, M.G., 1993. Simulator sickness questionnaire: an enhanced method for quantifying simulator sickness. Int. J. Aviat. Psychol. 3 (3), 203e220. Liu, Y., 2005. A simulated study on the effects of information volume on traffic signs, viewing strategies and sign familiarity upon driver's visual search performance. Int. J. Industrial Ergonomics 35, 1147e1158. McCart, A.T., Northrup, V.S., Retting, R.A., 2004. Types and characteristics of ramprelated motor vehicle crashes on urban interstate roadways in Northern Virginia. J. Saf. Res. 35, 107e114. NHTSA, 1997. Traffic Safety Facts 1996: Older Population, vol. 4. National Highway Traffic Safety Administration, Washington, DC. http://www-nrd.nhtsa.dot.gov/ Pubs/96OlderPop.pdf. Santos, J., Merat, N., Mouta, S., Brookhuis, K., Waard, D., 2005. The interaction between driving and in-vehicle information systems: comparison of results from laboratory, simulator and real-world studies. Transp. Res. Part F 8 (2), 135e146. Tefft, B.C., 2012. Motor Vehicle Crashes, Injuries, and Deaths in Relation to Driver Age: United States, 1995e2010. Project Summary Report. AAA Foundation for Traffic Safety, Washington, DC. Wang, Y., Mehler, B., Reimer, B., Lammers, V., D'Ambrosio, L.A., Coughlin, J.F., 2010. The validity of driving simulation for assessing differences between in-vehicle informational interfaces: a comparison with field testing. Ergonomics 53 (3), 404e420. Zhang, Y., Harris, E., Rogers, M., Kaber, D., Hummer, J., Rasdorf, W., Hu, J., 2013. Driver distraction and performance effects of highway logo sign design. Appl. Ergon. 44, 472e479.
Contents lists available at ScienceDirect
Applied Ergonomics journal homepage: www.elsevier.com/locate/apergo
Driver performance and attention allocation in use of logo signs on freeway exit ramps Maryam Zahabi a, Patricia Machado b, Mei Ying Lau a, Yulin Deng a, Carl Pankok Jr. a, Joseph Hummer c, William Rasdorf b, David B. Kaber a, * a b c
Edward P. Fitts Department of Industrial & Systems Engineering, North Carolina State University, United States Department of Civil, Construction, and Environmental Engineering, North Carolina State University, United States Staff Engineer, Mobility and Safety Division, North Carolina Department of Transportation, United States
a r t i c l e i n f o
a b s t r a c t
Article history: Received 14 August 2016 Received in revised form 29 January 2017 Accepted 1 June 2017
The objective of this research was to quantify the effects of driver age, ramp signage configuration, including number of panels, logo format and sign familiarity, on driver performance and attention allocation when exiting freeways. Sixty drivers participated in a simulator study and analysis of variance models were used to assess response effects of the controlled manipulations. Results revealed elderly drivers to demonstrate worse performance and conservative control strategies as compared to middleaged and young drivers. Elderly drivers also exhibited lower off-road fixation frequency and shorter offroad glance durations compared to middle-aged and young drivers. In general, drivers adopted a more conservative strategy when exposed to nine-panel signs as compared to six-panel signs and were more accurate in target detection when searching six-panels vs. nine and with familiar vs. unfamiliar logos. These findings provide an applicable guide for agency design of freeway ramp signage accounting for driver demographics. © 2017 Elsevier Ltd. All rights reserved.
Keywords: Roadway logo signs Driving simulation Exit ramp Driver distraction Highway safety
1. Introduction 1.1. Sign usage on freeways Several research studies have examined the effects of roadway sign characteristics and familiarity on driver behavior and attention allocation in freeway driving. These studies can be categorized as: (1) observational, (2) presentation-based experiments, and (3) controlled driving simulations. Observational studies indicate little difference in driving performance among specific service signs with different numbers of business panels (e.g., six vs. nine logos). Carter and Wang (2007) video-recorded vehicle activity on various stretches of North Carolina freeways and identified instances of unusual driver behavior (e.g., braking, drifting, and line encroachment). They found the number of unusual behaviors at nine-panel logo sign locations to be comparable to occurrences at six-panel sign locations. However, the lack of control in observational studies leaves some doubt as to the true effects of increased logo
* Corresponding author. Dept. ISE, North Carolina State University, 400 Daniels Hall, Raleigh, NC 27695-7906, United States. E-mail address: [email protected] (D.B. Kaber). http://dx.doi.org/10.1016/j.apergo.2017.06.001 0003-6870/© 2017 Elsevier Ltd. All rights reserved.
panels on driver behavior. Presentation-based experiments, which present sign images to participants (absent of any driving task or simulation requirements), have focused on the effect of variations in signage type on target business panel detection accuracy. Hummer and Maripalli (2008) conducted a slide-based experiment presenting realistic logo signs to subjects and asking them to identify whether a specific brand was included. Results showed overall accuracy for six-panel signs to be significantly greater than mixed-use and ninepanel signs. In another study, Dagnall et al. (2013) found that response times increased as the number of panels on a logo sign increased from four to six to nine. It was found that participants were generally able to recall 3e4 businesses regardless of the number of panels on the sign. They also reported that within the number of panels presented to viewers, response time was shorter for text logos than for pictorial logos, although the mean difference was only 0.1 s. In another presentation-based study on the effect of signage format, it was reported that pictorial signs enhanced viewer ability to recall signs and resulted in higher target search accuracy than text-only signs (Liu, 2005). In another presentationbased study of the effect of logo sign familiarity, Hawkins and Rose (2005) found search accuracy to increase when a target business
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logo was familiar (as rated by an experimenter) compared to when it was unfamiliar. Returning to the Hummer and Maripalli (2008) study, they reported participants were much more accurate in searching for unfamiliar vs. familiar logos. The authors postulated that increased search performance could have been due to participant scanning for a novel item on a board populated with mostly familiar images. While these presentation-based experiments integrated a great degree of experimental control, there is an absence of resource competition between sign search and driving tasks in such studies and, therefore, results may not be generalizable to real driving performance. Finally, in a controlled driving simulation experiment, Zhang et al. (2013) compared driving performance and visual attention allocation to six-panel, overflow combination, and nine-panel logo signs. They found no significant differences in signal detection, response time, maximum off-road glance duration, lane deviation, or reaction time to a hazard among the three sign types. In another study, Kaber et al. (2015) found no significant differences in signal detection, off-road fixation frequency, longest off-road glance duration, lane maintenance deviations, or speed control when exposed to six-panel logo signs, nine-panel signs, or green mileage guide signs. Related to the format of signs, Hummer (1989) reported that driving performance (measured as speed deviation, lane maintenance, and acceleration control) significantly decreased for text panels compared to pictorial logos. Driving simulation experiments are most similar to real-world driving, as participants are required to allocate resources to the driving task in addition to sign search tasks, etc. 1.2. Sign usage on freeway exit ramps A study of interchange collisions in Northern Virginia found that a majority of crashes occurred when drivers were exiting the freeway (McCart et al., 2004). They attributed this finding to the difficult task of slowing a vehicle from high-speed freeway travel while negotiating a (sometimes sharp) turn and navigating to a destination, simultaneously. In an assessment of roundabout signage design, where drivers are posed with similar vehicle control and perceptual requirements as at a freeway interchange (i.e., braking, negotiating a turn, and navigating), it was found that lane selection accuracy decreased significantly as the number of items on signs increased (Inman et al., 2006). Interchange logo signs typically include distances and directions (i.e., a left or right arrow) beneath logos in order to provide further information about business locations. Related to this, Cottrell and Edara (2011) found that adding distance information to freeway logo signs decreased mean legibility distance but did not affect the number of crashes in a “before-after” analysis. However, these signs were not located on interchanges; they were located roadside on freeways. Unfortunately, there is no existing research on sign usage at interchanges, which is surprising given previous reports that 18% of all interstate crashes occur at interchanges, despite the fact that interchanges constitute less than 5% of total freeway mileage (Firestine et al., 1989). 1.3. Driver age The relationship between driver age and crash rates has been shown in several studies. National Highway Traffic Safety Administration (NHTSA) reported that automobile crashes increase around age 65 and the fatality rate per million miles of travel for drivers over 65 years of age was found to be 17 times that of the 25e65 age group (NHTSA, 1997). In a more recent study, Tefft (2012) found that crash rates were highest for drivers between the age of 16e17, decreased until ages 60e69 but increased after
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age of 70. Regarding the effect of age on driver performance and attention allocation, Dingus et al. (1989) found that participants older than 50 years took significantly longer to complete tasks, had longer glance times to in-car instruments, and committed significantly more errors than participants younger than 50 years. Much more recently, Edquist et al. (2011) found that drivers 65 years and older were the slowest to change lanes in a lane changing task, followed by firstyear drivers under 25 years of age. Related to this work, Hummer (1989) found that drivers over 50 years of age exhibited poorer speed maintenance and poorer acceleration control in logo sign use as compared with drivers younger than 50 years of age. Finally, and most recently, Dagnall et al. (2013) reported that participants older than 50 years of age took longer to identify a sign in a search task than younger drivers, regardless of the number of panels on the logo sign. Although crash reports have shown higher numbers of crashes for elderly drivers, the majority of prior driving simulations experiments have been limited to young and middle-age drivers (e.g. 18e58 yrs, Zhang et al., 2013; 25e59 yrs, Kaber et al., 2015). On this basis, there remains a need to investigate a broader driver sample, as the results of prior studies on the role of signage in driver behavior may not be generalizable to the elderly driving population. 1.4. Problem statement Although simulator studies suggest that specific service signs with nine logo panels have little effect on driving performance and attention allocation as compared to six-panel signs (e.g., Zhang et al., 2013; Kaber et al., 2015), these studies have mainly focused on signage on freeways. Furthermore, young and middle-age driver groups have been the focus of study. There are inconsistent results regarding the effect of logo format and driver familiarity with business logos on driving behavior, likely due to a wide range of experimental conditions (e.g., Hummer and Maripalli, 2008; Dagnall et al., 2013). On this basis, and given the high number of crashes at interchanges, the objectives of this study were to answer the following research questions regarding freeway exit ramp driving behavior: - How does service sign logo count (six-panel vs. nine panel) affect driver target detection accuracy, attention allocation and vehicle control? - How does logo sign format (text vs. pictorial) affect driver target detection accuracy, attention allocation and vehicle control? - How does logo target familiarity affect driver target detection accuracy, attention allocation and vehicle control? - How does driver age affect target detection accuracy, attention allocation and vehicle control when exposed to logo signs?
1.5. Hypotheses Related to the above research questions, and based on the literature review, we formulated a series of research hypotheses for investigation in a driving simulation study. The hypotheses are detailed in Table 1. 2. Methodology 2.1. Participants Sixty participants were uniformly recruited to each of three age groups of (1) 18e22 yrs, (2) 23e64 yrs, and (3) 65 þ yrs. Gender
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Table 1 Experiment hypotheses (Hypothesis Number in Parenthesis). Detection Accuracy Number of Panels (6 vs. 9)
Accuracy of business target detection will be consistent among six-panel and ninepanel service signs (H1) Logo Format Accuracy of business target detection will (Text vs. Pictorial) be higher for pictorial targets than for text targets (H2) Accuracy of target detection will be higher Target Familiarity for search of familiar targets compared to (Familiar vs. unfamiliar targets (H3) Unfamiliar) Age Group Accuracy of business target detection will (Young, Middle, Elderly) be higher for young driver group followed by middle and elderly age groups (H4)
Attention Allocation
Vehicle Control
Visual attention to six-panel logo signs will be comparable to attention to nine-panel signs (H5) Attention to text-based logo signs will be longer than attention to pictorial-based logo signs (H6)
Vehicle control will be comparable in presence of six-panel and nine-panel logo signs (H9)
Visual attention to logo signs will increase in search of unfamiliar target compared to search for familiar target (H7) Visual attention to logo signs will increase for young driver group followed by middle and elderly age groups (H8)
Vehicle control will be degraded for unfamiliar targets compared to familiar targets (H11)
was balanced (i.e., 10 females and 10 males) within each age group. The age categories were defined using regression analysis on crash rate data (Tefft, 2012), which indicated young (less than 23 yrs) and elderly drivers (greater than 64 yrs) had significantly higher crash rates in comparison to middle-age drivers (between 23 and 64 yrs). These categories are also in-line with the findings of Chen et al. (2007) that drivers between the age of 25 and 60 years of age have lower crash rates in comparison to those who are younger than 25 years or older than 60 years. All participants were required to have a valid North Carolina driver's license and have 20/20 vision (or corrected vision). 2.2. Apparatus 2.2.1. Driving simulator A STISIM Drive M400 driving simulator (System Technology, Inc., Hawthorne, CA) was used in this experiment (see Fig. 1). The simulator setup included three 38-inch high resolution television monitors providing a 135-degree field of view for drivers. A set of full-size driving controls, including accelerator, brake pedal, steering wheel, and turn signals were used to provide drivers with realtime feedback (based on a Ford Taurus vehicle dynamics model). Similar setups have been validated by Santos et al. (2005) and Wang et al. (2010) as providing realistic simulations of real-world driving as well as research findings corresponding with results of on-road observational studies. 2.2.2. Eye tracking system A FaceLAB 5.1 (Seeing Machines, Australia) eye tracking system was used to collect real-time data on driver gaze positions. The system hardware included two cameras and an infrared light
Fig. 1. Driving simulator setup.
Vehicle control will be degraded for text targets compared to pictorial targets (H10)
Vehicle control will be degraded for elderly driver group followed by young and middle age groups (H12)
emitter (see Fig. 2). The cameras detect reflection of the infrared light on the surface of a driver's eyeballs. This reflection is used along with an outline of the iris or pupil to extrapolate direction of driver gaze in the simulation environment. Eye movements were recorded at a frequency of 60 Hz with an accuracy of 0.5 to 1 of rotational error.
2.3. Simulated driving environment The simulated driving environment presented a four-lane, rural Interstate freeway, including two standard diamond interchanges spaced 3 miles apart, each with a single exit Fig. 3. The simulation was designed to represent as accurately as possible a realistic driving environment, following regulations published by the North Carolina Department of Transportation (NCDOT) and the Manual on Uniform Traffic Control Devices (MUTCD; Federal Highway Administration, 2009). Each interchange included 500-ft deceleration and acceleration lanes. There were complete sets of exit gore, guide and service signs before and after interchanges, as required by the MUTCD. Signs preceding each interchange included specific service signs (lodging, food, and gas/attraction categories, as described in Section 2J.03 of the MUTCD) and standard guide signs. Fig. 4 shows the specific sign configurations used in the simulation. Fig. 5 shows an example of service sign configuration presented at the interstate exit ramps, including distance (miles to the specific business) and directional indicators (turn at the end of the ramp). In addition, Figs. 6 and 7 show the layout of signage located before each interchange and on the exit ramp.
Fig. 2. Eye tracking system hardware placed in front of simulator screens.
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Fig. 3. Overall layout of driving scenarios. Plate (a): Six-panel lodging and food signs and six-panel “overflow combination” sign. Plate (b): Nine-panel lodging and food signs and nine-panel “overflow combination” sign.
Fig. 4. Specific service sign configurations on freeway. Plate (a): Exit ramp six-panel lodging and food signs and six-panel “overflow combination” sign. Plate (a): Exit ramp ninepanel lodging and food signs and nine-panel “overflow combination” sign.
2.4. Independent variables Driver age (young, middle-aged, and elderly) served as a grouping variable in this study (with 20 participants in each group). Independent variables related to roadway sign configuration included the number of business panels on specific service signs, familiarity of logo targets for driver search, and logo format. Regarding the number of panels, the experiment compared sixwith nine-panel signs (see Fig. 8 for examples). Logo target familiarity was manipulated by presenting participants with either a nationally-known lodging chain or an unfamiliar independent hotel for search. Each unfamiliar lodging was presented only once across all experiment trials in order to eliminate any participant learning effects. All lodging signs contained 67% familiar logos and 33% unfamiliar logos, based on Hummer and Maripalli's (2008) observation of one unfamiliar target among familiar targets
producing high search accuracy. Finally, logo format was manipulated by asking participants to search for either a text or pictorial attraction sign. All attraction logos used in the experiment were unfamiliar to prevent any confound between logo format and familiarity. 2.5. Experiment design The experiment followed a 3x2x2x2 mixed within- and between-subject design with a full crossing of number of sign panels, lodging target familiarity, and attraction panel format. The order of trials was randomized to prevent potential order effects. Table 2 presents the trial conditions as well as target logo characteristics, including distance and direction, which participants were asked to identify. It is important to note that lodging and attraction targets were tested in separate trials.
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Fig. 5. Specific service sign configurations on freeway exit ramps.
Fig. 6. Layout of guide and specific service signs located before interchange.
2.6. Dependent variables Dependent variables were grouped into three categories, including sign detection accuracy, visual attention allocation, and driving performance. Attention allocation and driving performance measures for lodging signs (the first sign on the ramp) were calculated during time “windows” when the sign became readable for drivers (650 ft ahead of the sign) and ending when the vehicle passed the sign. However, since the distance between two consecutive food and attraction signs on a ramp was limited to 300 ft, attention allocation and driving performance measures for attraction signs were calculated based on time windows beginning 300 ft ahead of the sign (to ensure a driver was looking at the sign of interest) and ending when the vehicle passed the sign.
2.6.1. Accuracy Once drivers exited the freeway at the interchange, they were asked to verbally indicate the distance and direction of a target business (lodging or attraction). Accuracy was defined as the percentage of “correct” driver responses to all target sign exposures. A response was considered “correct” when a participant accurately identified the distance and/or direction to a business target. 2.6.2. Attention allocation The area of the center screen of the driving simulator, in which logo signs appeared during simulation trials, was identified as the area of visual interest (AOI) for visual behavior analysis. Two eye movement measures were derived from the FaceLab system output, including fixation frequency and longest glance duration to the AOI.
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Fig. 7. Layout of exit gore and specific service signs on ramp with other traffic controls.
Fig. 8. Examples of six-panel and nine-panel lodging logo signs on the ramp.
Table 2 Experiment design and targets. Number of Panels
Lodging Target
6
Familiarity
Attraction Target Name
Distance
Direction
Format
Distance
Direction
Familiar
0.4
Right
Text
Name
0.5
Right
6
Unfamiliar
0.2
Left
Pictorial
0.9
Left
9
Familiar
0.3
Left
Text
0.9
Left
9
Unfamiliar
0.6
Right
Pictorial
0.5
Left
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Fixation frequency (a percentage) was determined as the number of fixations to the AOI divided by total number of fixations during the target detection phase. A fixation was defined as any participant gaze with a velocity less than 100 /s for a minimum of 100 ms (Holmqvist et al., 2011). A glance was defined as the total time the focus of attention remained within the AOI, encompassing both fixations and saccades (rapid eye movements). 2.6.3. Driving performance Driver performance measures included speed deviation, lane deviation, and maximum deceleration. Speed deviation was calculated as the average absolute deviation of actual vehicle speed from the posted ramp speed limit (40 mph). Lane deviation was measured as the average absolute deviation of the center of the vehicle from the center of the lane. Maximum deceleration was calculated as the maximum of all deceleration values during the window of target sign observation. 2.7. Procedure Participants were asked to complete an informed consent form, a demographic questionnaire and a baseline Simulator Sickness Questionnaire (SSQ; Kennedy et al., 1993). They were then escorted to the driving simulator and provided with a brief introduction to the equipment, followed by two training trials (one for assessing lane and speed deviations on the freeway and another for assessing maximum deceleration on the ramp). The training trials were identical to experiment trials, save the inclusion of specific service signs. At the end of the training trials, participant performance was assessed to verify that they met established criteria, including average jlane deviationj 1:37 ft, averagejspeed deviationj 1 mph, and jmaximum decelerationj 31:7 ft=s2 . The lane deviation criterion was based on Horrey and Wickens (2004) research. The speed deviation criterion was established based on average speed deviation for five experienced simulator drivers. The maximum deceleration criterion was based on the 85th percentile of recommended deceleration to a complete stop on a freeway off-ramp under dry roadway and clear weather conditions (Campbell et al., 2012). Participants were administered another SSQ after training to ensure no motion sickness symptoms. Participants were provided with a short slide presentation on the signs to appear in test trials. The presentation covered speed limit, distance, specific service, and advance guide signs. This step was followed by calibration of the FaceLab eye tracking system. Participants then completed all eight experiment trials. Before each trial, they were shown the target business logo (lodging or attraction) for search. When drivers passed a lodging or attraction sign containing the target, they were expected to callout the distance and direction. Participants were given a 5-min rest period between trials, and were administered a SSQ every two trials to ensure no motion sickness. They were given additional breaks if symptoms were observed. Participants were compensated $20/hour. The experiment took about 3 h. 2.8. Data analyses Before conducting any inferential statistical tests, a data screening process was conducted to identify outliers due to participants not following instructions and/or technical difficulties with the simulator or eye tracking apparatus. Diagnostics were conducted on all dependent variables to ensure conformance of response data with parametric test assumptions of homoscedasticity and residual normality. Variance homoscedasticity was checked using Bartlett's tests and residual normality was assessed by inspection of normal probability plots and the Shapiro-Wilk
normality test. In case of parametric assumption violations, log, square root, and exponential transformations to the power of lambda (identified by the Box-Cox method) were applied to responses. Contingency table analyses were used to assess the effects of the sign configuration variables on sign detection accuracy with ChiSquare statistics generated based on expected likelihood ratios. Analysis of Variance (ANOVA) models were also developed for driver performance and visual behavior effects tests on the experimental manipulations, with separate analyses for lodging and attraction targets. Age group was included in the ANOVA models as a between-subject variable. Due to the fact that attraction business panels were always presented in groups of three (regardless of the total number of panels on a specific service sign), attraction target analyses only assessed the effect of driver age group, logo format and the interaction on responses; panel count was not a predictor variable. Lodging target analyses assessed the effects of driver age group, number of sign logos, and logo familiarity, as well as their interactions, on the dependent variables. Trial number was included in the statistical models as a co-variate and was removed in the absence of a significant effect on a response. Where appropriate, Tukey's Honest Significant Difference (HSD) post-hoc procedure was applied to identify differences among levels of any significant effects and results are presented in summary tables (below). A significance level of p 0.05 was set as a criterion for the study. 3. Results 3.1. Accuracy Tables 3 and 4 present summaries of the experiment results for target identification accuracy for lodging and attraction signs, respectively. Note that accuracy was ultimately analyzed for both direction and distance identification. With respect to the effect of sign familiarity within age group for lodging target distance identification accuracy, chi-square tests revealed no significant difference among groups for familiar logos (c2 (2) ¼ 2.253, p ¼ 0.1966) but an effect was present for unfamiliar logos (c2 (2) ¼ 7.220, p ¼ 0.0271). Post-hoc tests revealed accuracy to be significantly lower for the elderly group (38%) compared to young (64%) and middle-aged (65%) drivers for unfamiliar logos. Regarding the effect of panel count within age group for lodging target distance identification accuracy, chi-square tests revealed no significant difference among groups for nine-panel signs (c2 (2) ¼ 2.883, p ¼ 0.2366) but there was an effect for six-panel signs (c2 (2) ¼ 12.738, p ¼ 0.0017). Post-hoc tests revealed accuracy to be significantly lower for elderly drivers (69%) compared to young (95%) and middle-aged (95%) groups for six-panel signs. (We provide some explanation of this finding in the Discussion section.) Contingency table analysis also revealed a significant interaction between logo familiarity and the number of panels on distance identification accuracy for lodging signs (c2 (3) ¼ 66.145, p < 0.0001). Table 5 shows that accuracy was lower for unfamiliar targets in nine-panel signs compared to all other panel count and familiarity conditions. On the effect of target familiarity within age group for lodging target direction identification accuracy, Chi-Square tests revealed no significant differences among groups for familiar logos (c2 (2) ¼ 1.906, p ¼ 0.3856) but there was an effect for unfamiliar logos (c2 (2) ¼ 6.839, p ¼ 0.0327). Post-hoc tests revealed accuracy to be significantly lower for elderly drivers (56%) compared to middleaged (83%) drivers for unfamiliar logos. Regarding the effect of panel count within age group for lodging target direction identification accuracy, chi-square tests revealed no significant differences
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Table 3 Summary of accuracy results for lodging targets.
Number of Panels
Target Familiarity
Age Group (Elderly (E), Middle (M), Young (Y))
Direction Identification Accuracy
Distance Identification Accuracy
c2(1) ¼ 16.858
P < 0.0001* (6-Panel>9-Panel) c2(1) ¼ 8.406 P ¼ 0.0037* (Familiar > Unfamiliar) c2(2) ¼ 6.859 P ¼ 0.0324* (E < M)
c2(1) ¼ 32.286 P < 0.0001* (6-Panel>9-Panel) c2(1) ¼ 16.675 P < 0.0001* (Familiar > Unfamiliar) c2(2) ¼ 9.995 P < 0.0084* (E < Y,M)
Direction Identification Accuracy
Distance Identification Accuracy
c2(1) ¼ 0.094
c2(1) ¼ 0.320 P ¼ 0.5719 c2(2) ¼ 45.728 P < 0.0001* (E < Y,M)
Note: * indicates significant p-value.
Table 4 Summary of accuracy results for attraction targets.
Logo Format
P ¼ 0.7586 c2(2) ¼ 38.786 P < 0.0001* (E < Y,M)
Age Group (Elderly (E), Middle (M), Young (Y)) Note: * indicates significant p-value.
Table 5 Interaction effect of number of panels and familiarity on lodging sign distance identification accuracy.
Table 6 Interaction effect of number of panels and familiarity on lodging sign direction identification accuracy.
Number of Panels
Familiarity
Accuracy
Significance Grouping
Number of Panels
Familiarity
Accuracy
Significance Grouping
6 6 9 9
Familiar Unfamiliar Familiar Unfamiliar
85% 86% 76% 27%
A A A B
6 6 9 9
Familiar Unfamiliar Familiar Unfamiliar
87% 93% 86% 50%
A A A B
among groups for nine-panel signs (c2 (2) ¼ 2.883, p ¼ 0.2366) but there was a significant effect for six-panel signs (c2 (2) ¼ 10.773, p ¼ 0.0046). Post-hoc tests showed accuracy to be significantly lower for elderly drivers (77%) compared to young (95%) and middle-aged (98%) drivers for six-panel signs. Contingency table analyses also revealed a significant interaction between logo familiarity and the number of panels on direction identification in lodging signs (c2 (3) ¼ 41.323, p < 0.0001). Table 6 shows accuracy was lower when drivers searched for unfamiliar targets in nine-panel signs as compared to all other signage conditions. 3.2. Attention allocation Tables 7 and 8 present summaries of the experiment results on longest glance duration and fixation frequency responses for lodging and attraction targets, respectively. 3.3. Driving performance Tables 9 and 10 present summaries of experiment results on speed deviation, lane deviation and maximum deceleration in lodging and attraction sign use, respectively. Regarding attraction target analyses, there was a significant interaction between age group and service sign logo format (F (2,170) ¼ 3.87, p ¼ 0.0227, 1-b ¼ 0.69) on the speed deviation response. Tukey's HSD post-hoc test revealed that elderly and young drivers produced greater speed deviations when they were exposed to pictorial logos vs. text panels. However, for middle-aged drivers, greater speed deviations occurred with exposure to text-
based signs (see Fig. 9). There was also a significant interaction between age group and panel count (F (2,148) ¼ 3.54, p ¼ 0.0314, 1-b ¼ 0.65) on the lane deviation response for lodging signs. Tukey's HSD tests revealed middle-aged drivers to produce greater lane deviations when exposed to nine-panel signs compared to six (see Fig. 10). There was no significant difference in lane deviations among elderly and young drivers for six- and nine-panel sign exposures. 4. Discussion It is important to note that the inferences on results presented in this section are limited in potential applicability to rural freeway exit ramp driving under daytime roadway conditions with moderate traffic density. 4.1. Accuracy Hypothesis 1 posited that accuracy would be consistent among six- and nine-panel signs. This hypothesis was not supported by the data. Drivers were more accurate in identifying the direction and distance to target businesses when they were exposed to six-panel signs in comparison to nine. Prior studies found that target detection accuracy was consistent among six-panel and nine-panel signs (Kaber et al., 2015). However, Kaber et al. only required participants to search for a familiar target. In this study, drivers searched for familiar and unfamiliar targets in different trials. Contingency table analyses showed that both sign panel count and familiarity of lodging targets impacted driver detection accuracy with significantly lower accuracy in identifying unfamiliar targets in nine-
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M. Zahabi et al. / Applied Ergonomics 65 (2017) 70e80 Table 7 Summary of results on attention allocation responses for lodging targets.
Number of Panels Target Familiarity Age Group (Elderly (E), Middle (M), Young (Y))
Longest Glance Duration
Fixation Frequency
F(1,140) ¼ 0.01 P ¼ 0.9116 F(1,140) ¼ 0.10 P ¼ 0.7499 F(2,140) ¼ 35.50 P < 0.001* (E < M < Y)
F(1,147) ¼ 0.61 P ¼ 0.4376 P(1,147) ¼ 1.42 P ¼ 0.2364 F(2,147) ¼ 15.96 P < 0.0001* (E < Y,M)
Note: * indicates significant p-value.
Table 8 Summary of results on attention allocation responses for attraction targets.
Logo Format Age Group (Elderly (E), Middle (M), Young (Y))
Longest Glance Duration
Fixation Frequency
F(2,143) ¼ 0.60 P ¼ 0.44 F(2,143) ¼ 107.00 P < 0.0001* (E < M < Y)
F(1,162) ¼ 0.05 P ¼ 0.8280 F(2,162) ¼ 19.90 P < 0.0001* (E < M < Y)
Note: * indicates significant p-value.
Table 9 Summary of results on driving performance responses for lodging signs.
Number of Panels
Target Familiarity Age Group (Elderly (E), Middle (M), Young (Y))
Speed Deviation
Lane Deviation
Maximum Deceleration
F(1,148) ¼ 6.49 P ¼ 0.0118* (9-Panel>6-Panel) F(1,148) ¼ 0.18 P ¼ 0.6747 F(2,148) ¼ 6.71 P ¼ 0.0016* (E > M,Y)
F(1,148) ¼ 1.47 P ¼ 0.2273
F(1,149) ¼ 3.68 P ¼ 0.0569
F(1,148) ¼ 0.0002 P ¼ 0.9898 F(2,148) ¼ 6.14 P ¼ 0.0027* (E > M,Y)
F(1,149) ¼ 0.0467 P ¼ 0.8293 F(2,149) ¼ 3.96 P ¼ 0.0211 (E > M)
Note: * indicates significant p-value.
Table 10 Summary of results on driving performance responses for attraction signs.
Logo Format Age Group (Elderly (E), Middle (M), Young (Y))
Speed Deviation
Lane Deviation
Maximum Deceleration
F(1,170) ¼ 3.82 P ¼ 0.0523 F(2,170) ¼ 35.445 P<0.0001* (E > M > Y)
F(1,176) ¼ 0.26 P ¼ 0.61 F(2,176) ¼ 26.38 P<0.0001* (E > M,Y)
F(1,159) ¼ 1.09 P ¼ 0.30 F(2,159) ¼ 2.62 P ¼ 0.0763
Note: * indicates significant p-value.
panel signs, as compared to all other conditions. However, there was no significant difference in accuracy for six-panels vs. ninepanels when drivers were looking for familiar targets; a finding in-line with Kaber et al. (2015). Hypothesis 2 posited that accuracy would be higher for pictorial targets than for text targets; however, this expectation was also unsupported. Drivers demonstrated comparable detection accuracy when searching pictorial and text-based targets. In this study, comparison of text and pictorial logos was made for attraction signs only, which were unfamiliar targets. From a business perspective, our results suggest that when a target is unfamiliar to a driver, spending more money for a pictorial logo might not be beneficial as it does not significantly improve detection accuracy over plain text. Hypothesis 3 posited that detection accuracy would be higher for searches of familiar targets compared to unfamiliar targets and was supported by the data. This hypothesis was based on Hawkins
and Rose's (2005) study and our results were in-line their findings that detection accuracy was higher for familiar signs. Furthermore, the lowest detection accuracy occurred when drivers were looking for unfamiliar targets in nine-panel logo signs as compared to all other conditions. Hypothesis 4 posited that detection accuracy would be higher for younger drivers followed by middle-aged and elderly groups and the expectation was partially supported by data. The analyses on both lodging and attraction signs revealed elderly drivers to be less accurate in identifying targets in comparison to middle-aged and young drivers. However, it is important to note that when a business target was familiar, elderly drivers demonstrated comparable detection accuracy to middle-aged and young drivers. In addition, when drivers were exposed to nine-panel signs, there was no significant difference in detection accuracy among age groups.
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fixation frequency, as compared to middle-aged and young drivers. Our results suggest elderly drivers adopt more conservative strategies and allocate more attention to the driving task compared to middle-aged and young drivers. These results are also similar to findings of Kaber et al. (2012) that elderly drivers are more vulnerable to increases in roadway complexity as compared to younger drivers. 4.3. Driving performance
Fig. 9. Effect of attraction sign format on speed deviation among different age groups.
Fig. 10. Effect of number of panels on lane deviation among different age groups.
4.2. Attention allocation Hypothesis 5 posited that visual attention to six-panel service signs would be comparable to attention to nine-panel signs and was supported by the data. There was no significant effect of panel count on longest glance duration or fixation frequency. These results are in-line with Kaber et al. (2015) and Hummer and Maripalli (2008) findings that attention allocation for six-panel signs was comparable to nine-panel signs. Hypothesis 6 posited that attention to text-based logos would be greater than attention to pictorial logos but the expectation was not supported by data. There was no significant effect of logo format on longest glance duration or fixation frequency. Again, this finding might be due to the fact that our comparison of text vs. pictorial logos was limited to unfamiliar attraction targets. On this basis, attentional demands required for finding a text-based target were not greater than those for pictorial-based targets. Hypothesis 7 posited that visual attention to logo signs would increase in search for unfamiliar targets compared to familiar targets and was unsupported by the data. There was no significant effect of target familiarity on either attention allocation response. It is possible that drivers were looking for colors to identify targets, which would be similar for both unfamiliar and familiar targets. Hypothesis 8 posited that visual attention to logo signs would be greatest for young drivers followed by middle-aged and elderly groups, and was generally supported by data. Young drivers exhibited significantly longer off-road glance durations compared to middle-aged drivers. In addition, middle-aged drivers had significantly longer glance durations compared to elderly drivers. Furthermore, it was found that elderly drivers had lower off-road
Hypothesis 9 posited that vehicle control would be comparable in the presence of six-panel and nine-panel signs but the expectation was not supported by data. Although maximum deceleration responses for six-panel and nine-panel signs were comparable, drivers demonstrated greater speed deviations when exposed to nine-panel signs in comparison to six. Our findings suggest drivers adopt a more conservative driving strategy with more speed reductions in viewing nine-panel signs (53% speed reductions) vs. six-panel (45% speed reductions) on exit ramps. In addition, middle-aged drivers were found to exhibit greater lane deviations when exposed to nine-panel signs. Kaber et al. (2015) and Zhang et al. (2013) found no operational differences in driver performance when exposed to six-panel logo signs as compared to ninepanel signs; however, those studies were limited to assessing driver performance on freeways (not ramps). Hypothesis 10 posited that vehicle control would degrade with text target search as compared to pictorial target search. This expectation was not supported by the data. There was no difference in speed deviation, lane deviation or maximum deceleration when drivers were searching for text-based targets or pictorial logos. Results revealed that text-based signs did not impose greater attentional demand than pictorial signs; therefore, drivers maintained performance levels. However, there was a significant interaction between age group and logo format on speed deviations. Elderly and young drivers speed deviations were limited when exposed to text-based signs but middle-aged drivers exhibited greater deviations. Hypothesis 11 posited that vehicle control would be degraded in unfamiliar target search compared to familiar target search but was not supported by data. There was no effect of target familiarity on driver performance responses. Results suggest that searching for an unfamiliar logo target does not require more attention than a familiar logo and drivers can simultaneously allocate sufficient attentional resources to driving tasks. Hypothesis 12 posited that vehicle control would be degraded for elderly drivers followed by young and middle-aged drivers. This expectation was partially supported by data. Elderly drivers demonstrated worse driving performance (greater lane deviations) and more conservative control strategies (more speed reductions (56%) and greater maximum deceleration) as compared to middleaged (44% speed reductions) and young drivers (31% speed reductions). These findings are in-line with prior studies revealing that elderly drivers exhibit poorer vehicle control in comparison to younger drivers (e.g. Hummer, 1989). 5. Conclusions The goal of this research was to investigate the effects of driver age, specific service sign content and format, as well as sign familiarity, on driver performance and attention allocation on freeway exit ramps. Results indicate that drivers are more accurate in identifying target businesses in signs when exposed to six-panel vs. nine-panel signs and when searching for familiar vs. unfamiliar targets. In addition, it was found that elderly drivers are generally less accurate in identifying target businesses in signs in comparison
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to middle-aged and young drivers. Regarding attention allocation measures, our results indicate that elderly drivers adopt more conservative behaviors (lower off-road fixation frequencies and shorter glances) and allocate more attention to driving tasks in comparison to middle-aged and young drivers. Regarding driving performance responses, results indicated that drivers adopt a more conservative vehicle control strategy (more speed reductions on ramps) when exposed to nine-panel signs as compared to six-panel signs. Finally, findings suggest that elderly drivers generally exhibit poorer driving performance (greater lane deviations) and more conservative control strategies (more speed reductions and greater maximum deceleration levels) as compared to middle-aged and young drivers. Previously, these observations were colloquial notions for which we now have some objective evidence. It is also important to note that despite a number of our literature-based hypotheses going unsupported by the simulator and eye-tracking data, the experiment sample size and the high numbers of data points on all responses ensured sufficient power of tests (b < 0.35) for all inferential statistical analyses. Therefore, sensitivity was sufficient for all reported tests. 5.1. Limitations One limitation of this study is that all roadway scenarios were presented under daylight conditions and moderate traffic density (~3e4 vehicles per lane per minute or Level of Service A conditions). Furthermore, due to limitations of the driving simulator software, drivers were always asked to take the second exit to the ramp and the simulation was terminated after they passed the crossing street at the interchange. Results may not be applicable to more complex roadway conditions or higher simulated traffic densities. Another limitation of the study is that drivers were fully committed to the driving task and there were no in-vehicle distractions. Our results may not be applicable to situations in which drivers are, for example, engaged in talking on a cell phone or to passengers in a vehicle. Finally, this study was conducted using a fixed-base driving simulator that did not convey motion or kinesthetic cues to drivers. This setup might have limited simulator realism. 5.2. Future work Future research should investigate more complex driving scenarios including adverse weather conditions and traffic congestion. Another future direction might be to assess the effect of on-road signage on driver performance and attention allocation while they are also exposed to in-vehicle distractions and in using a fullmotion simulator system, possibly generating different steering and braking responses. Acknowledgments Funding for this project was provided by NCDOT (Grant No. 2015e46). The authors would like to thank Ron King, Signing Program Engineer, and Kevin Lacy, State Traffic Engineer, for their contributions in formulating the research questions, planning the experiment and reviewing the driving simulation scenario. The opinions expressed are those of the authors and do not necessarily
reflect the views of the NCDOT.
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