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• Road ahead top: top portion (approximately 1/3) of the area of the forward roadway (center camera). • Right side of road bottom: bottom portion (approximately 2/3) of the area to the right of the forward roadway (right camera). • Right side of road top: top portion (approximately 1/3) of the area to the right of the forward roadway (right camera). • Left side of road bottom (LSR_B): bottom portion (approximately 2/3) of the area to the left of the forward roadway (left camera). • Left side of road bottom (LSR_T): top portion (approximately 1/3) of the area to the left of the forward roadway (left camera). • Inside vehicle: below the panoramic video scene (outside of the view of the cameras, but eye tracking is still possible). • Top: above the panoramic video scene (outside of the view of the cameras, but eye tracking is still possible). Dynamic ROIs These ROIs are created multiple times within a DCZ for stimuli that move relative to the driver: • Driving-related safety risk: vehicle which posed a potential safety risk to the driver, defined as a car that is/may turn into the driver's direction of travel at a non-signalized or non-stop-controlled intersection (e.g., a car making a U-tum, a car waiting to turn right, or a car waiting to turn left). These vehicles were actively turning or entering the roadway or appeared to be in a position to enter the roadway. • Target standard billboard: target standard billboard that defines the start and end of the DCZ. • Other standard billboard: standard billboard(s) located in the DCZ, other than the target standard billboard or the target digital billboard. • CEVMS: target digital billboard that defines the start and end of the DCZ. The software determines the gaze intersection for each 60 Hz frame and assigns it to an ROI. In subsequent analyses and discussion, gaze intersections are referred to as gazes. Since ROIs may overlap, the software allows for the specification of priority for each ROI such that the ROI with the highest priority gets the gaze vector intersection assigned to it. For example, an ROI for a CEVMS may also be in the static ROI for the road ahead. 24 The 60 Hz temporal resolution of the eye tracking software does not provide suffjcient information to make detailed analysis of saccade characteristics,^ such as latency or speed. The analysis software uses three parameters in the determination of a fixation: a fixation radius, fixation duration, and a time out. The determination begins with a single-gaze vector intersection. Any subsequent intersection within a specified radius will be considered part of a fixation if the minimum fixation duration criterion is met. The radius parameter used in this study was 2 degrees and the minimum duration was 100 ms. The 2-degree selection was based on the estimated accuracy of the eye tracking system, as recommended by Recarte and Nunes.^^^^ The 100 ms minimum duration is consistent with many other published studies; however, some investigators use minimums of as little as 60 Because of mini-saccades and noise in the eye tracking system, it is possible to have brief excursions outside the 2 degree window for a fixation. In this study, an excursion time outside the 2-degree radius of less than 90 ms was ignored. Once the gaze intersection fell outside the 2-degree radius of a fixation for more than 90 ms, the process of identifying a fixation began anew. Other Measures Driving Behavior Measures During data collection, the front-seat researcher observed the driver's behavior and the driving environment. The researcher used the following subjective categories in observing the participant's driving behavior: • Driver Error: signified any error on behalf of the driver in which the researcher felt slightly uncomfortable, but not to a significant degree (e.g., driving on an exit ramp too quickly, turning too quickly). • Near Miss: signified any event in which the researcher felt uncomfortable due to driver response to external sources (e.g., slamming on brakes, swerving). A near miss is the extreme case of a driver error. • Incident: signified any event in the roadway which may have had a potential impact on the attention of the driver and/or the flow of traffic (e.g., crash, emergency vehicle, animal, construction, train). These observations were entered into a notebook computer linked to the research vehicle data collection system. Level ofService Estimates For each participant and each DCZ the analyst estimated the level of service of the road as they reviewed the scene camera video. One location per DCZ was selected (approximately halfway through the DCZ) where the number of vehicles in front of the research vehicle was counted. The procedure entailed (1) counting the number of travel lanes visible in the video, (2) using the ^ During visual scanning, the point of gaze alternates between brief pauses (ocular fixations) and rapid shifts (saccades). 25 skip lines on the road to estimate the approximate distance in front of the vehicle that constituted the analysis zone, and (3) counting the number of vehicles present within the analysis zone. Vehicle density was calculated with the formula: Vehicle Density = [(Number of Vehicles in Analysis Zone)/(Distance of Analysis Zone in ft/5280)]/Number of Lanes. Vehicle density is the number of vehicles per mile per lane. Vehicle Speed The speed of the research vehicle was recorded with GPS and a distance measurement instrument. Vehicle speed was used principally to ensure that the eye tracking data was recorded while the vehicle was in motion. RESULTS Results are presented with respect to the photometric measures of signs, the visual complexity of the DCZs, and the eye tracking measures. Photometric measurements were taken and analyzed to characterize the billboards in the study based on their luminance and contrasts, which are related to how bright the signs are perceived to be by drivers. Photometric Measurements Luminance The mean daytime luminance of both the standard billboards and CEVMS was greater than at night. Nighttime luminance measurements reflect the fact that CEVMS use illuminating LED components while standard billboards are often illuminated from below by metal halide lamps. At night, CEVMS have a greater average luminance than standard billboards. Table 3 presents summary statistics for luminance as a function of time of day for the CEVMS and standard billboards. Contrast The daytime and nighttime Weber contrast ratios for both types of billboards are shown in table 3. Both CEVMS and standard billboards had contrast ratios that were close to zero (the surroundings were about equal in brightness to the signs) during the daytime. On the other hand, at night the CEVMS and standard billboards had positive contrast ratios (the signs were brighter than the surrounding), with the CEVMS having higher contrast than the standard billboards. 26 Table 3. Summary of luminance (cdW) and contrast (Weber ratio) measurements Luminance (cd/ni') Contrast Day Mean St. Dev. Mean St. Dev. CEVMS 2126 798.81 -0.10 0.54 Standard Billboard 2993 2787.22 -0.27 0.84 Night CEVMS 56.00 23.16 73.72 56.92 Standard Billboard 17.80 17.11 36.01 30.93 Visual Complexity The DCZs were characterized by their overall visual complexity or clutter. For each DCZ, five pictures were taken from the driver's viewpoint at various locations within the DCZ. In Reading, the pictures were taken from 2:00 p.m. to 4:00 p.m. In Richmond, one route was photographed from 11:00 a.m. to noon and the other from 2:30 p.m. to 3:30 p.m. The pictures were taken at the start of the DCZ, quarter of the way through, half of the way through, three quarters of the way through, and at the end of the DCZ. The photographs were analyzed with MATLAB® routines that computed a measure of feature congestion for each image. Figure 10 shows the mean feature congestion measures for each of the DCZ environments. The arterial control condition was shown to have the highest level of clutter as measured by feature congestion. An analysis of variance was performed on the feature congestion measure to determine if the conditions differed significantly from each other. The four conditions with off-premise advertising did not differ significantly with respect to feature congestion; F(3,36) = 1.25,/' > 0.05. Based on the feature congestion measure, the results indicate that the four conditions with off-premise advertising were equated with respect to the overall visual complexity of the driving scenes. Arterial Highway Control CEVMS Adveitising Conditions Standard Figure 10. Mean feature congestion as a function of advertising condition and road type (standard errors for the mean are included in the graph). 27 Effects of Billboards on Gazes to the Road Ahead For each 60 Hz frame, a determination was made as to the direction of the gaze vector. Previous research has shown that gazes do not need to be separated into saccades and fixations before calculating such measures as percent of time or the probability of looking to the road ahead.^^^^ This analysis examines the degree to which drivers gaze toward the road ahead across the different advertising conditions as a function of road type and time of day. Gazing toward the road ahead is critical for driving, and so the analysis examines the degree to which gazes toward this area are affected by the independent variables (advertising type, type of road, and time of day) and their interactions. Generalized estimating equations (GEE) were used to analyze the probability of a participant gazing at driving-related information.^'^®''''^ The data for these analyses were not normally distributed and included repeated measures. The GEE model is appropriate for these types of data and analyses. Note that for all results included in this report, Wald statistics were the chosen alternative to likelihood ratio statistics because GEE uses quasi-likelihood instead of maximum likelihood.^"^^^ For this analysis, road ahead included the following ROIs (as previously described and displayed in figure 9): road ahead, road ahead top, and driving-related risks. A logistic regression model for repeated measures was generated by using a binomial response distribution and Logit (i.e., log odds) link function. Only two possible outcomes are allowed when selecting a binomial response distribution. Thus, a variable (RoadAhead) was created to classify a participant's gaze behavior. If the participant gazed toward the road ahead, road ahead top, or driving-related risks, then the value of RoadAhead was set to one. If the participant gazed at any other object in the panoramic scene, then the value of RoadAhead was set to zero. Logistic regression typically models the probability of a success. In the current analysis, a success would be a gaze to road ahead information (RoadAhead = 1) and a failure would be a gaze toward non- road ahead information (RoadAhead = 0). The resultant value was the probability of a participant gazing at road-ahead information. Time of day (day or night), road type (freeway or arterial), advertising condition (CEVMS, standard billboard, or control), and all corresponding second-order interactions were explanatory variables in the logistic regression model. The interaction of advertising condition by road type was statistically significant, (2) = 6.3, j? = 0.043. Table 4 shows the corresponding probabilities for gazing at the road ahead as a function of advertising condition and road type. Table 4. The probability of gazing at the road ahead as a function of advertising condition and road type. - Advertising Condition Arterial Freeway Control 0.92 0.86 CEVMS 0.82 0.73 II Standard 0.80 0.77 Follow-up analyses for the interaction used Tukey-Kramer adjustments with an alpha level of 0.05. The arterial control condition had the greatest probability of looking at the road ahead (M = 0.92). This probability differed significantly fi-om the remaining five probabilities. On 28 arterials, the probability of gazing at the road ahead did not differ between the CEVMS (M = 0.82) and the standard billboard (M = 0.80) DCZs. In contrast, there was a significant difference in this probability on freeways, where standard billboard DCZs yielded a higher probability (M = 0.77) than CEVMS DCZs (M = 0.73). The probability of gazing at the road ahead was also significantly higher in the freeway control DCZ (M = 0.86) than in either of the corresponding freeway off-premise advertising DCZs. The probability of gazing at road-ahead information in arterial CEVMS DCZs was not statistically different from the same probability in the freeway control DCZ. Additional descriptive statistics were computed to determine the probability of gazing at the various ROIs that were defined in the panoramic scene. Some of the ROIs depicted in figure 9 were combined in the following fashion for ease of analysis: • Road ahead, road ahead top, and driving-related risks combined to form road ahead. • Left side of road bottom and left side of road top combined to form left side ofvehicle. • Right side of road bottom and right side of road top combined to form right side of vehicle. • Inside vehicle and top combined to form participant vehicle. Table 5 presents the probability of gazing at the different ROIs. Table 5. Probability of gazing at ROIs for the three advertising conditions on arterials and freeways. Standard Road Type ROI CEVMS Billboard Control Arterial CEVMS 0.07 N/A N/A Lefi Side of Vehicle 0.06 0.06 0.02 Road ahead 0.82 0.80 0.92 Right Side of Vehicle 0.03 0.06 0.04 Standard Billboard N/A 0.03 N/A Participant Vehicle 0.03 0.05 0.02 Freeway CEVMS 0.05 N/A N/A Left Side of Vehicle 0.08 0.07 0.04 Road ahead 0.73 0.77 0.86 Right Side of Vehicle 0.09 0.02 0.05 Standard Billboard 0.02* 0.09 N/A Participant Vehicle 0.04 0.05 0.05 The probability of gazing away from the forward roadway ranged from 0.08 to 0.27. In particular, the probability of gazing toward a CEVMS was greater on arterials (M = 0.07) than on freeways (M = 0.05). In contrast, the probability of gazing toward a target standard billboard was greater on freeways (M = 0.09) than on arterials (M = 0.03). 29 Fixations to CEVMS and Standard Billboards About 2.4 percent of the fixations were to CEVMS. The mean fixation duration to a CEVMS was 388 ms and the maximum duration was 1,251 ms. Figure 11 shows the distribution of fixation durations to CEVMS during the day and night. In the daytime, the mean fixation duration to a CEVMS was 389 ms and at night it was 387 ms. Figure 12 shows the distribution of fixation durations to standard billboards. Approximately 2.4 percent of fixations were to standard billboards. The mean fixation duration to standard billboards was 341 ms during the daytime and 370 ms at night. The maximum fixation duration to standard billboards was 1,284 ms (which occurred at night). For comparison purposes, figure 13 shows the distribution of fixation durations to the road ahead (i.e., top and bottom road ahead ROIs) during the day and night. In the daytime, the mean fixation duration to the road ahead was 365 ms and at night it was 390 ms. Percentage Distribution of Fixation Duration CEVMS Fsatio-is 23- H a- 20- Day OE=L »S!lL 0--r—-r «3C0 1 \ 1 1 r TCO 1.100 1,500 1.900 500 SOQ 1.3CC 1,700 >2.000 Duration (ms] Figure 11. Distribution of fixation duration for CEVMS in the daytime and nighttime. 30 Percentage Distribution of Rxation Duration Standard BSIboard Rxations 60- 2 20- Pay N'iqht n 1 ' I : T" 1 1 WO 7C0 1 100 1,500 1-9O0 SOO 900 1,300 1.700 >2X0 Duration (ms) Figure 12. Distribution of fixation duration for standard billboards in the daytime and nighttime. Percentage ^stribubon of Fixation Ouratbn Road Ahead (Tcp and Bottom} Foalfons 60- 20- Dav rin o-J-4 □Or < 300 7C0 11C0 1,500 1.900 503 BOO 1,300 1.700 >2.300 Duration (ms) Figure 13. Distribution of fixation duration for road ahead (i.e., top and bottom road ahead ROIs) in the daytime and nighttime. 31 Dwell times on CEVMS and standard billboards were also examined. Dwell time is the duration of back-to-back fixations to the same The dwell times represent the cumulative time for the back-to-back fixations. Whereas there may be no long, single fixation to a billboard, there might still be multiple fixations that yield long dwell times. There were a total of 25 separate instances of multiple fixations to CEVMS wiA a mean of 2.4 fixations (minimum of 2 and maximum of 5). The 25 dwell times came from 15 different participants distributed across four different CEVMS. The mean duration of these dwell times was 994 ms (minimum of 418 ms and maximum of 1,467 ms). For standard billboards, there were a total of 17 separate dwell times with a mean of 3.47 sequential fixations (minimum of 2 fixations and maximum of 8 fixations). The 17 dwell times came from 11 different participants distributed across 4 different standard billboards. The mean duration of these multiple fixations was 1,172 ms (minimum of 418 ms and maximum of 3,319 ms). There were three dwell-time durations that were greater than 2,000 ms. These are described in more detail below. In some cases several dwell times came from the same participant. In order to compute a statistic on the difference between dwell times for CEVMS and standard billboards, average dwell times were computed per participant for the CEVMS and standard billboard conditions. These average values were used in a t-test assuming unequal variances. The difference in average dwell time between CEVMS (M = 981 ms) and standard billboards (M= 1,386 ms) was not statistically significant, r( 12) = -1.40, j? > .05. Figure 14 through figure 23 show heat maps for the dwell-time durations to the standard billboards that were greater than 2,000 ms. These heat maps are snapshots from the DCZ and attempt to convey in two dimensions the pattern of gazes that took place in a three dimensional world. The heat maps are set to look back approximately one to two seconds and integrate over time where the participant was gazing in the scene camera video. The green color in the heat map indicates the concentration of gaze over the past one to two seconds. The blue line indicates the gaze trail over the past one to two seconds. Figure 14 through figure 16 are for a DCZ on an arterial at night. The standard billboard was on the right side of the road (indicated by a pink rectangle). There were eight fixations to this billboard, and the single fixations were between 200 to 384 ms in duration. The dwell time for this billboard was 2,019 ms. At the start of the DCZ (see figure 14), the driver was directing his/her gaze to the forward roadway. Approaching the standard billboard, the driver began to fixate on the billboard. However, the billboard was still relatively close to the road ahead ROI. 32 Figure 14. Heat map for the start of a DCZ for a standard billboard at night on an arterial. Figure 15. Heat map for the middle of a DCZ for a standard billboard at night on an arterial. % Figure 16. Heat map near the end of a DCZ for a standard billboard at night on an arterial. Figure 17 through figure 19 are for a DCZ on a freeway at night. The standard billboard was on the right side of the road (indicated by a green rectangle). There were six consecutive fixations to this billboard, and the single fixations were between 200 and 801 ms in duration. The dwell time for this billboard was 2,753 ms. At the start of the DCZ (see figure 17), the driver was directing his/her gaze to a freeway guide sign in the road ahead and the standard billboard was to the left of the freeway guide sign. As the driver approached the standard billboard, his/her gaze was directed toward the billboard. The billboard was relatively close to the top and bottom road ahead ROIs. Near the end of the DCZ (see figure 19), the billboard was accurately portrayed as being on the right side of the road. 33 Figure 17. Heat map for start of a DCZ for a standard billboard at night on a freeway. Figure 18. Heat map for middle of a DCZ for a standard billboard at night on a freeway. Figure 19. Heat map near the end of a DCZ for a standard billboard at night on a freeway. Figure 20 through figure 23 are for a DCZ on a freeway during the day. The standard billboard was on the right side of the road (indicated by a pink rectangle). This is the same DCZ that was discussed in figure 17 through figure 19. There were six consecutive fixations to this billboard, and the single fixations were between 217 and 767 ms in duration. The dwell time for this billboard was 3,319 ms. At the start of the DCZ (see figure 20), the driver was principally directing his/her gaze to the road ahead. Figure 21 and figure 22 show the location along the DCZ where gaze was directed toward the standard billboard. The billboard was relatively close to the top and bottom road-ahead ROIs. As the driver passed the standard billboard, his/her gaze returned to the road ahead (see figure 23). 34 Figure 20. Heat map for the start of a DCZ for a standard billboard in the daytime on a freeway. Figure 21. Heat map near the middle of a DCZ for a standard billboard in the daytime on a freeway. Figure 22. Heat map near the end of DCZ for standard billboard in the daytime on a freeway. Figure 23. Heat map at the end of DCZ for standard billboard in the daytime on a freeway. 35 Comparison of Gazes to CEVMS and Standard Billboards The GEE were used to analyze whether a participant gazed more toward CEVMS than toward standard billboards, given that the participant was gazing at off-premise advertising. With this analysis method, a logistic regression model for repeated measures was generated by using a binomial response distribution and Logit link function. First, the data was partitioned to include only those instances when a participant was gazing toward off-premise advertising (either to a CEVMS or to a standard billboard); all other gaze behavior was excluded from the input data set. Only two possible outcomes are allowed when selecting a binomial response distribution. Thus, a variable (SBB_CEVMS) was created to classify a participant's gaze behavior. If the participant gazed toward a CEVMS, the value of SBB_CEVMS was set to one. If the participant gazed toward a standard billboard, then the value of SBB_CEVMS was set to zero. Logistic regression typically models the probability of a success. In the current analysis, a success would be a gaze to a CEVMS (SBB_CEVMS = 1) and a failure would be a gaze to a standard billboard (SBB_CEVMS = 0).^ A success probability greater than 0.5 indicates there were more successes than failures in the sample. Therefore, if the sample probability of the response variable (i.e., SBB_CEVMS) was greater than 0.5, this would show that participants gazed more toward CEVMS than toward standard billboards when the participants gazed at off- premise advertising. In contrast, if the sample probability of the response variable was less than 0.5, then participants showed a preference to gaze more toward standard billboards than toward CEVMS v/hen directing gazes to off-premise advertising. Time of day (i.e., day or night), road type (i.e., freeway or arterial), and the corresponding interaction were explanatory variables in the logistic regression model. Road type was the only predictor to have a significant effect, x^(l) = 13.17, p < 0.001. On arterials, participants gazed more toward CEVMS than toward standard billboards (M = 0.63). In contrast, participants gazed more toward standard billboards than toward CEVMS when driving on freeways (M = 0.33). Observation of Driver Behavior No near misses or driver errors were observed in Reading. Level of Service The mean vehicle densities were converted to level of service as shown in table As expected, less congestion occurred at night than in the day. In general, there was traffic during the data collection runs. Review of the scene camera data verified that all eye tracking data within the DCZs were recorded while the vehicle was in motion. Success and failure are not used to reflect the merits of either type of sign, but only for statistical purposes. 36 Table 6. Level of service as a function of advertising type, road type, and time of day. Arterial Freeway Day Night Day Night Control B A C B CEVMS C A B A Standard A A B A DISCUSSION OF READING RESULTS Overall the probability of gazing at the road ahead was high and similar in magnitude to what has been found in other field studies addressing billboards/^^'^'^^^ For the DCZs on freeways, CEVMS showed a lower proportion of gazes to the road ahead than the standard billboard condition, and both off-premise advertising conditions had lower probability of gazes to the road ahead than the control. On the other hand, on the arterials, the CEVMS and standard billboard conditions did not differ from each other but were significantly different from their respective control condition. Though the CEVMS condition on the freeway had the lowest proportion of gazes to the road ahead, in this condition there was a lower proportion of gazes to CEVMS as compared to the arterials (see table 5 for the trade-off of gazes to the different ROIs). A greater proportion of gazes to other ROIs (left side of the road, right side of the road, and participant vehicle) contributed to the decrease in proportion of gazes to the road aliead. Also, for the CEVMS on freeways, there were a few gazes to a standard billboard located in the same DCZ and there were more gazes distributed to the left and right side of the road than in standard billboard and control conditions. The gazes to ROIs other than CEVMS contributed to the lower probability of gazes to the road ahead in this condition. The control condition on the arterial had buildings along the sides of the road and generally presented a visually cluttered area. As was presented earlier, the feature congestion measure computed on a series of photographs from each DCZ showed a significantly higher feature congestion score for the control condition on arterials as compared to all of the other DCZs. Nevertheless, the highest probability for gazing at the road ahead was seen in the control condition on the arterial. The area with the highest feature congestion, especially on the sides of the road, had the highest probability for drivers looking at the road ahead. Bottom-up or stimulus driven measures of salience or visual clutter have been useful in predicting visual search and the effects of visual salience in laboratory tasks.^^'^''^^^ These measures of salience basically consider the stimulus characteristics (e.g., size, color, brightness) independent of the requirements of the task or plans that an individual may have. Models of visual salience may predict that buildings and other prominent features on the side of the road may be visually salient objects and thus would attract a driver's attention.^"^^^ Figure 24 shows an example of a roadway photograph that was analyzed with the Salience Toolbox based on the Itti et al. implementation of a saliency based model of bottom-up attention.^'^^''^^^ The numbered circles in figure 24 are the first through fifth salient areas selected by the software. Based on this software, the most salient areas in the photographs are the buildings on the sides of the road where the road ahead (and a car) is the fifth selected salient area. 37 Figure 24. Example of identified salient areas in a road scene based on bottom-up analysis. It appears that in the present study participants principally kept their eyes on the road even in the presence of visual clutter on the sides of the road, which simports the hypothesis that drivers tend to look toward information relevant to the task at hand/^^'~ In the case of the driving task, visual clutter may be more of an issue with respect to crowding that may affect the driver's ability to detect visual information in the periphery/'^' Crowding is generally defined as the negative effect of nearby objects or features on visual discrimination of a target/^^^ Crowding impairs the ability to recognize objects in clutter and principally affects perception in peripheral vision. However, crowing effects were not analyzed in the present study. Stimulus salience, clutter, and the nature of the task at hand interact in visual perception. For tasks such as driving, the task demands tend to outweigh stimulus salience when it comes to gaze control. Clutter may be more of an issue with the detection and recognition of objects in peripheral vision (e.g., detecting a sign on the side of the road) that are surrounded by other stimuli that result in a crowding effect. The mean fixation durations to CEVMS, standard billboards, and the road ahead were found to be very similar. Also, there were no long fixations (greater than 2,000 ms) to CEVMS or standard billboards. The examination of multiple sequential fixations to CEVMS yielded average dwell times that were less than 1,000 ms. However, when examining the tails of the distribution, there were three dwell times to standard billboards that were in excess of 2,000 ms (the three dwell times came from three different participants to two different billboards). These three standard billboards were dwelled upon when they were near the road ahead area but drivers quit gazing at the signs as they neared them and the signs were no longer near the forward field of view. Though there were three dwell times for standard billboards greater than 2,000 ms, the difference in average dwell times for CEVMS and standard billboards was not significant. Using a gaze duration of 2,000 ms away from the road ahead as a criterion indicative of increased risk has been developed principally as it relates to looking inside the vehicle to in- vehicle information systems and other devices (e.g., for texting) where the driver is indeed looking completely away from the road ahead.^'"^'^^'^'^^ The fixations to the standard billboards in the present case showed a long dwell time for a billboard. However, unlike gazing or fixating inside the vehicle, the driver's gaze was within the forward roadway where peripheral vision could be used to monitor for hazards and for vehicle control. Peripheral vision has been shown to be important for lane keeping, visual search orienting, and monitoring of surrounding objects.*^^'^^^ 38 The results showed that drivers were more likely to gaze at CEVMS on arterials and at standard billboards on freeways. Though every attempt was made to select CEVMS and standard billboard DCZs that were equated on important parameters (e.g., which side of the road the sign was located on, type of road, level of visual clutter), the CEVMS DCZs on freeways had a greater setback from the road (133 ft for both CEVMS) than the standard billboards (10 and 35 ft). Signs with greater setback from the road would in a sense move out of the forward view (road ahead) more quickly than signs that are closer to the road. The CEVMS and standard billboards on the arterials were more closely matched with respect to setback firom the road (12 and 43 ft for CEVMS and 20 and 40 ft for standard billboards). The differences in setback from the road for CEVMS and standard billboards may also account for differences in dwell times to these two types of billboards. However, on arterials where the CEVMS and standard billboards were more closely matched there was only one long dwell time (greater than 2,000 ms) and it was to a standard billboard at night. 39 RICHMOND The objectives of the second study were the same as those in the first study, and the design of the Richmond data collection effort was very similar to that employed in Reading. This study was conducted to replicate as closely as possible the design of Reading in a different driving environment. The independent variables included the type of DCZ (CEVMS, standard billboard, or no off-premise advertising), time of day (day or night) and road type (freeway or arterial). As with Reading, the time of day was a between-subjects variable and the other variables were within subjects. METHOD Selection of DCZ Limits Selection of the DCZ limits procedure was the same as that employed in Reading. Advertising Type Three DCZ types (similar to those used in Reading) were used in Richmond: • CEVMS. DCZs contained one target CEVMS. • Standard billboard. DCZs contained one target standard billboard. • Control conditions. DCZs did not contain any off-premise advertising. There were an equal number of CEVMS and standard billboard DCZs on freeways and arterials. Also, there two DCZ that did not contain off-premise advertising with one located on a freeway and the other on an arterial. Table 7 is an inventory of the target employed in this second study. Table 7. Inventory of tai^et billboards in Richmond with relevant parameters. DCZ Advertising Type Copy Dimensions (ft) Side of Road Setback from Road (ft) Other Standard Billboards Approach Length (ft) Roadway Type 5 CONTROL N/A N/A N/A N/A 710 Arterial 3 CONTROL N/A N/A N/A N/A 845 Freeway 9 CEVMS 14'0"x28'0" L 37 0 696 Arterial 13 CEVMS 14'0"x28'0" R 37 0 602 Arterial 2 CEVMS 12'5"x40'0" R 91 0 297 Freeway 8 CEVMS ir0x23'0" L 71 0 321 Freeway 10 Standard 14'0"x48'0" L 79 1 857 Arterial 12 Standard 10'6"x45'3" R 79 2 651 Arterial Standard 14'0"x48'0" L 87 0 997 Freeway 7 Standard 14'0"x48'0" R 88 0 816 Freeway N/A indicates that there were no ojf-premise advertising in these areas and these values are undefined 40 Figure 25 through figure 30 below represent various pairings of DCZ type and road type. Target off-premise billboards are indicated by red rectangles. Figure 25. Example of a CEVMS DCZ on a freeway. Figure 26. Example of CEVMS DCZ an arterial. Figure 27. Example of a standard billboard DCZ on a freeway. 41 Figure 28. Example of a standard billboard DCZ on an arterial. Figure 29. Example of a control DCZ on a freeway. ll Figure 30. Example of a control DCZ on an arterial. Photometric Measurement of Signs The methods and procedures for the photometric measures were the same as for Reading. Visual Complexity The methods and procedures for visual complexity measurement were the same as for Reading. 42 Participants A total of 41 participants were recruited for the study. Of these, 6 participants did not complete data collection because of an inability to properly calibrate with the eye tracking system, and 11 were excluded because of equipment failures. A total of 24 participants (13 male, M = 28 years; 11 female, M = 25 years) successfully completed the drive. Fourteen people participated during the day and 10 participated at night. Procedures Research participants were recruited locally by means of visits to public libraries, student unions, community centers, etc. A large number of the participants were recruited from a nearby university, resulting in a lower mean participant age than in Reading. Participant Testing Two people participated each day. One person participated during the day beginning at approximately 12:45 p.m. The second participated at night beginning at around 7:00 p.m. Data collection ran from November 20,2009, through April 23,2010. There were several long gaps in the data collection schedule due to holidays and inclement weather. Pre-Data Collection Activities This was the same as in Reading. Practice Drive Except for location, this was the same as in Reading. Data Collection The procedure was much the same as in Reading. On average, each test route required approximately 30 to 35 minutes to complete. As in Reading, the routes included a variety of freeway and arterial driving segments. One route was 15 miles long and contained two target CEVMS, two target standard billboards, and two DCZs with no off-premise advertising. The second route was 20 miles long and had two target CEVMS and two target standard billboards. The data collection drives in this second study were longer than those in Reading. The eye tracking system had problems dealing with the large files that resulted. To mitigate this technical difficulty, participants were asked to pull over in a safe location during the middle of each data collection drive so that new data files could be initiated. Upon completion of the data collection, the participant was instructed to return to the designated meeting location for debriefing. Debriefing This was the same as in Reading. 43 DATA REDUCTION Eye Tracking Measures The approach and procedures were the same as used in Reading. Other Measures The approach and procedures were the same as used in Reading. RESULTS Photometric Measurement of Signs The photometric measurements were performed using the same equipment and procedures that were employed in Reading with a few minor changes. Photometric measurements were taken during the day and at night. Measurements of the standard billboards were taken at an average distance of 284 ft, with maximum and minimum distances of 570 ft and 43 ft, respectively. The average distance of measurements for the CEVMS was 479 ft, with maximum and minimum distances of 972 ft and 220 ft, respectively. Again, the distances employed were significantly affected by the requirement to find a safe location on the road from which to take the measurements. Luminance The mean luminance of CEVMS and standard billboards, during daytime and nighttime are shown below in table 8. The results here are similar to those for Reading. Contrast The daytime and nighttime Weber contrast ratios for both types of billboards are shown in table 8. During the day, the contrast ratios of both CEVMS and standard billboards were close to zero (the surroundings were about equal in brightness to the signs). At night, the CEVMS and standard billboards had positive contrast ratios. Similar to Reading, the CEVMS showed a higher contrast ratio than the standard billboards at night. Table 8. Summary of luminance (cd/m^) and contrast (Weber ratio) measurements. Luminance (cd/m^) Contrast Day Mean St. Dev. Mean St. Dev. CEVMS 2134 798.70 -0.20 0.53 Standard Billboard 3063 2730.92 0.03 0.32 Night CEVMS 56.44 16.61 69.70 59.18 Standard Billboard 8.00 5.10 6.56 3.99 44 Visual Complexity As with Reading, the feature congestion measure was used to estimate the level of visual complexity/clutter in the DCZs. The analysis procedures were the same as for Reading. Figure 31 shows the mean feature congestion measures for each of the advertising types (standard errors are included in the figure). Unlike the results for Reading, the selected off- premise advertising DCZs for Richmond differed in terms of mean feature congestion; F(3, 36) = 3.95,p = 0.016. Follow up t-tests with an alpha of 0.05 showed that the CEYMS DCZs on arterials had significantly lower feature congestion than all of the other off-premise advertising conditions. None of the remaining DCZs with off-premise advertising differed from each other. The selection of DCZs for the conditions with off-premise advertising took into account the type of road, the side of the road the target billboard was placed, and the perceived level of visual clutter. Based on the feature congestion measure, these results indicated that the conditions with off-premise advertising were not equated with respect to level of visual clutter. Artenal c Highway w 3.00 Control CEVMS Standard Advertising Condition Figure 31. Mean feature congestion as a function of advertising condition and road type. Effects of Billboards on Gazes to the Road Ahead As was done for the data from Reading, GEE were used to analyze the probability of a participant gazing at the road ahead. A logistic regression model for repeated measures was generated by using a binomial response distribution and Logit link function. The resultant value was the probability of a participant gazing at the road ahead (as previously defined). Time of day (day or night), road type (freeway or arterial), advertising type (CEVMS, standard billboard, or control), and all corresponding second-order interactions were explanatory variables in the logistic regression model. The interaction of advertising type by road type was statistically significant, (2) = 14.19,;7 < 0.001. Table 9 shows the corresponding probability of gazing at the road ahead as a function of advertising condition and road type. 45 Table 9. The probability of gazing at the road ahead as a function of advertising condition and road type. Advertising Condition Arterial Freeway Control 0.78 0.92 CEVMS 0.76 0.82 Standard 0.81 0.85 Follow-up analyses for the interaction used Tukey-Kramer adjustments with an alpha level of 0.05. The freeway control had the greatest probability of gazing at the road ahead (M = 0.92). This probability differed significantly from the remaining five probabilities. On arterials, there were no significant differences among the probabilities of gazing at the road ahead among the three advertising conditions. On fireeways, there was no significant difference between the probability associated with CEVMS DCZs and the probability associated with standard billboard DCZs. Additional descriptive statistics were computed for the three advertising types to determine the probability of gazing at the ROIs that were defined in the panoramic scene. As was done with the data from Reading, some of the ROIs were combined for ease of analysis. Table 10 presents the probability of gazing at the different ROIs. Table 10. Probability of gazing at ROIs for the three advertising conditions on arterials and freeways. Standard Road Type ROI CEVMS Billboard Control Arterial CEVMS 0.06 N/A N/A Left Side of Vehicle 0.03 0.05 0.04 Road ahead 0.76 0.81 0.78 Right Side of Vehicle 0.07 0.06 0.09 Standard Billboard N/A 0.02 N/A Participant Vehicle 0.07 0.06 0.09 Freeway CEVMS 0.05 N/A N/A Left Side of Vehicle 0.03 0.01 0.01 Road ahead 0.82 0.85 0.92 Right Side of Vehicle 0.04 0.04 0.03 Standard Billboard N/A 0.04 N/A Participant Vehicle 0.06 0.06 0.05 The probability of gazing away from the forward roadway ranged from 0.08 to 0.24. In particular, the probability of gazing toward a CEVMS was slightly greater on arterials (M = 0.06) than on freeways (M = 0.05). In contrast, the probability of gazing toward a standard billboard was greater on freeways (M = 0.04) than on arterials (M = 0.02). In both situations, the probability of gazing at the road ahead was greatest on freeways. 46 Fixations to CEVMS and Standard Billboards About 2.5 percent of the fixations were to CEVMS. The mean fixation duration to a CEVMS was 371 ms and the maximum fixation duration was 1,335 ms. Figure 32 shows the distribution of fixation durations to CEVMS during the day and at night. In the daytime, the mean fixation duration to a CEVMS was 440 ms and at night it was 333 ms. Approximately 1.5 percent of the fixations were to standard billboards. The mean fixation duration to standard billboards was 318 ms and the maximum fixation duration was 801 ms. Figure 33 shows the distribution of fixation durations for standard billboards. The mean fixation duration to a standard billboard was 313 ms and 325 ms during the day and night, respectively. For comparison purposes, figure 34 shows the distribution of fixation durations to the road ahead during the day and night. In the daytime, the mean fixation duration to the road ahead was 378 ms and at night it was 358 ms. Percentaga Distribution of Fixation Duration CCVUS Fixations 60- S 0- 20- Oav □ JQ Wwht n < 300 7C0 500 110C 1.500 1.SC3 1.3£H3 1,700 >2.(a3 Duration (ms) Figure 32. Fixation duration for CEVMS in the day and at night. 47 Percentage Distribution of Fixation Duration Standard Billboards Fixations g 20- g 60- £ 20- _Oai_ r\r-n Ntght I ","U .n, I I i I I ?00 "CO ' ■'CC 1.50O 1.5»W 500 eoc t.300 1,700 >2X0 Dj'atcn ins; Figure 33. Fixation duration for standard billboards in the day and at night. Percentage Distribution of Rxation Dura&n Road Ahead (Top and Bctfon) Facalions 20- 20- rxiy Ltic N'iohl iHr •n——' I ' I 1 1 j 1 SCO 7C0 MM 1,€0O 1.900 500 SCO 1,300 -.700 >zooa DJiBtion (ms) Figure 34. Fixation duration for the road ahead in the day and at night. 48 As was done with the data for Reading, the record of fixations was examined to determine dwell times to CEVMS and standard billboards. There were a total of 21 separate dwell times to CEVMS with a mean of 2.86 sequential fixations (minimum of 2 fixations and maximum of 6 fixations). The 21 dwell times came from 12 different participants and four different CEVMS. The mean dwell time duration to the CEVMS was 1,039 ms (minimum of 500 ms and maximum of 2,720 ms). There was one dwell time greater than 2,000 ms to CEVMS. To the standard billboards there were 13 separate dwell times with a mean of 2.31 sequential fixations (minimum of 2 fixations and maximum of 3 fixations). The 13 dwell times came from 11 different participants and four different standard billboards. The mean dwell time duration to the standard billboards was 687 ms (minimum of 450 ms and maximum of 1,152 ms). There were no dwell times greater than 2,000 ms to standard billboards. In some cases several dwell times came from the same participant. To compute a statistic on the difference between dwell times for CEVMS and standard billboards, average dwell times were computed per participant for the CEVMS and standard billboard conditions. These average values were used in a Mest assuming unequal variances. The difference in average dwell time between CEVMS (M = 1,096 ms) and standard billboards (M= 674 ms) was statistically significant, r(14) - 2.23,/? = .043. Figure 35 through figure 37 show heat maps for the dwell-time durations to the CEVMS that were greater than 2,000 ms. The DCZ was on a freeway during the daytime. The CEVMS is located on the left side of the road (indicated by an orange rectangle). There were three fixations to this billboard, and the single fixations were between 651 ms and 1,335 ms. The dwell time for this billboard was 2,270 ms. Figure 35 shows the first fixation toward the CEVMS. There are no vehicles near the participant in his/her respective travel lane or adjacent lanes. In this situation, the billboard is relatively close to the road ahead ROI. Figure 36 shows a heat map later in the DCZ where the driver continues to look at the CEVMS. The heat map does not overlay the CEVMS in the picture since the heat map has integrated over time where the driver was gazing. The CEVMS has moved out of the area because of the vehicle moving down the road. However, visual inspection of the video and eye tracking statistics showed that the driver was fixating on the CEVMS. Figure 37 shows the end of the sequential fixations to the CEVMS. The driver returns to gaze directly in front of the vehicle. Once the CEVMS was out of the forward field of view, the driver quit looking at the billboard. Figure 35. Heat map for first fixation to CEVMS with long dwell time. 49 Figure 36. Heat map for later fixations to CEVMS with long dwell time. Figure 37. Heat map at end of fixations to CEVMS with long dwell time. Comparison of Gazes to CEVMS and Standard Billboards As was done for the data from Reading, GEE were used to analyze whether a participant gazed more toward CEVMS than toward standard billboards, given that the participant was looking at off-premise advertising. Recall that a sample probability greater than 0.5 indicated that participants gazed more toward CEVMS than standard billboards when the participants gazed at off-premise advertising. In contrast, if the sample probability was less than 0.5, participants showed a preference to gaze more toward standard billboards than CEVMS when directing visual attention to off-premise advertising. Time of day (i.e., day or night), road type (i.e., freeway or arterial), and the corresponding interaction were explanatory variables in the logistic regression model. Time of day had a significant effect on participant gazes toward off-premise advertising, (1) = 4.46, p = 0.035. Participants showed a preference to gaze more toward CEVMS than toward standard billboards during both times of day. During the day the preference was only slight (M = 0.52), but at night the preference was more pronounced (M = 0.71). Road type was also a significant predictor of where participants directed their gazes at off-premise advertising, x^ (0 ~ 3.96, p = 0.047. Participants gazed more toward CEVMS than toward standard billboards while driving on both types of roadways. However, driving on freeways yielded a slight preference for CEVMS over standard billboards (M = 0.55), but driving on arterials resulted in a larger preference in favor of CEVMS (M = 0.68). 50 Observation of Driver Behavior No near misses or driver errors occurred. Level of Service Table 11 shows the level of service as a function of advertising type, type of road, and time of day. As expected, there was less congestion during the nighttime runs than in the daytime. In general, there was traffic during the data collection runs; however, the eye tracking data were recorded while the vehicles were in motion. Table 11. Estimated level of service as a function of advertising condition, road type, and time of day. Arterial Freeway Day Night Day Night Control B A C B CEVMS B A B A Standard C A C C DISCUSSION OF RICHMOND RESULTS Overall the probability of looking at the forward roadway was high across all conditions and consistent with the fmdings from Reading and previous related research.^^^'^'^^^ In this second study the CEVMS and standard billboard conditions did not differ from each other. For the DCZs on arterials there were no significant differences among the control, CEVMS, and standard billboard conditions. On the other hand, while the CEVMS and standard billboard conditions on the freeways did not differ from each other, they were significantly different from their respective control conditions. The control condition on the freeway principally had trees along the sides of the road and the signs that were present were freeway signs located in the road ahead ROI. Measures such as feature congestion rated the three DCZs on freeways as not being statistically different from each other. These types of measures have been useful in predicting visual search and the effects of visual salience in laboratory tasks.^^'^^ Models of visual salience may predict that, at least during the daytime, trees on the side of the road may be visually salient objects that would attract a driver's attention.^"^^^ However, it appears that in the present study, participants principally kept their eyes on the road ahead. The mean fixations to CEVMS, standard billboards, and the road ahead were found to be similar in magnitude with no long fixations. Examination of dwell times showed that there was one long dwell time for a CEVMS greater than 2,000 ms and it occurred in the daytime on a sign located on the left side of the road on a freeway DCZ. Furthermore, when averaging among participants the mean dwell time for CEVMS was significantly longer than to standard billboards, but still under 2,000 ms. For the dwell time greater than 2,000 ms, examination of the scene camera video and eye tracking heat maps showed that the driver was initially looking toward the forward roadway and made a first fixation to the sign. Three fixations were made to the sign and then the 51 driver started looking back to the road ahead as the sign moved out of the forward field of view. On the video there were no vehicles near the subject driver's own lane or in adjacent lanes. Only the central 2 degrees of vision, foveal vision, provide resolution sharp enough for reading or recognizing fine detail.^^^^ However, useful information for reading can be extracted from parafoveal vision, which encompasses the central 10 degrees ofvision.^^^^ More recent research on scene gist recognition^ has shown that peripheral vision (beyond parafoveal vision) is more useful than central vision for recognizing the gist of a scene.^^®^ Scene gist recognition is a critically important early stage of scene perception, and influences more complex cognitive processes such as directing attention within a scene and facilitating object recognition, both of which are important in obtaining information while driving. The results of this study do show one duration of eyes off the forward roadway greater than 2,000 ms, the duration at which Klauer et al. observed near-crash/crash risk at more than twice those of normal, baseline driving.^^'*'"^ When looking at the tails of the fixation distributions, few fixations were greater than 1,000 ms, with the longest fixation being equal to 1,335 The one long dwell time on a CEVMS that was observed was a rare event in this study, and review of the video and eye tracking data suggests that the driver was effectively managing acquisition of visual information while driving and fixated on the advertising. However, additional work needs to be done to derive criteria for gazing or fixating away from the forward road view where the road scene is still visible in peripheral vision. The results showed that drivers are more likely to look at CEVMS than standard billboards during the nighttime across the conditions tested (at night the average probability of gazing at CEVMS was M= 0.71). CEVMS do have greater luminance than standard billboards at night and also have higher contrast. The CEVMS have the capability of being lit up so that they would appear as very bright signs to drivers (for example, up to aboutl0,000 cd/m^ for a white square on the sign.). However, our measurements of these signs showed an average luminance of about 56 cd/m^. These signs would be. conspicuous in a nighttime driving environment but significantly less so than other light sources such as vehicle headlights. Drivers were also more likely to look at CEVMS than standard billboards on both arterials and freeways, with a higher probability of gazes on arterials. In this second study, CEVMS and standard billboards were more nearly equated with respect to setback from the road. Gazes to the road ahead were not significantly different between CEVMS and standard billboard DCZs across conditions and the proportion of gazes to the road ahead were consistent with previous research. One long dwell time for a CEVMS was observed in this study; however, it occurred in the daytime where the luminance and contrast (affecting the perceived brightness) of these signs are similar to those for standard billboards. ^ "Scene gist recognition" refers to the element of human cognition that enables us to determine the meaning of a scene and categorize it by type (e.g., a beach, an office) almost immediately upon seeing it. 52 GENERAL DISCUSSION This study was conducted to investigate the effect of CEVMS on driver visual behavior in a roadway driving environment. An instrumented vehicle with an eye tracking system was used. Roads containing CEVMS, standard billboards, and control areas with no off-premise advertising were selected. The CEVMS and standard billboards were measured with respect to luminance, location, size, and other relevant variables to characterize these visual stimuli. Unlike previous studies on digital billboards, the present study examined CEVMS as deployed in two United States cities and did not contain dynamic video or other dynamic elements. The CEVMS changed content approximately every 8 to 10 seconds, consistent within the limits provided by FHWA guidance.^' In addition, the eye tracking system used had nearly a 2-degree level of resolution that provided significantly more accuracy in determining what objects the drivers were gazing or fixating on as compared to some previous field studies examining CEVMS. CONCLUSIONS Do CEVMS attract drivers' attention away from the forward roadway and other driving relevant stimuli? Overall, the probability of looking at the road ahead was high across all conditions. In Reading, the CEVMS condition had a lower proportion of gazes to the road ahead than the standard billboard condition on the freeways. Both of the off-premise advertising conditions had a lower proportion of gazes to the road ahead than the control condition on the freeway. The lower proportion of gazes to the road ahead can be attributed to the overall distribution of gazes away from the road ahead and not just to the CEVMS. On the other hand, for the arterials the CEVMS and standard billboard conditions did not differ from each other, but both had a lower proportion of gazes to the road ahead compared to the control. In Richmond there were no differences among the three advertising conditions on the arterials. However, for the freeways the CEVMS and standard billboard conditions did not differ from each other but had a lower proportion of gazes to the road ahead than the control. The control conditions differed across studies. In Reading, the control condition on arterials showed 92 percent for gazing at the road ahead while on the freeway it was 86 percent. On the other hand, in Richmond the control condition for arterials was 78 percent and for the freeway it was 92 percent. The control conditions on the freeway differed across the two studies. In Reading there were businesses off to the side of the road; whereas in Richmond the sides of the road were mostly covered with trees. The control conditions on the arterials also differed across cities in that both contained businesses and on-premise advertising; however, in Reading arterials had four lanes and in Richmond arterials had six lanes. The reason for these differences across cities was that these control conditions were selected to match the other conditions (CEVMS and standard billboards) that the drivers would experience in the two respective cities. Also, the selection of DCZs was obviously constrained by what was available on the ground in these cities. The results for the off-premise advertising conditions are consistent with Lee et al., who observed that 76 percent of drivers' time was spent looking at the road ahead in the CEVMS scenario and 75 percent in the standard billboard scenario.^^^ However, it should be kept in mind 53 that drivers did gaze away from the road ahead even when no off-premise advertising was present and that the presence of clutter or salient visual stimuli did not necessarily control where drivers gazed. Do glances to CEVMS occur that would suggest a decrease in safety? In DCZs containing CEVMS, about 2.5 percent of the fixations were to CEVMS (about 2.4 percent to standard billboards). The results for fixations are similar to those reported in other field data collection efforts that included advertising signs.^^^'^^'^'^^^ Fixations greater than 2,000 ms were not observed for CEVMS or standards billboards. However, an analysis of dwell times to CEVMS showed a mean dwell time of 994 ms (maximum of 1,467 ms) for Reading and a mean of 1,039 ms (maximum of 2,270 ms) for Richmond. Statistical comparisons of average dwell times between CEVMS and standard billboards were not significant in Reading; however, in Richmond the average dwell times to CEVMS were significantly longer than to standard billboards, though below 2,000 ms. There was one dwell time greater than 2,000 ms to a CEVMS across the two cities. On the other hand, for standard billboards there were three long dwell times in Reading; there were no long dwell times to these billboards in Richmond. Review of the video data for these four long dwell times showed that the signs were not far from the forward view when participants were fixating. Therefore, the drivers still had access to information about what was in front of them through peripheral vision. As the analyses of gazes to the road ahead showed, drivers distributed their gazes away from the road ahead even when there were no off-premise billboards present. Also, drivers gazed and fixated on off-premise signs even though they were generally irrelevant to the driving task. However, the results did not provide evidence indicating that CEVMS were associated with long glances away from the road that may reflect an increase in risk. When long dwell times occurred to CEVMS or standard billboards, the road ahead was still in the driver's field of view. Do drivers look at CEVMS more than at standard billboards? The drivers were generally more likely to gaze at CEVMS than at standard billboards. However, there was some variability between the two locations and between type of roadway (arterial or freeway). In Reading, the participants looked more often at CEVMS when on arterials, whereas they looked more often at standard billboards when on freeways. In Richmond, the drivers looked at CEVMS more than standard billboards no matter the type of road they were on, but as in Reading the preference for gazing at CEVMS was greater on arterials (68 percent on arterials and 55 percent on freeways). The slower speed on arterials and sign placement may present drivers with more opportunities to gaze at the signs. In Richmond, the results showed that drivers gazed more at CEVMS than standard billboards at night; however, for Reading no effect for time of day was found. CEVMS do have higher luminance and contrast than standard billboards at night. The results showed mean luminance of about 56 cd/m^ in the two cities where testing was conducted. These signs would appear clearly visible but not overly bright. 54 SUMMARY The results of these studies are consistent with a wealth of research that has been conducted on vision in natural environments/^^'^^'^'^ Inthe driving environment, gaze allocation is principally controlled by the requirements of the task. Consistent results were shown for the proportion of gazes to the road ahead for off-premise advertising conditions across the two cities. Average fixations were similar to CEVMS and standard billboards with no long single fixations evident for either condition. Across the two cities, four long dwell times were observed: one to a CEVMS on a freeway in the day, two to the same standard billboard on a freeway (once at night and once in the daytime), and one to a standard billboard on an arterial at night. Examination of the scene video and eye tracking data indicated that these long dwell times occurred when the billboards were close to the forward field of view where peripheral vision could still be used to gather visual information on the forward roadway. The present data suggest that the drivers in this study directed the majority of their visual attention to areas of the roadway that were relevant to the task at hand (i.e., the driving task). Furthermore, it is possible, and likely, that in the time that the drivers looked away from the forward roadway, they may have elected to glance at other objects in the surrounding environment (in the absence of billboards) that were not relevant to the driving task. When billboards were present, the drivers in this study sometimes looked at them, but not such that overall attention to the forward roadway decreased. LIMITATIONS OF THE RESEARCH In this study the participants drove a research vehicle with two experimenters on board. The participants were provided with audio tum-by-tum directions and consequently did not have a taxing navigation task to perform. The participants were instructed to drive as they normally would. However, the presence of researchers in the vehicle and the nature of the driving task do limit the degree to which one may generalize the current results to other driving situations. This is a general limitation of instrumented vehicle research. The two cities employed in the study appeared to follow common practices with respect to the content change frequency (every 8 to 10 seconds) and the brightness of the CEVMS. The current results would not generalize to situations where these guidelines are not being followed. Participant recruiting was done through libraries, community centers and at a university. This recruiting procedure resulted in a participant demographic distribution that may not be representative of the general driving population. The study employed a head-free eye tracking device to increase the realism of the driving situation (no head-mounted gear). However, the eye tracker had a sampling rate of 60 Hz, which made determining saccades problematic. The eye tracker and analyses software employed in this effort represents a significant improvement in technology over previous similar efforts in this area. The study focused on objects that were 1,000 feet or less from the drivers. This was dictated by the accuracy of the eye tracking system and the ability to resolve objects for data reduction. In addition, the geometry of the roadway precluded the consideration of objects at great distances. 55 The study was performed on actual roadways, and this limited the control of the visual scenes except via the route selection process. 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Journal of Vision, 9, 2009, 1-16. 60 EXHIBITS Title: Accession Number: Record Type: Language: Source Agency: Source Data: Abstract: Evaluating the Clearview Typeface System for Negative Contrast Signs 01489749 Project English Mid-Atlantic Universities Transportation Center 201 Transportation Research Building University Park, PA 16802-4710 USA RiP Project 35041 The development of Clearview typeface began in response to a Federal Highway Administration (FHWA) study that recommended a 20 percent increase in sign letter height to provide greater reading distances for aging drivers. The original Clearview studies showed that it was possible to obtain significant improvements in guide sign reading distances for older drivers without increasing letter height by using mixed-case Clearview typefaces in place of all-uppercase Standard Highway Alphabets. Furthermore, the positive contrast (i.e., lighter letters on darker background) mixed case Clearview typefaces were found to be significantly more legible than the mixed case Standard Highway Series E(M) in several independent studies. This body of research led to tlie 2004 interim approval of Clearview on positive contrast guide signs by the FHWA. Clearview was specifically designed to improve guide sign readability at night for older drivers when used with high brightness sign materials by reducing or eliminating the negative effects of halation and overglow. However, the Clearview Typeface System also includes negative contrast versions to be used on regulatory and warning signs. The difference between positive contrast versions of Clearview and negative contrast versions are limited to stroke width; with negative contrast being heavier to counter-balance the halation effect of the lighter background when viewed at a distance and with high brightness retroreflective materials. While the research discussed above led to the development of guidelines and approval for the use of Clearview in positive contrast, definitive studies have not been conducted for negative contrast applications. Without this research, Clearview's approval will remain restricted to positive contrast applications and full adoption will not take place. The objective of the proposed research is to compare the legibility distance of the negative contrast (i.e., darker letters on a lighter background) Clearview Typeface System with that of Standard Highway Alphabets on regulatory signs in the daytime and nighttime with older and younger motorists. The researchers will identify the legibility distances and evaluate the effects of letter spacing of sign TRIS Files: Contract Numbers: Funding: Start Date; Actual Completion Date: Performing Agencies: Funding Agencies: Index Terms: Subject Areas: legends using: mixed case Clearview (Clearview 2B, 3B, and 4B) and both mixed and all upper-case Standard Highway Alphabets (Series C, D, and E) on white signs with black legends. UTC, RIP DTRT12-G-UTC03 PSU-2013-02 35000.00 20130215 20140214 Mid-Atlantic Universities Transportation Center 201 Transportation Research Building University Park, PA 16802-4710 USA Research and Innovative Technology Administration University Transportation Centers Program Washington, DC 20590 USA Mid-Atlantic Universities Transportation Center 201 Transportation Research Building University Park, PA 16802-4710 USA Clearview font; Contrast; Legibility; Sign legend typefaces; Traffic signs; Visibility Construction; Design; Highways; Operations and Traffic Management; Safety and Human Factors Title: Accession Number: Record Type: Language: Record URL: Source Agency: Evaluation of Guide Sign Fonts 01481805 Project English http://www.DOoledfund.org/Details/Studv/490 Federal Highway Administration 1200 New Jersey Avenue, SE Washington, DC 20590 USA Source Data: Abstract: TRIS Files: Contract Numbers: Funding: Start Date: Funding Agencies: RiP Project 29842 The objective of this study is to conduct a field evaluation of new available highway fonts versus the Series E (Modified) and Clearview 5WR fonts for use on guide signs. This study will provide the analysis necessary for decision to be made on inclusion of fonts for older drivers in the Manual on Uniform Traffic Control Devices (MUTCD). In previous studies comparing Clearview fonts to previously used types, legibility distance, the distance at which a subject can read an unknown word, has been a robust measure of effectiveness. Researchers believe that in order to test the proposed font against the current standard, nighttime data collection in which participants drive an instrumented vehicle along a closed course while researcher's record data would be most conducive. RiP TPF-5(262) 195000.00 20120223 Colorado Department of Transportation 4201 East Arkansas Avenue Denver, CO 80222 USA Florida Department of Transportation Research Center 605 Suwannee Street MS-30 Tallahassee, PL 32399-0450 USA West Virginia Department of Transportation Division of Highways Building 5, Room A-110 Charleston, WV 25305-0430 USA Minnesota Department of Transportation Transportation Building 395 John Ireland Boulevard St Paul, MN 55155 USA Iowa Department of Transportation 800 Lincoln Way Ames, lA 50010 USA California Department of Transportation 1227 O Street Sacramento, CA 95843 USA Responsible Individuals: Index Terms: Subject Areas: Johnson, Cory J Clearview font; Legibility; Manual on Uniform Traffic Control Devices; Nighttime driving; Sight distance; Sign fonts Highways; Operations and Traffic Management Title: Accession Number: Record Type: Language: Source Agency: Source Data: Abstract: Clearview Font in Traffic Signs: Assessing IDOT Experiences and Needs 01480029 Project English Illinois Department of Transportation 2300 S. Dirksen Parkway Springfield, IL 62764 USA RiP Project 28631 The Clearview font has many advantages over previous font practices, such as night legibility and lack of smearing effect. Some of the potential concerns include incompatibility with commonly used background sheeting and sight distance. However, experience in other states to date, as well as experimental studies in the lietarture, suggest that the advantages appear to outweigh the concerns. The main objective of this study is to determine Illinois' progress in using the Clearview font and whether its use has proven successful. While most previous in the literature were mostly done in an experimental in setting, this study (R27-75) aims to determine how the implementation of the Clearview font for signs has affected Illinois and its motorists in a real-world context. Accordingly, the study team is examining the implementation of this font from the perspectives of both installers and motorists. This project will also monitor media reports about the Clearview font and/or this research project. The main field research portion of this study will include both intercept and follow-up surveys with motorists who have encountered Clearview signs. An important aspect of this project will be developing questions that adequately query user perceptions. Some questions will be whether motorists notice a difference between Clearview signs and other signs and how eligible Clearview signs are to them. This will be done for illuminated TRIS Files: Contract Numbers: Funding: Start Date: Actual Completion Date: Performing Agencies: Funding Agencies: Index Terms: Subject Areas: and non-illuminated locations in order to compare motorist reactions. The final task is the final report, which is accompanied by an interim report in April followed by the final report a year later. There will also be quarterly progress reports along with meetings of the panel every four months. RiP R27-75 230000.00 20100515 20120331 Northwestern University, Evanston Transportation Center, 600 Foster Sti'eet Evanston, IL 60208-4055 USA Illinois Department of Transportation 2300 S. Dirksen Parkway Springfield, IL 62764 USA Clearview font; Illinois; Night visibility; Sight distance; Sign sheeting; Traffic signs Data and Information Technology; Highways; Operations and Traffic Management Title: Accession Number: Record Type: Language: Record URL: Source Agency: Effectiveness of Larger Traffic Signs, High-Performance Sheeting and Clearview Font on Accident Reduction 01480047 Project English http://transDort.ksu.edu/researcli/KSUTC-08-6.Ddf Kansas State University's Center for Transportation Research 2118 Friedier Hall Manhattan, KS 66506-5000 USA Source Data: RiP Project 20990 Abstract: TRIS Files: Contract Numbers: During the last several decades, the number of drivers and rural/urban traffic has significantly increased, along with the number of traffic signs. Traffic signs provide a plethora of necessary information - directions, guidance, warnings, regulations, and recreation. With today's congestion and higher speed, it's very important to recognize specific situations where larger, brighter, and easier to read signs should be installed to increase safety of the drivers. It is equally important to counties with limited funds to fmd locations and/or scenarios where bigger, more costly signs are not cost effective. Many signing studies of problem locations recommend bigger, brighter, easier to read signs. However, there is little evidence that sign size, or readability directly relate to accident causation. Counties want evidence that larger, more costly signs are cost effective in regard to safety. A study of the other attributes can be made within this same project at little incremental cost with results made beneficial to both the state and counties. There is a need for documentation of the accident reduction benefits of these sign attributes for a range of approach scenarios and accident types. The main objective of this research will be to determine typical locations and/or scenarios where bigger signs are effective in reducing accidents on rural/urban roads and where they are not effective, thus saving money for use at more critical locations. Within this study, a secondary objective will be to also study the safety benefits of brighter, easier to read signs. UTC, RiP RE-040-05; HPD-R043 KSUTC-08-6 Funding: Start Date: 49659.00 20070228 Actual Completion Date: 20110630 Performing Agencies: Kansas State University's Center for Transportation Research 2118 FriedlerHall Manhattan, KS 66506-5000 USA Funding Agencies: Kansas Department of Transportation Eisenhower State Office Building 700 SW Harrison Street Topeka, KS 66603-3754 USA Responsible Individuab: Stokes, Robert W Index Terms: Overhead traffic signs; Research projects; Road and highway directions; Sign sheeting; Traffic control devices; Traffic signs; Visibility Subject Areas: Highways; Operations and Traffic Management; Research EXHIBIT? Industry's Traffic Safety Research The outdoor advertising industry's foundation (Foundation for Outdoor Advertising Research and Education) has pioneered research on digital billboards and traffic safety, commissioning top experts to study driver behavior and also to analyze crash data. An initial study, released in 2007, was based on "human factors" such as drivers' eye glances. Meanwhile, engineering experts have analyzed accident reports provided by state and local authorities in jurisdictions across the country. Human Factors Research • Virginia Tech Transportation Institute (2007) • This study used an instrumented vehicle to measure eye glances in the presence of off-premise digital billboards and conventional billboards (The pending FHWA study relies on eye-glance methodology) • Findings of industrv research: o Drivers did not glance more frequently in the direction of digital billboards than in the direction of other event types o The mean glance towards digital billboards was less than one second Accident Analysis • Tantala Associates, LLC o Cleveland, OH (2007 and updated in 2009) o Rochester, MN (2009) o Albuquerque, NM (2010) o Reading, PA (2010) o Richmond, VA (2010) I Each of these studies analyzed crash data before and after deployment of digital billboards. Research in Reading, PA and Richmond, VA, also used a contemporary AASHTO-approved method known as Bayes Analysis (with and without billboards) o Findings: I The accident data does not show a statistical relationship between vehicular accidents and billboards (conventional and digital billboards) I The number or rate of vehicular accidents didn't increase after the installation of off-premise digital billboards I The accident statistics near billboards are comparable to the accident statistics on similar sections of highway without billboards I Accidents occur with or without billboards (digital or conventional) Summary of Past Traffic Safety Research Studies • Other Studies Not on Point Dozens of studies have examined driver distraction, including the role of signs. However, the body of research cited by Jerry Wachtel is primarily comprised of reports not specifically focused on digital billboards, in fact in a companion research project, the FHWA's literature review of these studies has determined that these studies are "inconclusive." These studies look at: o On-premise signs On-premise digital signs can flash, feature full motion video, and scroll text under U.S. regulations. Roadside digital billboards operate under tougher regulations than on-premise signs. 1. Beijer (2002) (Canada) 2. Smiley (2005) (Canada) 3. Wisconsin DOT study of the Milwaukee Stadium sign (1994) (U.S.) o Simulators According to Jerry Wachtel, studies using simulators to examine the effects of digital signs are not reliable due to the inherent limitations of the simulator environment. 1. Finnish Road Administration (2004) (Finland) 2. Brunei University (2007) (England) 3. Young and Mahfoud (2007) (England) 4. Edquist (2009) (Australia) 5. Fisher (2009) (U.S.) o Literature reviews (No research completed) 1. Wachtel and Netherton (1980) (U.S.) 2. Farbry (2001) (U.S.) 3. CTC & Associates (2003) (U.S.) 4. SWOV Institute for Road Safety Research (2006) (Dutch) 5. SRF Consulting Group (2007) 6. FWHA (2009) (U.S.) • Driver Distraction/inattention Research by government and the insurance industry has identified many factors that distract drivers, as well as conditions related to accidents. In 2006, the National Highway Traffic Safety Administration released a comprehensive report known at the "100-Car Crash Study" which found that: o Drowsiness increases the risk of accidents 4-6 times o Distraction or inattention was estimated to cause more than 23% of all crashes and near crashes o Glances totaling more than 2 seconds increase near-crash/crash risk by at least two times (A typical glance at a digital billboard is less than one second) o Short, brief glances away from the roadway for the purpose of scanning the driving environment are safe and actually decrease near-crash/crash risk EXHIBITS LIGHTING SCIENCES Lighting Sciences Inc. 7826 East Evans Road Scotfsdale, Arizona 85260 U.S.A. Tel: 480-991-9260 Fax: 480-991-0375 www.liglitingsciences.com October 1,2008 Report to: Outdoor Advertising Association of America Subject: Digital Billboard Recommendations and Comparisons to Conventional Billboards Abstract This report summarizes several research projects undertaken by Lighting Sciences, Inc. (LSI) related to billboard lighting. The topics that have been addressed are: • Development of digital billboard luminance recommendations • A comparison of luminances of conventional billboards and digital billboards • "Sky Glow** lumens entering the night sky from conventional and digital billboards. i. Digital Billboard Luminance Recommendations Lighting Sciences, Inc., has undertaken research to develop a method for specification of luminance (brightness) limits for digital billboards based on accepted practice by the Tlluminating Engineering-SoGiety-of North AmeriGa-(IESNA). The recommendation is extremely- simple to implement and requires only a footcandle (fc) meter to be used. The research establishes criteria for billboard luminance limits based on biUboard-to-viewer distances for standardized billboard categories. For example, a standard billboard-to-viewer distance of 250 feet is used to establish the billboard luminance limits for a 14' x 48' foot (672 sq.ft.) bulletin. The recommended technique is based on accepted lESNA practice for "light trespass." Previous outdoor lighting research has documented an established limit on the amoimt of light arriving at a person's eyes to ensure that the source of the light is not offensive, or worse, potentially dangerous. The technique is simple: the light level at the eye is measured in footcandles and has an upper limit. The limit is low for areas that ai*e generally quite dark, but considerably higher in well lit urban ai'eas. A recommended specification for digital billboards is to use a limit of 0.3 fc over ambient light conditions. To check if the level is acceptable, a footcandle meter would be held at a height of 5 ft. (which is. approximately eye height) and faced towards the billboaid at the desired billboard- to-viewer distance. A reading of 0.3 fc or less above ambient light conditions would indicate compliance. The standards set forth in the report are based on the worst-case scenario of a driver or pedestrian -viewing-the-displa-y-head=on.-(directly-ata90idegree.angle),-whilein-practice.most-display-s_are— viewed at an angle. Since displays are generally viewed at an angle, the luminance (glare) is substantially reduced. Furthermore, the report provides values for billboard luminance of different color images and notes that luminance levels are based on a worst-case scenario of an all-white display, which is unlikely to happen, save for a malfunction. Knowing these values, and having established a billboard luminance limit for a particular billboard, the allowable percentage of dimming setting is also easily calculated. The investigations and this report do not cover factors related to changing images and billboard message movement. Issues that may be related to motorist attention are beyond the scope of the work and use of the proposals in this study should be based on that understanding. a. Comparison ofConventional and Digital Billboard Luminances A study by Rensselaer Polytechnic Institute Lighting Research Center has measured the luminance of typical conventional billboards and has developed the maximum value of luminance that can be expected. LSI has compared the recommendations developed in this report to the Rensselaer measured values.- The digital billboards will be brighter,-but only - — slightly brighter, than the maximum luminance of conventional billboards. Hi. Sky Glow Sky glow is caused by lighting at night entering the atmosphere and being scattered by airborne particulates. Sky glow may result from the use of lighting fixtures that emit Ught above a horizontal plane so that it enters the atmosphere directly. The effect also is caused by light reflecting from lighted objects, such as a road suiface, a building or a billboard. The study has evaluated the amount of light entering the atmosphere from a variety of lighting installations. Measured in "sky lumens," the results allow a comparison to be made of different lighting systems relative to slcy glow. Specifically calculations have been made to compare tlie sky lumens produced by conventional billboard lighting systems, both three and four luminarre bottom moimted systems lighting a standard 14 x 48fl. billboard, to the sky lumens caused by roadway and parking lot lighting. Various scenarios have been used for the roadway lighting, combining residential and major highway lighting in a typical neighborhood. Areas have been considered that consist only of roadway lighting, as well as areas that contain both roadway and parking lot lighting. -Xhoxesults-olthe study.support.a-conclusiQn-thatihe-V.astjnajoi'i1y-OfLsky_glowis..a.pi:nduct of urban development. Even where full cut-off fixtures are used on all roadway and parking lot lighting fixtures, and if there is an average of one billboard per square mile, over 96% of the sky glow produced per urban square mile is fi'om those sources and not billboard lighting, for tlie conditions examined. For ^e examples considered, a single three fixture billboard lighting system produces approximately 2 to 3% of the sky lumens caused by roadway/parking area lighting in the example one square mile area. For a four fixture billboai'd lighting system, the range becomes roughly 2.5 to 4%. These figures can be prorated. For example, if there are two such billboards per square mile, the percentages are doubled; if there is one such billboard per two square miles, the percentages will be halved. The exact percentages of sky glow are affected by the density of roadways/parking areas, the type of lighting fixtures used and the lighting level provided, among other factors. However, it is apparent that for the scenarios considered, the contribution of billboard lighting to sky glow is small in comparison to that firom other sources of lighting. The other sources produce 96 to 98% of sky lumens, compared to the 2 to 4% produced per billboard in the example urban square mile. Digital billboards operating at the luminance levels recommended in this report produce much fewer lumens into the night sky-than conventional bottom mounted lighting systemSi- This-is primarily due to the elimination of the external luminaires, but also is a result of the characteristics of the billboard pixel design whereby light in upward directions is reduced in comparison to light sent below the horizontal in the direction of viewers. Definitions Luminance. Also known as photometric brightness, this is the "brightness" of the billboard as seen fiom a particular angle of view. It is measured in candelas per sq. meter, also termed "nits.' Illuminance. This is the amount of light firom the billboard landing on a distant surface. It is measured in footcandles (fc) or lux. Intensity. This is the candlepower, or concentration, of light emitted in a given direction jfrom the entire billboard. Reflectance. This is a measm'e of the proportion, or percentage, of light falling on a surface that is reflected by the surface. SECTION A - DIGITAL BILLBOARD LUMINANCE RECOMMENDATIONS Al. Introduction This report has been prepared for OAAA under the contract issued to Lighting Sciences Inc. for the development of luminance (brightness) recommendations for digital billboards under nighttime conditions. Extensive investigations have been conducted into methodologies that could be used to develop such recommendations, specifically addressing environmental impact and possible visibility effects on drivers. The following approaches can be used: 1. Develop billboard recommendations based on the control of possible glare to which drivers may be subjected. or 2. Produce recommendations founded on environmental impact, addressing the subject known as light trespass. Either of these methods can be used as a viable approach to providing an acceptable practice for the control of digital billboard appearance, though the first method has disadvantages. In analyzing these methods,- strict-attention has-been-paid to satisfying the following-: - 1. The needs of the general pubUc, including drivers. 2. The requirements of local government personnel, who may wish to incorporate language into ordinances related to the use of digital billboards. For this, the procedures must be straight forward and enforceable. 3. The needs of OAAA members, who require effective use of digital billboards, which in turn requires adequate brightness for clear visibility. The two approaches are addressed below. A2. Method 1, Specifications Based on Driver Glare Drivers on roadways at night where vutually any form of lighting is provided are inevitably subjected to glare. Glare may be, for example, from oncoming headlights, street lights, or commercial lighting, including billboards. There ai*e recommended limits to the amount of glare that can be produced by vehicle headlights (from tlie U.S. Department of Transportation) and by roadway lighting (from the American National Standards Institute and tlie Illuminating Engineering Society of North America -lESNA.) In particular, the extensive procedures that have been developed by lESNA can, in theory, be used to produce limitations on digital -billboM^d-luminance-tliat-will-ensure-thatany-glare-problems-createdfoiLdri-vers-wilLbe-jelatively- minor, in the order of glai'e often produced by a street lighting installation. Lighting Sciences has conducted detailed investigations into this approach, based upon publication ANSI DESNA RP-8-00, "American National Standard Practice for Roadway Lighting." The basic procedures for such a method would be to specify an allowable average billboard luminance level that would ensure that the glare it produces does not exceed certain limits. These limits would be based on the level of highway lighting that is present. For example, higher billboard luminances would be allowed where a high level of street lighting is provided. Publication RP-8-00 classifies highways into many different types, and there is a set of recommendations for the lighting of each type. Thus using these principles for digital billboard specifications, there would be many different recommended billboard luminance limits, dependent upon the form of roadway lighting provided in the area. After much consideration. Lighting Sciences does not recommend this approach for establishing digital billboard luminance limits. The reasons include the following: 1. Publication RP-8-00 describes 14 different roadway classifications. These are based on different roadway types (for example, fireeways, major roadways, local roadways). There is a ■" further breakdovm basedmn the level ofpedestrian activity,-which may be high, medium or- low. Basing billboard luminances on this wide range would produce a complex system of specifications that would lack the simplicity and clarity that is our goal. 2. Digital billboards are firequently visible from numerous vantage points. This creates an issue of deciding which of the 14 different categories would be applicable if different levels of roadway lighting exist in a general ai'ea. 3. There is further complexity in determining the amount of glare produced by a digital billboard using the metliodology of publication RP-8-00. The amount of glare is affected not only by the luminance of a digital billboard, but by its distance from the driver. What distance would be selected to perform the necessary calculations when the driver might view tlie billboard fi:om a wide range of distances? 4. The amount of glare is affected also by tlie location of the billboard with respect to the driver's line of sight. Tliis changes as tlie driver looks in different dhections and as his location changes. What billboard position would be used? 5. The extent of any glare produced is dependent upon the billboard size. Recommended lumts of luminance, if based on glare control, would be different for each billboai'd size. Thus it can be seen that, because of all tlie variables involved, the establishing of realistic -billboard-luminanGe-iimitsbased-on-the-RR-8=00-methodology-wouldhe-exceedingly^complex^ Even if simplifications were introduced, there would be problems in deciding which luminance limit would be applicable to a given billboard. Checking and enforcement similarly would be highly problematic. For these reasons, Lighting Sciences Inc. has not developed and is not recommending a billboard luminance specification system based upon glare limitations. However, in conducting the detailed study of this metliod and the second method that follows below, it has been determined that if the method provided below is adopted, billboard luminances will be such that producing a significant amount of glare to drivers from a single digital billboard is unlikely, (although a multiplicity of such billboards appearing in the driver's field of view simultaneously may possibly create a problem.) Further evaluations of this topic are suggested using documents produced by other research organizations. A3. Method 2, Specification Based on Light Trespass A3.1 Method Overview "Light trespass" is a term used in the outdoor lighting industry to describe light that falls outside of theareathatis primarily intended to be lighted. "For example, if the lighting system for a shopping center parking lot causes light to spill over into an adjacent residential neighborhood, this would be considered to be light trespass. High levels of light trespass, as well as bemg wasteful of energy, may have an appearance that is objectionable. Publication TM-11-00 of the lESNA provides a table of limits of light trespass for various "lighting zones." These zones range from "no ambient electric light" (dark rural areas) to "high ambient electric light" (typically high use urban areas.) The limits are expressed in terms of the illuminance in footcandles that the light source in question can produce at a person's eyes, measured above the ambient lighting that is produced by all other sources of light. The limitation values were determined from an extensive human factors research project into the levels of light trespass that may or may not be considered objectionable in the various zones. Application of the limits keep light trespass to a low level that is unlikely to be considered objectionable to most persons. Digital billboards are not the form of lighting that TM-11-00 was developed to limit. In fact, digital billboai'ds ai*e specifically intended to be seen over a wide area, much of which may be remote from the billboard itself. Nevertheless, the principles of TM-l 1-00, in terms of the calculation meOiod and the limits it provides, can be examined to determine whether the methodology can form a useful metliod of specifying billboard luminance limits. Numerous calculations have been performed to evaluate billboard luminance in terms of the TM- 11 -00 procedures. The calculations involved are simpler than those discussed above for RP-8-00 procedures, as they simply involve determining the illuminance in footcandles (fc) at the location of the eyes of a viewer. (Referred to as "eye illuminance.") TM-11-00 provides four different _e.ye-illuminanGe-limits-depending-on-the-lightmg-zone,^i-to-E4,-ranging-fconi:y-e3yJo-W_ambient_ electric light to high ambient electric light. See table 1. (A description of each type of ambient electric light zone is included in Appendix B.) Table 1 Eye Illuminance Limits (Light Produced by Billboard, a>ove Ambient) Zone Eye Illuminance Limit (fc) El Very low ambient electric light 0.1 E2 Low ambient electric light 0.3 E3 Medium ambient electric light 0.8 E4 High ambient electric light 1.5 To simplify billboard luminance specifications, it is proposed that all billboard luminance limits, no matter where a billboard is located, are governed by the values given in the above table for zone E2. This will then produce a uniform method that does not require the lighting zone to be known. The logic for choosing zone E2 is based on two considerations. Firstly, it is highly unlikely that digital billboards will ever be used in areas described as zone El. El applies to inherently very dark rural areas where there is almost no electric lighting, such as national parks. Distal billboards'are likely to be used ih zones E2'through E4 ."By u^^ the limitations specified by lESNA for zone E2, the specifications are very stringent; any billboard meeting the E2 limits will be satisfactory for the higher ambient light conditions of zones E3 and E4. On this basis, while any eye illuminance value could be used, this report recommends using only that provided for zone E2. Providing that a method is available to calculate the billboard luminance that will generate a certain illuminance at the eye of a viewer, the illuminance limits of TM-11-00 can be converted to billboai'd luminance limits. The conversion formula is provided below. It must be noted, however, that this method is not totally straightforward, for there are variables that must be considered for any given billboard, also discussed below. A3.2 Determining the Maximum Allowable Billboard Average Luminance The system for relating billboard luminance to the illuminance produced at tlie eye is briefly summaiized in this section. A more detailed coverage of this topic, and lighting units and terms in general, is provided in Appendix A. Billboard luminance (which refers to the average luminance or brightness of billboard) is expressed in candelas per square meter, cd/sq.m., sometimes termed "nits." The illuminance produced at the eye, considered as landing on a vertical plane at the eye, is designated Ey and is measured in footcandles. To determine the Tnaximnm billboard average luminance, L, that can be allowed so as to meet a given illuminance limit at the viewer's eye. By in footcandles, the following must be know: • Area of billboard = S sq. ft. • Distance from billboard center to observation point = D feet (as measured from a plan view. Differences in height of the billboard and viewer normally can be disregarded, as can lateral angle effects from the billboard face.) Allowable maximum billboard average luminance, L = —- cd./sq.m. (nits) 1 For example, to determine whether a billboard meets a particular limit for the lESNA publication TM-11-00, the following steps are taken: 1. Select the applicable lighting zone. It is proposed that E2, an area with a low level of electric lighting, be selected as a st^dard._ _ _ 2. Find the applicable eye illuminance limit from table 1. If zone E2 is assumed, this will be 0.3 fc. 3. Determine the billboard size. Assume for example a billboard measuring 10 ft. 6 ins. x 36 ft., giving an area of 378 sq. ft. 4. Assume a distance to the viewer. Use 200 ft. (See discussion below). These values are entered into formula 1 above. 10.76.200'-0.3 Allowable maximum billboard average luminance = 378 = 342 cd/sq.m. (nits) A3.2.1 Viewer Distance The distance from the billboard to the viewer, D in the above formula, has a significant effect on the calculated allowable maximum billboard luminance. Billboards ai*e typically viewed over a range of distances, and so the choice of the value of D will be somewhat arbitrary. A short distance such as 100 ft. is probably too small for normal situations, and can produce a very low luminance limit. On the other hand, a very large distance such as 1000 ft. will rarely be applicable because viewers will normally be closer when reading the billboard. It may be questioned whether a short distance should be used as a standard to guard against possible glare effects produced at the eyes of a person driving past a digital billboard. Considering this, as a driver moves closer to a billboard that is positioned to the side of the roadway and the driver is viewing the road ahead, the lateral angle firom the driver's line of sight to the billboard increases. This angular effect causes any glare ^at the billboard may produce to reduce significantly. (Reference: American National Standard for Roadway Lighting, publication ANSI/IESNA RP-8-00, section A7. Glare reduces as the square of the angle from the line of sight.) Further, as this angle iucreases, the light intensity (candelas) directed toward the driver's eye decreases, as shown by photometric testing of a sample billboard. (Lighting Sciences Inc. test report no. LSI 21628). This effect also contributes to the reduction in glare as the driver approaches and then passes the billboard. These two effects more than offset other factors in determining the glare produced at the driver's changing location: that is, glare actually reduces as the driver's distance to a billboard that is off the side of the road becomes smaller, assuming attention is on the road ahead. In discussions with members of the advertising industry, it is apparent that billboard size and viewing distance are related. Larger billboards are used to attract viewers at a greater distance, while small billboards are provided where the observer is fairly close. On this basis, the viewing distances, D, provided below are suggested for use with the formula, based on four prevalent standard billboard sizes: Proposec Table 2 Viewer Distance Values, D Billboard Size Billboard Dimensions (ft) D ft. Small 11x22 150 Medium 10.5x36 200 Large 14x48 250 Very large 20x60 350 If there is a specific reason why a value of D other than as given above should be applied for a particular billboard installation, this different value may be substituted accordingly in the formula. It should be noted, however, that use of the above distances for the various billboard sizes, and the billboard luminance values so produced, have been field evaluated and appear to -be-reasonable A3.2.2 Allowable Average Luminance and Billboard Size For any given billboard size, formula 1 can be used to compute the allowable average luminance by incoiporating the suggested distance value fi'om table 2. The results for the standard dimension billboards are provided in table 3. Table 3 Maximum Level of Digital Billboard Average Luminance Candelas per Sq.M. (Nits) Proposed Standard (Based on lESNA Lighting Zone E2) Billboard Luminance --Dimensions (ft.) -- ft; (Gd-./sqim;)- - 11x22 150 300 10.5x36 200 342 14x48 250 300 20x60 350 330 ♦Based on an illuminance produced at the viewer's eye of 0.3 footcandles. ** Distance measured at groiind level to observer facing the billboard perpendicularly A3.3 Digital Billboard Photometric Testing A small sample digital billboard was supplied to Lighting Sciences' laboratories in Scottsdale, Arizona for photometric evaluation. This was a model as commercially produced in November 2006 by Young Electric Sign Company. Tliis was tested using a model 6440 goniophotometer. Tests were run for the device displaying entirely wliite, red, green and blue colors respectively. The white color is not formed by illuminating white LED's but rather by a combination of red, green and blue LED's. 10 The digital billboard was progi*ammable for different levels of dimming. Tests were conducted to measure the luminance at 10% dimming steps from 100% down to 10%. -It.was determined that the actual luminance reduction achieved using the various dimming steps accurately corresponded to within a few percent of the dimming settings indicated on the controller. Data from the series of tests allow the calculation of the luminance of any digital billboard color for full intensity or any level of dimming. Of specific interest were the luminances of a white display because this is the maximum luminance color, as it is generated by the combination of the red, blue and green LED's. A3.4 Determining the Allowable Dimmer Setting If a billboard luminance limit has been established by the methodology described above, the photometric data can also provide the dimming setting to be used. Results of the testing indicated that the digital billboard produced a maximum average luminance of approximately 7000 cd/sq.m. when displaying a completely white image at full power. In the above example, to limit the luminance to 342 cd/sq.m. the dimmer setting can be computed as - - follows: . Allowable luminance % dimmer settmg = — x 100 7000 xlOO 7000 = 4.9% This example is for amedium billboard size measuring 10.5 x 36'. The dimmer setting will be different for other billboard sizes because tlie allowable luminance changes per table 3. Table 4 presents the dimming settings calculated in an equivalent manner for the standard billboard sizes. TT Table 4 Suggested Dimming Settings Proposed Standard (Based on lESNA Lighting Zone E2) Billboard Dimensions (ft.) Dimming Setting 11x22 4.3% 10.5x36 4.9% 14x48 4.3% 20x60 4.7% It should be noted that table 4 is applicable only to the digital billboard that was tested. Different types of billboards will produce different results, and therefore require separate photometric testing. A3.5 Non-white Billboards If the digital image will never be totally white, higher % dimming settings can be used while still meeting the luminance limit: The actual measured luminances for the sample billboard measured in 2006 for a 100% luminance setting for different colors are: White Red Green Blue 7000 cd/sq.m. ,1500 cd/sq.m. 5100 cd/sq.m. 700 cd/sq.m. For a normal image diat includes multiple colors, the average luminance for a 100% setting will depend on tlie proportion of colors in the mix. Software and instrumentation is available to analyze billboard luminance when the billboard is being programmed, A3.6 Adoption of the Method This method uses the established and recommended procedures of lESNA to develop billboard luminance limits. The procedm'e can be adopted by referrmg to the limits of lESNA publication TM-11 -00 as provided in table 1 above, with the suggestion that lighting zone E2 values be 12