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Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview (2020)

Chapter: Chapter 5. Research and Testing Results

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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
×
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
×
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
×
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
×
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
×
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
×
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
×
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
×
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
×
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
×
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
×
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
×
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
×
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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Suggested Citation:"Chapter 5. Research and Testing Results." National Academies of Sciences, Engineering, and Medicine. 2020. Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/25679.
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43 CHAPTER 5. RESEARCH AND TESTING RESULTS According to the project plan, the tasks to be completed as part of this project include the execution of the research work plan (Smart Road Testing), development of the SSL Guide, and inclusion of a research and implementation roadmap of recommended future research and approximate associated costs. The project plan also includes ongoing monitoring of research currently being conducted in areas of relevance to this report and SSL Guide. These areas include: • Light and human health • Light and environmental impacts • Light and adverse weather • Light and aging • Lighting Design metrics • Crash comparisons between HID and SSL installations Any new data that emerges before the completion of the final document will be included. SMART ROAD TESTING An experiment on the closed Smart Road test track at VTTI was undertaken to identify critical metrics that can be used to determine the quality and usefulness of a roadway lighting system. This experiment was a driving task in the lighted test environment where the performance of the critical visual task by drivers was investigated in terms of the lighting design. This critical visual task was determined as a result of the assessment of safety performed as part of this project. Experimental Methods Participants Participants were recruited from the VTTI’s internal database. In addition, printed flyers and email listserv announcements were used to recruit participants. Participants were recruited from two age ranges: 18 to 35 years (younger), and 65 years and older (older). Sixty participants were recruited to participate in the study. The participant sample was age balanced to have equal number of younger and older participants. Participants had a valid U.S. drivers’ license and had a visual acuity of at least 20/40 (measured by Early Treatment Diabetic Retinopathy Study chart with an illuminator cabinet) and were not color blind (measured by the Ishihara Color Vision test). Experimental Design A repeated measures experimental design was used to assess the effects of light SPD, light level, surround ratio, and uniformity ratio on driver visual performance. Driver visual performance was measured using detection distances of objects as participants drove through the test environment. Pedestrians and small targets were used for the detection task. The independent variables used in this experiment are summarized in Table 3.

44 Table 3. Independent Variables and their Levels Used in the Study Independent Variable Level Classification Light Spectral Power Distribution 3000K LED 4000K LED 5000K LED HPS (only at medium light level) Between-Subjects Light Level High (1.5 cd/m2) Medium (1.0 cd/m2) Low (0.7 cd/m2) Within-Subjects Surround Ratio (Avg. Shoulder Illuminance to Avg. Lane Illuminance) High (0.8) Low (0.45) Between-Subjects Uniformity Ratio (Avg. to Min Luminance) High (1.8 to 3.5) Low (1.3 to 1.4) Between-Subjects Speed High (55 mi/h) Low (35 mi/h) Within-Subjects Age Old (65 and older) Young (18 to 35 years) Between-Subjects Independent Variables As seen in the experimental design, several variables were controlled or manipulated during the study. Because this study used human participants, the limiting duration of each experimental session was critical to reduce the potential for confounding effects of learning and fatigue, which were important in this experiment due to the higher number of independent of variables. Some of the independent variables were classified as between-subjects, while the remaining were classified as within-subjects. Between- subjects variables are those variables that cannot be manipulated within an experimental session like the spectral power distribution of the light and surround ratio. They are listed below.

45 Spectral Power Distribution of the Light Three commercially available LED luminaire types with varying SPDs along with an HPS light source were tested (see Figure 17). In the absence of an established metric to distinguish the SPDs of luminaires, CCT was used to evaluate the effect of SPDs on visual performance. Three distinct CCTs of 3000, 4000, and 5000K LEDs were selected for the purpose of the evaluation. 3000K and 5000K CCT luminaires represent the minimum and the maximum range of color temperatures available currently in the market, while 4000K LED is widely used for roadway lighting based on the results of the survey discussed in Chapter 2. As a reference, an HPS light source was used at a single light level. The LED luminaires used for the study were obtained from a single manufacturer and had matching light intensity distributions. Dimming was used to match the lighting levels. All the luminaires used for this study were Type-II medium throw luminaires. Figure 17. Spectral Power Distributions of the Light Sources Used to Measure Drivers’ Visual Performance and Discomfort Glare Perception Surround Ratio Surround ratio is the measure of how bright the roadway travel lanes are with respect to the adjacent areas of paved and unpaved shoulder. CIE 140-2000 defines surround ratio as the average horizontal illuminance on the two longitudinal strips each adjacent to the two edges of the carriageway to the average horizontal illuminance on two longitudinal strips each adjacent to the two edges of the carriageway. Two surround ratios (see Table 3) were tested to examine the impact of surround ratio on visual performance. Uniformity Ratio Uniformity ratio is defined as the ratio of average to minimum pavement luminance of the roadway. Two levels of uniformity (see Table 3) were assessed to examine how the uniformity ratio affects the visibility of objects.

46 Age Participants were divided into two age groups (Table 3) to examine how physiological changes in the eyes affect the visual performance of drivers. Younger drivers typically have a higher performing visual system that can be partially offset by the years of driving experience. Thus, by including age, a wide range of physiological capabilities and driving experiences can be considered. Light Level Light level is the average pavement luminance level of roadway. Three light levels were used in the study (Table 3). This factor was included to examine the effect of lighting levels on visual performance. As noted above, the light fixtures had similar intensity distributions, and a dimming system was used to control the light level of the roadway. These levels were verified through a photometric analysis of each lighting condition. The horizontal and vertical illuminance was also characterized for all lighting levels in the roadway and on the shoulder of the road. Speed Because speed can affect the field of view of the driver, two speeds were selected to represent common driving scenarios: highway at 55 miles per hour (mi/h) and arterial at 35 mi/h. Object Two objects of different sizes and shapes were selected to examine how different lighting configurations affect the visibility of different objects. Small targets represented hazards that may be on or near the roadway and are just large enough that they may cause damage to a vehicle and pedestrians were used because they are commonly encountered in many driving scenarios and can also represent the presence of wildlife. Each of these objects was presented in a different color and at a different offset distance from the roadway to simulate hazards on the roadways. Object Color: Roadway lighting with different spectral power distribution values can affect the perception of color of objects on or near the roadway. For targets, three color variants (red, blue, and gray) were used to determine if the spectral power distribution of the lighting affects the visibility differently. For pedestrian, four color variants (red, blue, gray, and black) were used to determine if the SPD has an effect on the visibility of pedestrians’ clothing color. Object Offset: Objects and pedestrians may appear at different distances from the driver’s lane and will therefore be lit differently based on their location within the roadway luminaires intensity distribution. Three different distances from the roadway were used in the experiment. For targets, two offsets were used: 2 feet left of the driving lane (indicated by 2L in Figure 18), and 2 feet right of the driving lane (indicated by 2R in Figure 18). For pedestrians, the two offsets used were 2 feet right of the driving lane (indicated by 2R in Figure 18) and 10 feet right of the driving lane (indicated by 10R in Figure 18). The vertical illuminance levels on the pedestrian locations and the target locations were matched across the lighting conditions (see Table 8 and Table 9).

47 Figure 18. Locations of the Offset Locations with Respect to the Vehicle Travel Direction on the Virginia Smart Road Dependent Variables Detection Distance Detection distance was the dependent variable in the experiment. Detection distance is the distance between the object that is being detected and the driver when the driver detects the object. Detection distance is a commonly used measure of visual performance in nighttime roadway visibility studies (Bhagavathula & Gibbons, 2013; Bhagavathula, Gibbons, & Nussbaum, 2017; Mayeur, Bremond, & Bastien, 2010; Shinar, 1985). Discomfort Glare Rating Discomfort glare was measured using a rating scale as shown in Table 4. This scale has been reported to produce reliable data, with smaller numbers meaning lower discomfort glare and higher numbers meaning higher discomfort glare (Bhagavathula & Gibbons, 2018; Fisher, 1991; Tyukhova, 2015; Tyukhova & Waters, 2018). The scale also has a “zero” anchor for no discomfort glare. Table 4. Discomfort Glare Rating Scale Used in this Study Description Rating No discomfort glare 0 Glare between non-existent and noticeable 1 Glare noticeable 2 Glare between noticeable and disagreeable 3 Glare disagreeable 4 Glare between disagreeable and intolerable 5 Glare intolerable 6

48 Equipment and Facilities Virginia Smart Road This study was conducted on the Virginia Smart Road, which is a 2.2-mile-long, controlled-access test track. The Smart Road is equipped with a configurable roadway lighting system that includes 75 poles that each support three luminaires (Figure 19). The lighting system can be configured to 40 m, 80 m, or 120 m spacing between lights. The lighting system can be controlled remotely, allowing researchers to change luminaires, spacing, or light levels as needed. For the current study, a spacing of 262 ft (80 m) and a mounting height of 49 ft (15 meters) were used. Figure 19. Smart Road Lighting System To manipulate the surround and uniformity ratios, custom shields were designed and fitted to the luminaires (see Figure 20). Three sets of shields were specifically designed to manipulate the surround and uniformity ratios. One shield increased the uniformity ratio (road was not evenly lighted). In the second shield condition, the surround ratio was increased (light levels on areas adjacent to the travel lane, e.g., shoulders were lowered). In the final shield condition, both uniformity and the surround ratios were increased. The shields were designed to be clipped on to the luminaires.

49 Figure 20. (a) No Shield on the Luminaire Representing High Uniformity-High Surround, (b) Luminaire with High Uniformity-Low Surround Shield, (c) Luminaire with Low Uniformity-High Surround Shield, (d) Luminaire with Low Uniformity-Low Surround Shield Test Vehicles The test vehicles were two identical 2017 Ford Explorers. Each vehicle is equipped with a data acquisition system that captures four camera views inside and outside the vehicle, GPS, and vehicle network data. Targets Targets were 7-inch-square pieces of plywood painted with coarse, non-reflective paint (Figure 21). Targets were propped up with wooden stands and placed approximately 2 feet outside the driving lane either on the right or left side of the participant’s driving lane. For safety, targets were designed to break if struck by the participant vehicle. The spectral reflectance of the targets is shown in Figure 22. The colors of the targets were selected because they represent two opposite ends of the color spectrum and a neutral color. As a result of this color choice, the benefits of the high and the low CCT light sources would be highlighted. Figure 21. Red, Gray, and Blue Targets

50 Figure 22. Spectral Reflectances of the Three Colored Targets Used in the Study Pedestrians Pedestrians were on-road experimenters wearing colored surgical scrubs (Figure 23), which allowed the pedestrian to have a consistent color contrast. Four colored scrubs were used: red, black, gray, and blue. Pedestrians always stood facing the roadway with their arms by their side, so that they appeared similarly across lighting configurations. The spectral reflectances of these targets are shown in Figure 24. The color selection was the same as for the targets, but an additional low luminance neutral was added for further analysis possibilities. Figure 23. Pedestrians in Colored Scrubs

51 Figure 24. Spectral Reflectance of the Colored Scrubs Used in the Study Illuminance Characterization The horizontal and vertical illuminance was characterized for all types of luminaires without shields (3000K LED, 4000K LED, 5000K LED, and 2100K HPS) and for one light type (4000K LED) with all of the shield conditions (HUR-LSR, LUR-HSR, and LUR-LSR) at all the light levels. All the luminaires were of the same type and were from the same manufacturer, so it was assumed that the light levels would not change for the luminaires of other CCTs. The illuminance was characterized using the trailer-based roadway lighting mobile measurement system (TRLMMS) data collection system with horizontal illuminance sensors at the quarter lane points. The data capture rate was 20 Hz, and the vehicle travel rate was for a spatial sample rate of approximately 0.9 m. In addition, vertical illuminance was also captured opposite the direction of travel using two illuminance meters mounted at 5 ft (~1.5 m) high and at the quarter lane position. Summarized measurements of average illuminance, uniformity, and surround ratios are shown in Table 5, Table 6, and Table 7. Detailed illuminance measurements are included in Appendix C.

52 Table 5. Average Vertical and Horizontal Illuminance Levels Across Different Light Types, Shield Conditions and Light Levels Light Type Shield Light Level Avg. Horizontal Illuminance (lx) Avg. Vertical Illuminance (lx) 2100K HPS HSR-HUR Medium 12.3 10.5 5000K LED High 20.2 15.9 4000K LED 19.0 16.3 3000K LED 18.9 16.2 5000K LED Low 8.9 8.7 4000K LED 9.4 8.6 3000K LED 9.9 7.9 5000K LED Medium 12.9 10.5 4000K LED 11.5 10.2 3000K LED 12.6 10.1 4000K LED HUR-LSR High 17.7 10.5 Low 9.6 6.1 Medium 11.0 7.1 LUR-HSR High 17.2 13.2 Low 8.5 6.6 Medium 10.8 8.3 LUR-LSR High 17.3 13.9 Low 8.3 6.4 Medium 12.7 9.4

53 Table 6. Uniformity Ratios of Illuminance Across Different Light Types, Shield Conditions and Light Levels Light Type Shield Light Level Uniformity Ratio (Avg/Min Illuminance) 2100K HPS HSR-HUR Medium 7.5 5000K LED High 7.5 4000K LED 3.8 3000K LED 3.8 5000K LED Low 4.9 4000K LED 3.7 3000K LED 5.8 5000K LED Medium 6.1 4000K LED 4.6 3000K LED 6.8 4000K LED HUR-LSR High 4.1 Low 7.2 Medium 5.1 LUR-HSR High 6.7 Low 4.3 Medium 4.6 LUR-LSR High 3.5 Low 4.5 Medium 3.6

54 Table 7. Surround Ratios of Illuminance Across Different Light Types, Shield Conditions and Light Levels Light Type Shield Light Level Surround Ratio (Avg. Shoulder / Avg. Lane Illuminance) 2100K HPS HSR-HUR Medium 0.9 5000K LED High 0.9 4000K LED 0.8 3000K LED 0.8 5000K LED Low 0.9 4000K LED 0.9 3000K LED 0.7 5000K LED Medium 0.8 4000K LED Medium 0.8 3000K LED 0.9 4000K LED HUR-LSR High 0.5 4000K LED Low 0.5 4000K LED Medium 0.5 4000K LED LUR-HSR High 0.8 4000K LED Low 0.7 4000K LED Medium 0.7 4000K LED LUR-LSR High 0.6 4000K LED Low 0.5 4000K LED Medium 0.6 The vertical illuminance on the pedestrians and targets was measured by an illuminance meter (Minolta T-10A). For the pedestrians, the illuminance was measured at 5 ft (~1.5 m) from the road surface facing the direction of vehicle. For the targets, the vertical illuminance was measured by placing the illuminance flush with the side facing the vehicle when the target was placed on the road. The vertical illuminance values for pedestrians and target locations are shown in Table 8 and Table 9.

55 Table 8. Vertical Illuminances Measured at the Pedestrian and Target Locations in the HUR- HSR Shield Condition Light Level Light Type Pedestrian Target 2 ft. Right 10 ft. Right 2 ft. Right 2 ft. Left High 2100K HPS 19.9 14.9 17.5 21.1 3000K LED 21.5 15.1 18.5 21.9 4000K LED 20.1 15.9 17.8 22.7 5000K LED 22.3 16.7 18.3 22.5 Medium 2100K HPS 13.8 9.7 11.1 13.5 3000K LED 14.7 10.6 11.8 14.2 4000K LED 14.3 9.2 12.4 13.6 5000K LED 13.7 10.2 11.4 14.4 Low 2100K HPS 8.9 5.8 7.1 8.9 3000K LED 9.4 6.5 6.9 9.1 4000K LED 9.1 5.9 7.6 9.6 5000K LED 8.5 6.3 7.4 9.7 Table 9. Vertical Illuminances Measured at the Pedestrian and Target Locations in the Surround and Uniformity Shield Conditions Light Level Shield Condition Pedestrian Target 2 ft. Right 10 ft. Right 2 ft. Right 2 ft. Left High HUR-LSR 19.6 12.6 16.8 21.6 LUR-HSR 20.9 13.6 16.6 20.4 LUR-LSR 19.0 12.5 16.2 20.0 Medium HUR-LSR 10.3 8.5 10.3 13.3 LUR-HSR 11.8 9.4 10.1 13.2 LUR-LSR 9.8 7.8 9.9 13.6 Low HUR-LSR 7.9 7.1 6.2 9.9 LUR-HSR 7.5 7.0 6.8 10.9 LUR-LSR 7.4 5.5 6.2 10.0

56 Experimental Procedure Participant Recruitment, Consent, and Compensation Participants were recruited from VTTI’s internal database of volunteers. Participants were contacted by telephone to determine if they were interested and eligible for participation, and if so, they were scheduled for an experimental session. When participants arrived at VTTI, they filled out an informed consent form and performed some simple vision tests to make sure they were eligible. Eligible participants performed the experimental tasks described below. Participants were paid $30 per hour of participation. Participants were recruited for two sessions and experienced the same lighting conditions at the two different speeds in each session. Procedures Two participants were scheduled for each experimental session. Once the paperwork and vision screening were completed, participants were read a script that described the experimental tasks. Participants were also shown the discomfort rating scale and were given a definition of discomfort glare and how to provide the discomfort glare rating. Participants were then escorted to the two experimental vehicles. Each participant drove one of the vehicles, and in-vehicle experimenters accompanied each participant. Each participant drove laps on the Smart Road. Only one participant drove through the lighted section of the Smart Road at a time to allow both participants to drive laps without ever driving past the other, and without interference from the other vehicle’s headlamps. Participants drove a total of 12 laps. After every four laps, the light levels on the road were changed. At the end of 1st, 5th, and 9th laps, the in-vehicle experimenter asked the participants to rate the amount of discomfort glare using the scale. Copies of the scale were provided to the participants for reference. As participants drove through the laps, several objects were presented at designated stations. Participants were asked to verbally identify the type of object (pedestrian or target) as soon as it was visible. The in-vehicle experimenter flagged these moments in the data stream by pressing a hand-held button. The GPS coordinates of the object’s locations were predetermined. The GPS coordinates at detection and recognition were cleaned up at a later point. The detections were adjusted to the point in time at which the participants said “target” or “pedestrian,” thereby eliminating the time delay due to experimenter input. A high precision differential GPS unit was used in the experimental vehicle and to collect the GPS locations of the worker. The GPS system had an accuracy of about 0.33 ft (0.1 m). The presentation of the objects, speeds, and light levels was counterbalanced across several orders to offset any learning effects. Once all 12 laps were complete, participants were instructed to exit the Smart Road and return to VTTI. There, they were compensated and released. The same process was repeated for the second experimental session at a different speed. Analyses Visual Performance across LED Light Sources The goal of this analysis was to determine if there were visual performance differences between LED light sources of different CCTs across different light levels, surround ratios, and uniformity ratios. Two analyses were conducted to assess visual performance differences across LED light sources. Detection Distance Analysis In the first analysis, detection distances were compared across all the light sources using a linear mixed model (LMM). Two LMM analyses were used to assess the effects of light source type, light level,

57 surround ratio, and uniformity ratio on detection distance. One LMM was used for the pedestrian detection task and the other was used for the target detection task. Safe Stop Analysis In the second analysis, each participants’ detection distance under each condition was classified into “safe” vs. “not safe.” The analysis was termed “Safe Stop Analysis.” A detection was classified as “safe,” if the detection distance was greater than the stopping sight distance (SSD) for the speed at which the vehicle was travelling during the detection task. AASHTO uses SSD as a design standard for sight distance in road design (2001); it is the minimum distance required for a driver travelling at the design speed to come to a complete stop. If the detection distance is lower than the SSD, then it can be assumed that under the given conditions, the driver would not be able to come to a safe stop and as a result that particular distance was classified as “not safe.” For the second analysis, a mixed models logistic regression (MMLR) was performed with the type of stop (“safe” vs. “not safe”) as the dependent measure. Two MMLR analyses were used to assess the effects of light source type, light level, surround ratio, and uniformity ratio on the types of “safe stop” vs. “not safe stop.” One MMLR was used for the pedestrian detection task and the other was used for the target detection task. MMLR allows the estimate of a specific factor to be considered as an averaged value over all the levels of other factors included in the same model. Thus, the confounding effect can be effectively addressed by simply including multiple factors that might simultaneously confound each other in the model. Visual Performance across LED, HPS and Unlighted Conditions The goal of this analysis was to determine if there were visual performance differences between LED light sources of different CCTs, a legacy HPS light source, and unlighted conditions. Similar to the visual performance analysis across LED light sources, a two-pronged approach was used for this analysis. In the first analysis, LMMs were used to assess the detection distance differences between LED, HPS, and unlighted conditions. In the second analysis, MMLRs were used to assess the differences in the type of stop between LED, HPS, and unlighted conditions. Two LMMs and MMLRs were used to assess the effects of light source type on detection distance and type of stop (one for the pedestrian detection task and the other for the target detection task). Visual Performance across Uniformity Ratios of LED and HPS Light Sources Uniformity ratios varied across unshielded (high uniformity and high surround ratios) LED light sources and HPS light sources (1.3 vs 3.5). As a result, it was anticipated that for achromatic detections of pedestrian and targets, the uniformity ratio difference between the light sources instead of the spectral power distribution could affect visual performance. Two LMMs was conducted at the medium light level for both targets (gray colored) and pedestrian (gray clothing) detection tasks with the detection distance as the dependent measure and uniformity ratio, speed, and offset as independent measures. Discomfort Glare Perception across LED Light Sources The goal of this analysis was to determine if t discomfort glare perception differences occurred between LED light sources of different CCTs across difference light levels, surround ratios, and uniformity ratios. One LMM was used to assess the effects of light source type, light level, surround ratio, and uniformity ratio on perception of discomfort glare. Discomfort Glare Perception across LED and HPS Light Sources The goal of this analysis was to determine if t discomfort glare perception differences occurred between LED light sources of different CCTs, a legacy HPS light source, and unlighted conditions. One LMM was used to assess the effects of light source type on discomfort glare perception.

58 For all statistical tests, age was included as a blocking factor. The level of significance was established at p < 0.05 for all statistical tests. Where relevant, post hoc analyses (pairwise comparisons) were performed using Tukey’s honest significant difference (HSD). Least square means and standard errors were reported for LLMs. For MMLRs, odds ratios along with 95% confidence intervals were reported for significant factors. Photometric Analyses A photometric analysis was conducted using a calibrated photometer (ProMetric PM-9913E-1, Radiant Imaging®) to measure the changes in object luminance and contrast as the vehicles approached the object under different lighting conditions. The results of this analysis will help explain the influence of photometric properties of each lighting condition on a driver’s visual performance. Luminance and contrast measurements were conducted with the photometer mounted inside the experimental vehicle at a height of 5 ft (~1.5 m) from the surface of the road (see Figure 25). The Weber contrast formula was used to calculate the contrast of pedestrians and targets. Weber contrast is the difference between an object and its background relative to the luminance of the background and is calculated based on the following formula: 𝐶𝐶 = (𝐿𝐿𝑡𝑡 − 𝐿𝐿𝑏𝑏) 𝐿𝐿𝑏𝑏 where C is the luminance (Weber) contrast, Lt is the object luminance, and Lb is the background luminance. An object is considered be in negative contrast when it is darker than its background and positive contrast when it is brighter than its background. For calculating the luminance of targets and pedestrians, polygons were traced around the pedestrian (see Figure 26) and target, and the software calculated the mean luminance within the selected polygon. Background luminance was calculated by tracing the same size polygon around the object location close to its boundaries (on the left, right, top, and bottom of the object of interest). The Weber contrast formula was used to calculate the contrast of pedestrians and targets. Figure 25. Location of the Calibrated Photometer Inside the Test Vehicle at the Driver’s Seat

59 Figure 26. Sample Image Output from the Photometer and the Method Used to Measure the Luminance of the Pedestrian by Tracing Polygons For pedestrians, the luminance and contrast were measured at distances of 820 ft (250 m), 410 ft (125 m), and 164 ft (50 m) from the pedestrian locations. These distances constituted the 90th, 50th, and the 10th percentiles of the detection distances recorded for the pedestrians. Luminances and contrasts of only black- and gray-clothed pedestrians were measured because most of the significant differences were between those two colors of clothing. Similarly, for targets, the luminances and contrasts were measured at distances of 49 ft (15 m), 50 ft (164 m), and 328 ft (100 m), which constituted the 10th, 50th, and 90th percentiles of detection distances recorded during the visual performance analysis. In addition, the measured luminance and contrast values of the pedestrians and targets were interpolated to determine the luminance and contrasts at the instance of detection. Results Visual Performance across LED Light Sources Pedestrian Detection Task – Detection Distance Analysis All significant LMM results are summarized in Table 10. For the ease of understanding, only the interactions in bold text are discussed in the subsequent subsections. These interactions were selected because analyzing their interactions can help understand the effects of all the relevant independent variables (light source type, light level, surround ratio, and uniformity ratio) on visual performance of drivers.

60 Table 10. Significant Statistical Results from the LMM Analysis of Detection Distance of LED Light Source Comparison Source Numerator (Num) DF Denominator (Den) DF F Ratio P Value Age 1 33.3 30.2 <.0001 Surround Ratio 1 34.2 18.8 0.000 Light Level 2 2082.5 26.2 <.0001 Speed 1 2114.1 71.1 <.0001 Clothing Color 3 2082.2 61.7 <.0001 Offset 1 2082.2 369.0 <.0001 Age*Surround Ratio 1 34.3 5.8 0.022 Age*Speed 1 2117.1 15.1 0.000 Age*Offset 1 2082.2 29.9 <.0001 Surround Ratio*Light Level 2 2082.5 4.2 0.015 Surround Ratio*Clothing Color 3 2082.2 6.5 0.000 Light Source Type*Speed 2 2106.5 12.6 <.0001 Light Level*Clothing Color 6 2082.2 14.8 <.0001 Speed*Clothing Color 3 2082.2 12.2 <.0001 Speed*Offset 1 2082.2 15.7 <.0001 Clothing Color*Offset 3 2082.2 29.6 <.0001 Age*Surround Ratio*Light Level 2 2082.5 6.8 0.001 Age*Surround Ratio*Speed 1 2117.9 15.7 <.0001 Age*Speed*Clothing Color 3 2082.2 2.9 0.034 Uniformity Ratio*Surround Ratio*Speed 1 2114.5 10.5 0.001 Surround Ratio*Light Source Type*Speed 2 2106.8 3.6 0.028 Surround Ratio*Light Source Type*Offset 2 2082.3 3.5 0.030 Surround Ratio*Light Level*Clothing Color 6 2082.2 9.1 <.0001 Surround Ratio*Light Level*Offset 2 2082.3 9.7 <.0001 Surround Ratio*Speed*Clothing Color 3 2082.2 8.7 <.0001 Light Level*Speed*Offset 2 2082.3 10.1 <.0001 Light Level*Clothing Color*Offset 6 2082.3 14.9 <.0001 Speed*Clothing Color*Offset 3 2082.3 15.4 <.0001

61 Source Numerator (Num) DF Denominator (Den) DF F Ratio P Value Uniformity Ratio*Surround Ratio*Light Source Type*Speed 2 2106.6 3.2 0.041 Uniformity Ratio*Surround Ratio*Light Source Type*Clothing Color*Age 6 2082.3 2.2 0.044 Interactive Effect of Light Source Type, Surround Ratio, and Offset The combined effects of light source type, surround ratio, and offset on detection distance are summarized in this section. There were no statistical differences between any of the LED light sources at either the offset location or the surround ratio conditions, although, 4000K LED had slightly longer detection distances at the 2-ft and 10-ft offset locations under the high surround ratio condition and the 10-ft offset location under the low surround ratio condition (see Figure 27). Detection distance differences were statistically significant for the 4000K LED between the low and high surround ratio conditions at the 2-ft offset location. Further, detection distances were higher for the 2-ft offset location than the 10-ft offset location for each light source and surround ratio condition (see Figure 27). Note: Values are means of detection distances and error bars indicate standard errors. Figure 27. Interactive Effect of Light Source Type, Surround Ratio, and Offset on Detection Distance Pedestrian luminance increased as the vehicle approached the pedestrian locations under all surround ratios because of the vehicle headlamps (see Figure 28). This increase in pedestrian luminance also altered the contrast (see Figure 29) in which the pedestrian is rendered in because the luminance of the pedestrian increased (due to headlamps) without significantly influencing the background luminance. Pedestrian luminances (see Figure 28) were greater in the higher surround ratio condition than in the lower surround ratio condition at both pedestrian locations. The pedestrian at the 2-ft offset location in the higher surround ratio was rendered in negative contrast until the vehicle was at distance of 300 ft from the pedestrian beyond which the pedestrian was rendered in positive contrast (see Figure 29). When the pedestrians undergo a change in the contrast polarity, they go through a phase of contrast neutrality. Contrast neutrality occurs when the luminance of the pedestrian is equal to luminance of the background and contrast becomes zero. At zero contrast the pedestrian becomes invisible to the driver. This change in

62 the contrast polarity occurred only in the high surround ratio condition at approximately 300 ft from the pedestrian at the 2-ft offset location. Figure 28. Change in Luminance as the Vehicle Approaches the Pedestrian Locations

63 Figure 29. Change in Contrast as the Vehicle Approaches the Pedestrian Locations In the higher surround ratio condition at the instant of the detection, the pedestrian was rendered in negative contrast, whereas, under lower surround ratio, the pedestrian was always rendered in positive contrast (see Figure 30). This negative contrast could have aided pedestrian detection in the higher surround ratio condition because earlier research has shown that negative contrasts objects are detected sooner than positively contrasted objects (Adrian, 1989b; Aulhorn, 1964; Bhagavathula & Gibbons, 2018). The pedestrian at the 10-ft offset location was always rendered in negative contrast throughout the vehicles’ approach to the pedestrian (see Figure 29). The differences in detection distance across the higher and lower surround ratios at the 10-ft offset location were not as high as the differences at the 2-ft location, perhaps because the pedestrian at the 10-ft offset location under both surround ratio conditions was rendered in negative contrast (see Figure 30). Although the pedestrian was rendered in negative contrast with the same magnitude, the luminance on the pedestrian at the higher surround ratio condition was higher than the lower surround ratio condition (see Figure 28). This higher luminance under the higher surround ratio at the 10-ft offset location could have aided detection, which resulted in a longer detection distance.

64 Figure 30. Average Contrasts of the Pedestrians at the Instance of Detection for Each Pedestrian Location Under Both Surround Ratio Conditions Interactive Effect of Light Source Type, Surround Ratio, and Speed The combined effects of light source type, surround ratio, and speed on detection distance are summarized in this section. There were no statistically significant differences between LED light sources across the surround ratios and speeds. However, 4000K LED had the highest detection distance under the high surround ratio condition at 55 mi/h, and both 3000K and 4000K LED had the highest detection distance under the high surround ratio at 35 mi/h (see Figure 31). Notes: Values are means of detection distances and error bars indicate standard errors. Figure 31. Interactive Effect of Light Source Type, Surround Ratio, and Speed on Detection Distance Detection distance differences were statistically significant for the 4000K LED between the low and high surround ratio conditions at 55 mi/h. Detection distances were longer at 35 mi/h than at 55 mi/h for 3000K LED under low and high surround ratio conditions and for 5000K LED only at the high surround ratio condition. For 4000K LED, there were no differences in the detection distances between the 35 and 55 mi/h speeds.

65 Interactive Effect of Light Level, Surround Ratio, and Clothing Color The combined effects of light level, surround ratio, and clothing color on detection distance are summarized in this section. In general, increases in the light level resulted in an increase in the detection distance irrespective of the surround ratio and the color of the clothing (see Figure 32). Further, detection distances in the higher surround ratio were statistically greater than those in the lower surround ratio at each light level, especially for the black colored clothing (see Figure 32). Note: Values are means of detection distances and error bars indicate standard errors. Figure 32. Interactive Effect of Light Level, Surround Ratio, and Clothing Color on detection Distance Interactive Effect of Light Level, Surround Ratio, and Offset The combined effects of light level, surround ratio, and offset on detection distance are summarized in this section. Increases in light level resulted in an increase in the detection distance in both the surround ratios at the 2-ft offset location and the high surround ratio at the 10-ft. offset location (see Figure 33). For the 2-ft offset location, detection distances in the high surround ratio were longer than those in the low surround ratio. For the 10-ft offset distance, the difference between high surround ratio and low surround ratio was only statistically significant at the highest light level. Once again, at all light levels and surround ratios, detection distances at the 2-ft offset location were higher than those at the 10-ft. offset location (see Figure 33).

66 Note: Values are means of detection distances and error bars indicate standard errors. Figure 33. Interactive Effect of Light Level, Surround Ratio, and Offset on Detection Distance Increases in the light level resulted in increases in the pedestrian luminance at both pedestrian offset locations (see Figure 34). Higher surround ratio conditions also had higher pedestrian luminances than lower surround conditions. Pedestrians in the high surround ratio condition for the 2-ft offset condition were initially rendered in negative contrast, and as the vehicle got closer to the pedestrian, the contrast polarity reversed, and they were rendered in positive contrast (see Figure 35). For the same offset location under lower surround ratio, the pedestrian was always rendered in positive contrast. For the pedestrian at the 2-ft offset under the higher surround ratio, the light level affected the distance at which contrast neutrality (phase of zero contrast when the negative contrast transitions to positive contrast) occurred (see Figure 35). Contrast neutrality occurred closest to the pedestrian location under the lowest light level and farthest under the medium light level.

67 Figure 34. Change in Pedestrian Luminance at Different Light Levels as Vehicles Approached the Pedestrian Figure 35. Change in Pedestrian Contrast at Different Light Levels as Vehicles Approached the Pedestrian

68 If pedestrians were rendered in negative contrast, this could have aided in their detection for the 2-ft offset location under the higher surround ratio (Figure 36). For the 10-ft offset location, pedestrians were rendered in almost similar negative contrast under all surround ratios (see Figure 36), but luminances were higher in the higher surround ratio condition, which could have contributed to the increase in detection distances. Detection distances in the 10-ft offset location were also lower than detection distances in the 2-ft offset location because they need higher contrast (more negative contrast) to be detected because they were offset from the line of sight of the driver. This is consistent with existing research in the area that showed that an increase in the offset distance from the roadway increases the threshold contrast for detection (Gibbons, 1993). Figure 36. Average Contrasts of the Pedestrians at Detection for Each Pedestrian Location Under Both the Surround Ratio Conditions at the Three Light Levels Interactive Effect of Uniformity Ratio, Surround Ratio, and Speed The combined effects of uniformity ratio, surround ratio, and speed on detection distance are summarized in this section. There were no significant differences between low and high uniformity ratios across both the surround ratios and speed limits. In general, detection distances were longer in the high surround ratio than in the low surround ratio (see Figure 37). Detection distances were also longer in 35 mi/h (M = 505.21 ft [153.99 m]) than 55 mi/h (M = 434.28 ft [132.37 m]).

69 Note: Values are means of detection distances and error bars indicate standard errors. Figure 37. Interactive Effect of Uniformity Ratio, Surround Ratio, and Speed on Detection Distance Interactive Effect of Age and Surround Ratio The detection distance differences between older and younger drivers were also dependent on the surround ratios. The detection distance between younger and older drivers was statistically significant only under the high surround ratio, with the younger drivers having longer detection distances than older drivers (see Figure 38). There were no statistical differences between the older and younger drivers at the low surround ratio. Note: Values are means of detection distances and error bars indicate standard errors. Figure 38. Interactive Effect of Age and Surround Ratio on Detection Distance Pedestrian Detection Task – Safe Stop Analysis All significant MMLR results are summarized in Table 11. All the main effects except the effect of light source type were significant. 0 100 200 300 400 500 600 700 800 HSR LSR D et ec tio n D is ta nc e (f t.) Surround Ratio Old Young

70 Table 11. Significant Statistical Results from the MMLR Analysis of Type of Stop of Pedestrians Under LED Light Source Comparison Effect Num DF Den DF F Value P Value Light Level 2 2383 6.8 0.001 Surround Ratio 1 2383 8.0 0.005 Uniformity Ratio 1 2383 6.2 0.013 Age 1 2383 22.5 <.0001 Clothing Color 3 2383 17.7 <.0001 Offset 1 2383 99.5 <.0001 Speed 1 2383 546.8 <.0001 The odds ratios of each of the significant factors and the 95% confidence intervals are shown in Table 12. For light level, the odds of detecting the pedestrian from a safe distance in the high light level were 1.7 and 1.4 times that of detecting the pedestrian from safe distance under low and medium light levels, respectively. This shows that the high level increases the odds of detecting a pedestrian. Higher surround ratios and higher uniformity ratios also increase the odds of detecting the pedestrian safely by 2.6 and 2.3 times, respectively. Younger driver were 5.6 times more likely to detect the pedestrian from a safe distance than older drivers. Regarding the color of clothing, pedestrians wearing red were three times more likely to be detected from safe distance than those wearing black. Similarly, pedestrians wearing blue and gray were 1.3 and 1.6 times, respectively, more likely to be detected from a safe distance than those wearing black. Pedestrians that were located at the 2-ft offset were three times more likely to be detected from a safe distance than those who were at the 10-ft offset location. Finally, pedestrians were 37 times more likely to be detected from a safe distance when the drivers were travelling at 35 mi/h than at 55 mi/h. Table 12. Odds Ratios of Significant Factors in MMLR Analysis of Detection of Pedestrians Independent Variable Level Reference Level Odds Ratio 95% Confidence Limits Light Level High Low 1.7 1.3 2.3 High Medium 1.4 1.1 1.8 Surround Ratio HSR LSR 2.6 1.3 5.0 Uniformity Ratio HUR LUR 2.3 1.2 4.4 Age Younger Older 5.6 2.7 11.4 Color Blue Black 1.3 1.0 1.8 Gray Black 1.6 1.2 2.2 Red Black 3.3 2.4 4.6 Offset 2 ft. 10 ft. 3.4 2.7 4.3 Speed 35 mi/h 55 mi/h 37.7 27.8 51.1

71 Target Detection Task All significant LMM results are summarized in Table 13. For ease of understanding, only the interactions in bold text will be discussed in the subsequent subsections. Table 13. Significant Statistical Results from the LMM Analysis of Detection Distance of Targets Under LED Light Source Comparison Source Num DF DF Den F Ratio P Value Age 1 33 34.6 <.0001 Light Level 2 1368.1 46.2 <.0001 Speed 1 1395 26.6 <.0001 Target Color 2 1367.5 13.5 <.0001 Offset 1 1368.1 19.0 <.0001 Age*Light Level 2 1367.9 5.9 0.003 Age* Target Color 2 1367.5 4.7 0.009 Age*Offset 1 1367.7 7.2 0.007 Uniformity Ratio* Target Color 2 1367.2 10.0 <.0001 Uniformity Ratio*Offset 1 1367.9 16.4 <.0001 Surround Ratio* Target Color 2 1367.4 10.4 <.0001 Light Source Type*Light Level 4 1367.9 2.5 0.039 Light Source Type*Speed 2 1385.3 11.5 <.0001 Light Level*Speed 2 1367.6 16.2 <.0001 Light Level* Target Color 4 1367.4 29.9 <.0001 Light Level*Offset 2 1367.5 7.4 0.001 Clothing Color*Offset 2 1367.2 12.3 <.0001 Age*Uniformity Ratio* Target Color 2 1367.2 3.2 0.041 Age*Uniformity Ratio*Offset 1 1367.5 13.2 0.000 Age*Light Source Type*Speed 2 1397.8 3.7 0.026 Age*Speed* Target Color 2 1367.3 3.5 0.030 Uniformity Ratio*Surround Ratio*Offset 1 1367.1 7.3 0.007 Uniformity Ratio*Light Source Type*Light Level 4 1367.6 3.0 0.019 Uniformity Ratio*Light Source Type* Target Color 4 1367.1 2.5 0.039 Uniformity Ratio*Light Source Type*Offset 2 1367.3 4.5 0.012 Uniformity Ratio* Target Color*Offset 2 1366.9 5.3 0.005 Surround Ratio*Light Source Type*Light Level 4 1367.5 2.7 0.028

72 Source Num DF DF Den F Ratio P Value Light Source Type* Target Color*Offset 4 1367 3.7 0.005 Light Level*Speed* Target Color 4 1367.6 35.6 <.0001 Light Level*Speed*Offset 2 1367.3 4.6 0.010 Light Level* Target Color*Offset 4 1367.3 20.1 <.0001 Speed*Target Color*Offset 2 1367.1 12.1 <.0001 Interactive Effect of Light Source Type, Light Level, and Uniformity Ratio The combined effects of light source type, light level, and uniformity ratio on detection distance are summarized in this section. Uniformity ratios across the LED light sources and light levels were not different. An increase in light levels across all the light sources and uniformity ratios resulted in longer detection distances (see Figure 39). There were also no statistical differences between light types across the uniformity ratios and light levels. Note: Values are means of detection distances and error bars indicate standard errors. Figure 39. Interactive Effect of Light Source Type, Light Level, and Uniformity Ratio on Detection Distance Interactive Effect of Light Source Type, Light Level, and Surround Ratio There were no statistical differences between the surround ratios across the LED light sources and light levels. There were also no statistical differences between LED light sources across the surround ratios and light levels. At medium and high light levels under high surround ratio, 4000K LED had slightly higher

73 detection distances (see Figure 40). Increases in light levels resulted in an increase of the detection distances across all the light types and surround ratios. Note: Values are means of detection distances and error bars represent standard errors. Figure 40. Interactive Effect of Light Type, Light Level, and Surround Ratio on Detection Distance of Targets The luminance and the contrasts of the targets in both surround ratios changed as the vehicle approached the target locations because of increased illumination from the vehicle headlamp. Target luminances were slightly higher in the higher surround ratio when compared to the lower surround ratio (see Figure 41). The targets were initially rendered in negative contrast and transitioned into positive contrast as the vehicle approached the target location (see Figure 42). The distance at which the change in the contrast polarity occurred, where the target passed through a phase of contrast neutrality, depended on the prevailing light level. The higher the light level, the closer to the target was to the vehicle when contrast neutrality occurred (see Figure 42).

74 Figure 41. Change in Target Luminance as the Vehicle Approaches the Target Under Different Light Levels Figure 42. Change in Target Contrast as the Vehicle Approaches the Target Under Different Light Levels

75 The lack of significant differences between the detection distances at each surround ratio could be explained by the absence of major differences between the contrasts of the targets at detection (see Figure 43). At each light level, the contrasts between the surround ratios were of the same polarity. However, with increasing light level, the targets were detected from farther and, as a result, the contrast at which they were detected also differed widely in magnitude and polarity (see Figure 43). Targets in the highest light level were detected from the farthest when they were rendered in negative contrast (see Figure 43). At the medium light level, the targets were still rendered in negative contrast but were much lower in magnitude than the highest light level and, as a result, were detected from shorter distances. In the lowest light level, the targets were detected when they were rendered in positive contrast at the shortest detection distance (see Figure 43). Figure 43. Average Target Contrasts at Detection at Each Light Level Under Different Light Levels Interactive Effect of Light Type, Uniformity Ratio, and Target Color There were no significant differences between LED light sources across the uniformity ratios and target colors. There were also no significant differences between uniformity ratios across all the LED light sources and target colors. At the higher uniformity ratio for 4000K LED, blue-colored targets had longer detection distances than gray- and red-colored targets (see Figure 44). Similarly, for the 3000K LED, blue-colored targets had longer detection distances than gray-colored targets under the high uniformity ratio.

76 Note: Values are means of detection distances and error bars represent standard errors. Figure 44. Interactive Effects of Light Source Type and Target Color at the High Uniformity Ratio The luminances and the contrasts of all colors of targets changed as the vehicle approached their locations (see Figure 45). Blue and red targets were initially rendered in negative contrast and transitioned to positive contrast as the vehicle approached their location. The gray target was always rendered in positive contrast. Figure 45. Change in Target Luminance and Contrast Across the Three Colored Targets as the Vehicle Approached the Target 0 50 100 150 200 250 300 Blue Gray Red D et ec tio n D is ta nc e (f t.) Target Color LED 3 K LED 4 K LED 5 K

77 On average, at detection, the blue and red targets were rendered in negative contrast, and the gray target was rendered in positive contrast (see Figure 46). The blue target rendered in higher negative contrast could have resulted a longer detection distance than the gray target that was rendered in positive contrast. Further, the contrast of the blue target was higher in magnitude than the red target (see Figure 46) at detection, which could have resulted in the blue target having a longer detection distance than the red target. Figure 46. Average Contrasts of the Blue-, Gray-, and Red-Colored Targets at Detection Interactive Effect of Light Level, Speed and Offset Significant differences in the detection distances were observed at the lowest light level across both offset distances; where the 35 mi/h speed had longer detection distances than the 55mi/h speed (see Figure 47). At the medium light level, these differences in detection distances across lower and higher speed were only observed for the targets on the right-hand side (see Figure 47); at the highest light level, there were no differences between detection distances across either speed (see Figure 47). Note: Values are means of detection distances and error bars represent standard errors. Figure 47. Interactive Effect of Light Level, Speed and Offset on Detection Distance of Targets

78 Interactive Effect of Age and Light Level At all the light levels, younger drivers had significantly longer detection distances than older drivers (see Figure 48). For both age groups, the differences between high and low, and high and medium light levels were statistically significant (see Figure 48). Note: Values are means of detection distances and error bars represent standard errors. Figure 48. Interactive Effect of Age and Light Level on Detection Distance of Targets Target Detection Task – Safe Stop Analysis All significant MMLR results are summarized in Table 14. The main effects of light level, age, offset, and speed were significant. Table 14. Significant Statistical Results from the MMLR Analysis of Type of Stop of Targets Under LED Light Source Comparison Effect Num DF Den DF F Value P Value Light Level 2 1742 3.9 0.021 Age 1 1742 19.5 <.0001 Offset 1 1742 4.7 0.031 Speed 1 1742 148.8 <.0001 The odds ratio estimates of each of the significant factors and the 95% confidence intervals are shown in Table 15. Similar to the pedestrian detection task, the odds of detecting the targets from a safe distance were 1.6 times higher in high light level compared to medium and low levels. Younger drivers were almost four times more likely to detect the target from a safe distance than older drivers. Targets on the left were 1.4 times more likely to be detected from a safe distance than those on the right. Targets were almost 20 times more likely to be detected from a safe distance when drivers were travelling at 35 mi/h compared those travelling at 55 mi/h. 0 50 100 150 200 250 300 Older Younger D et ec tio n D is ta nc e (f t.) Age Low Medium High

79 Table 15. Odds Ratios of Significant Factors in MMLR Analysis of Detection of Pedestrians Independent Variable Level Reference Level Odds Ratio 95% Confidence Limits Light Level High Medium 1.6 1.1 2.3 High Low 1.6 1.1 2.3 Age Younger Older 3.8 2.1 6.8 Offset 2 ft. Left 2 ft. Right 1.4 1.0 1.9 Speed 35 mi/h 55 mi/h 20.2 12.5 32.8 Visual Performance across LED, HPS and Unlighted Conditions Pedestrian Detection Task – Detection Distance Analysis All significant LMM results are summarized in Table 16. The main effects of age, light source type, and offset were significant. There were no significant differences between any of the light sources. Table 16. Significant Statistical Results from the LMM Analysis of Detection Distance of Pedestrians Under LED and HPS Light Source Comparison Source Num DF Den DF F Ratio P Value Age 1 18.2 14.0 0.002 Light Source Type 4 69.7 48.4 <.0001 Offset 1 351.2 39.2 <.0001 The only significant differences were the differences between each light source and the unlighted condition. The unlighted condition had the lowest detection distance when compared to all other light sources (see Figure 49). Older drivers (M = 431.46 ft [131.51 m]) had shorter detection distances than younger drivers (M = 579.10 ft [176.51 m]), and pedestrians at a 2-ft offset (M = (567.65 ft [173.02 m]) distance had longer detection distances than those at 10-ft offset (M = 442.91 ft [135.00 m]).

80 Note: Values are means of detection distances and error bars represent standard errors. Figure 49. Effect of Light Source Type on the Detection Distance of Pedestrians Pedestrian Detection Task – Safe Stop Analysis All significant MMLR results are summarized in Table 17. All the main effects were significant. Table 17. Significant Statistical Results from the MMLR Analysis of Type of Stop of Pedestrians Under LED and HPS Light Source Comparison Effect Num DF Den DF F Value P Value Light Type 4 396 17.2 <.0001 Age 1 396 9.0 0.003 Clothing Color 3 396 3.5 0.015 Offset 1 396 5.4 0.021 Speed 1 396 55.2 <.0001 The odds ratio estimates of each of the significant factors and the 95% confidence intervals are shown in Table 18. Detection of pedestrians at a safe stopping distance were higher in the lighted conditions than in the unlighted conditions. There were no statistical differences between the types of light sources used in the study in terms of detection of pedestrians from a safe distance. Younger drivers were 10 times more likely to detect the pedestrian from a safe distance than older drivers. Pedestrians in red-colored clothing were almost three times as likely to be detected from a safe distance as those in black-colored clothing. Interestingly, the odds of detecting a pedestrian in blue clothing were lower than the odds of detecting a pedestrian in black clothing. Pedestrians at an offset of 2 ft were two times more likely to be detected from a safe distance than those at 10-ft offset. Drivers travelling at the 35 mi/h speed limit were 94 times more likely to detect a pedestrian from a safe distance than those travelling at 55 mi/h.

81 Table 18. Odds Ratios of Significant Factors in MMLR Analysis of Detection of Pedestrians Independent Variable Level Reference Level Odds Ratio 95% Confidence Limits Light Type 3000K LED Unlit 45.1 11.2 181.4 4000K LED Unlit 259.6 55.6 >999.9 5000K LED Unlit 225.9 27.0 >999.9 2100K HPS Unlit >999.9 143.9 >999.9 Age Younger Older 9.9 2.2 44.5 Color Blue Black 0.7 0.3 1.5 Gray Black 1.0 0.4 2.5 Red Black 2.9 1.2 7.4 Offset 2 ft. 10 ft. 2.1 1.1 4.1 Speed 35 mi/h 55 mi/h 94.5 28.4 314.9 Target Detection Task All significant LMM results are summarized in Table 19. Only the main effects of age and light source type were significant. Table 19. Significant Statistical Results from the LMM Analysis of Detection Distance of Targets Under LED and HPS Light Source Comparison Source Num DF Den DF F Ratio P Value Age 1 15.2 11.3 0.004 Light Source Type 4 67.8 4.5 0.003 Of all the light sources, 3000K LED had the lowest detection distance, and the differences between 3000K and 4000K LED were statistically significant (see Figure 50). The detection distance difference between 4000K LED and unlighted conditions was also statistically significant. Interestingly, the differences between 4000K LED, 5000K LED, and HPS light sources were not statistically significant even though HPS had the highest detection distance (see Figure 50). The detection distance difference between HPS and unlighted conditions was also not statistically significant.

82 Note: Values are means of detection distances and error bars represent standard errors. Figure 50. Effect of Light Source Type on the Detection Distance of Targets Target Detection Task – Type of Stop Analysis The MMLR model for the type of stop for targets did not converge. Visual Performance across Uniformity Ratios of LED and HPS Light Sources For both pedestrian and target detection LMMs, the effect of the uniformity ratio on detection distance was not significant. Discomfort Glare Perception across LED Light Sources All significant LMM results are summarized in Table 20. Only the main effects of light level and a two- way interaction involving age and light source type were significant. Table 20. Significant Statistical Results from the LMM Analysis of Discomfort Glare Perception Under LED Light Sources Source Num DF Den DF F Ratio P Value Light Level 2 203.2 6.0 0.003 Age*Light Type 2 38.3 4.8 0.014 For the two interactions involving age and light source type, there were no statistical differences observed between the older and younger drivers across the LED light sources. There were also no differences in the glare ratings across each light type for each age group. Increasing in the light level resulted in an increase in the glare rating with the only significant difference being between high and low light levels with the high light level having a higher glare rating than the low light level (see Figure 51).

83 Figure 51. Effect of Light Level on Discomfort Glare Rating Discomfort Glare Perception across LED and HPS Light Sources The LMM analysis revealed that none of the factors were significant, which suggests no differences between LED light sources and the HPS light source. Glare ratings were not measured in the unlighted condition. Discussion The goals of this study were to determine whether a drivers’ visual performance (measured using pedestrian and target detection tasks) and discomfort glare perception (measured by a rating scale) were affected by different types of light sources (3000K, 4000K, 5000K LEDs, and HPS), light levels, surround ratios, and uniformity ratios. Five major findings were evident. First, there were no major statistical differences in the visual performance across any of the light sources. Second, increasing the light level increased visual performance. Third, higher surround ratios increased drivers’ visual performance. Fourth, uniformity ratios did not seem to influence drivers’ visual performance. Finally, none of the light sources was a major source of discomfort glare for drivers. Effect of Light Type In both the pedestrian and target detection tasks, there were no statistical differences between the detection distances of drivers or drivers’ ability to detect the pedestrians or targets from a safe distance across all the light sources used in the study. The only statistically significant differences in the visual performance of pedestrian and target detection were those between the lighted and unlighted conditions. There was, however, a practical difference for 4000K, which had greater detection distances especially for pedestrians, than any of the other light sources. The detection distances were statistically longer under every light source when compared to the unlighted condition. Similarly, the odds of detection from a safe distance were higher under every light source than under the unlighted condition. There were some differences in the detection of targets; detection distances of targets under the 3000K LED were the lowest and were not statistically different from the detection distances in the unlighted condition. Overall for pedestrian detection tasks, detection distances under 4000K LED were slightly higher, albeit not statistically significant, especially in the higher surround ratio and higher speed conditions. These results

84 could indicate the 4000K LED light could increase visual performance of drivers at higher speeds. At the medium light level, HPS light sources had higher detection distances, but this was not statistically different from the detection distances under LED light sources like 3000K, 4000K, and 5000K LED. The results indicate that the SPD of the light source does not significantly influence driver visual performance at speeds greater than 35 mi/h. Effect of Light Level Increases in the light level increased visual performance, as evidenced by the increase in detection distances. Increases in the light level also increased the odds of detection from a safe distance. This result is consistent with earlier research on the effects of illuminance on visual performance (Bhagavathula & Gibbons, 2015; Bhagavathula, Gibbons, & Nussbaum, 2018; Fotios & Cheal, 2009; Gibbons, Terry, Bhagavathula, Meyer, & Lewis, 2016; Gibbons, Edwards, Williams, & Andersen, 2008). The increase in visual performance with the increase in light level was observed across both detection tasks. Increase in the visual performance with the increase of light level (especially for detecting the pedestrians at the 2-ft offset location and all target locations) was likely because the change increased the negative contrast in which both the pedestrians and targets were rendered. Further, the increase in the light level increased the luminance on the pedestrians and targets and affected the distance at which the contrast neutrality occurred. It is important for the contrast polarity to occur farthest from the object location or not to occur at all because during contrast polarity, the object goes through contrast neutrality (contrast is zero because the target luminance is equal to the background luminance) at which point the object becomes invisible to the driver. If this transition occurs closer to the object, it gives the driver less time to react than when it occurs farther from the object. The increase in light level increases the amount of visual information a driver can gather from the environment and can result in better visual performance. Most of the significant differences between the light levels were between high and low, and high and medium. There were no differences between medium and low light levels; this could be because of differences in average luminance across medium and low light levels. The luminance differences between medium and low light levels was only 0.3 cd/m2 and was likely not enough to produce a statistical difference in visual performance. The differences between average luminance for the high to low, and high and medium light level comparisons were 0.8 and 0.5 cd/m2, respectively, which could have produced statistically significant results. The increase in light level also increased the detection distances at 55 mi/h and the low surround ratios, especially for the pedestrians. These results indicate that on higher speed roadways, higher light levels aid driver visual performance and could potentially aid pedestrian safety. While pedestrians are not typical on higher speed roadways, it should be noted that the same benefit is expected for an animal crossing. Effect of Surround Ratio Surround ratios significantly affected driver visual performance. Increases in the surround ratio increased visual performance as evidenced by longer detection distances and increased odds of detection from a safe distance. The effect of surround ratio was predominantly observed in the pedestrian detection task because it was the only task that had the furthermost offset presentation (10 ft). The increase in detection under higher surround ratios was observed across all LED light sources but was more pronounced under the 4000K LED for the pedestrian detection task at an offset distance of 10 ft and at the higher speed (55 mi/h). Higher surround ratios increase the amount of light incident outside the roadway, and this additional illumination could help drivers’ gather more visual information from areas adjacent to the roadway. Also, at higher speeds, the cone of drivers’ field of vision narrows (US Department of Transportation, 2015), and additional illumination in the high surround ratio seems to benefit the driver based on the results of this study.

85 High visual performance in the higher surround ratios (especially at the 2-ft offset location) could be attributed to both pedestrians and targets being rendered in negative contrast. Existing research (Adrian, 1989b; Aulhorn, 1964; Bhagavathula et al., 2018) shows that objects rendered in negative contrast were detected sooner or from farther away than those in rendered in positive contrast. Effect of Uniformity Ratio Uniformity ratio did not seem to influence driver detection distance in either the pedestrian and target detection task. However, for the pedestrian detection task in the type of stop analysis, results showed that under higher uniformity ratios, drivers were more likely to detect the pedestrian from a safe distance than under lower uniformity ratios. These results indicate the higher uniformity ratios (less uniform roadways) aid the detection of pedestrians at night. It should be noted that the uniformity ratios studied in the project were relatively similar and within the AASHTO recommendations. A larger uniformity gap may yield different results. The lack of difference in the uniformity ratios in the detection distance analysis could be because uniformity of the road affects drivers’ visual comfort more their visual performance. These results also indicate that further research is required to understand the effects of uniformity on visual performance. Perception of Discomfort Glare There were no significant effects of light source type, surround ratio, and uniformity ratio on the perception of discomfort glare. Only light level had a significant effect on the perception of discomfort glare; the increase in the light level increased the discomfort glare rating. However, it is important to note that even at the highest light level, the glare rating was less than “noticeable” (less than 2 on a scale ranging from 0 to 6, where 6 is “intolerable”). Effects of Offset, Speed, Age, and Color An increase in offset distance and speed lowered the drivers’ visual performance as observed by lower detection distances and decreasing odds of detection from a safe distance. This decrease in the visual performance could be attributed to the reduction in the drivers’ field of view with the increase in speed (US Department of Transportation, 2015). Younger drivers had better visual performance than older drivers as evidenced by longer detection distances and higher odds of detection from a safe distance. This result is consistent with existing research in this area (Bhagavathula et al., 2018). The decrease in visual performance among older drivers was likely due to age-related physiological changes in the eyes that lead to reduced visual acuity and contrast sensitivity. Pedestrians in black-colored clothing had the lowest detection distance and the lowest odds of detecting from a safe distance. These results also indicate that pedestrians should refrain from wearing black-colored clothing in nighttime roadway environments to increase their visibility. Effects of Headlamps on Object Luminance and Contrast Pedestrians and targets underwent changes in luminance and contrast as the vehicles approached their locations. The increase in luminance was dependent on the prevailing light level, surround, and uniformity ratio conditions. In general, target luminances increased as the vehicle got closer because targets entered the effective range of the vehicle headlamps. Light levels and surround ratios also greatly influenced the magnitude and polarity of contrast in which pedestrians and targets were rendered. All target locations under all surround ratios and pedestrians at the 2-ft offset locations under high surround ratios underwent changes in the contrast polarity as the vehicle approached their locations. During these contrast polarity transitions, objects become invisible as the luminance of the object became equal to the luminance of the background and the object temporarily was invisible. It is important to minimize these transitions so that drivers can always locate hazards on the roadway.

86 Conclusion The results of the Smart Road testing can be summarized as follows: 1. Surround ratio plays an important role in augmenting the visual performance of the driver. Increasing the surround ratio by illuminating the areas adjacent to the roadway increased the drivers’ visual performance. Increasing the surround ratio also increased the visual performance of drivers of all age groups at both the tested speeds. 2. Increasing the light level also increased the visual performance of drivers. Increasing the light level aided driver visual performance at higher speeds. Higher light levels also increased driver visual performance under the lower surround ratio conditions. 3. The photometric analysis showed that target and pedestrian luminance increased as the vehicle approached their locations. Target and pedestrian contrasts were also affected the by vehicle headlamps and the location of the vehicle with respect to the location of the object. Some pedestrian and target locations also changed the polarity of contrast in which they were rendered (i.e., starting in negative contrast and then transitioning to positive contrast as the vehicle approached the pedestrian or target location). A good roadway lighting design should ensure that this transition happens farther from the pedestrian/target location or not happen at all so that the driver can always see the hazard on the roadway. 4. The photometric analysis also showed that better visual performance in the higher surround ratios and higher light levels resulted from the negative contrast in which the objects were rendered in those conditions. Negative contrasts resulted in better visual performance when compared to positive contrast. Increases in the magnitude of negative contrast also resulted in better visual performance. 5. No major differences were observed between the correlated color temperatures of LED light sources or between the LED and HPS light sources in terms of driver visual performance in most conditions. However, under 4000K LED, the visual performance was the highest at both tested speeds and higher surround ratios. 6. An increase in the uniformity ratio of the roadway also increased the odds of detection from a safe distance. 7. Discomfort glare ratings were mainly affected by the light levels and even at the highest light level evaluated, they were lower than “noticeable.” None of the evaluated light sources (LEDs of all CCTs and HPS) were a significant source of discomfort glare. RESEARCH ROADMAP The following areas of research should be considered to better understand the impacts and benefits of SSL. Areas of funding include NCHRP and FHWA. Metrics for Assessing the Effectiveness of Roadway Lighting, With and Without Headlights to Achieve Adequate Contrast A number of factors are included in this research problem. One of those is what task is being used to assess effectiveness of a lighting system. While the ultimate goal is to reduce crashes and fatalities on the

87 roadways, a street may have a primary task of seeing a pedestrian or cyclist and a highway may have a primary task of remaining on the roadway and identifying geometric changes and off/on ramps. Contrast may be the proper metric for determining effectiveness, but other risk factors must be considered. IES has a task force, including members of this project team, examining this issue. The work of the task force and other work could help in addressing some of these items in future research. Approximate Research Cost - $200,000 Standardization and Asset Management for SSL Replacement to Simplify Maintenance Asset management for roadway lighting systems is done in a number of ways. At times, a system assessment is performed, and data are collected for an entire lighting system and incorporated into a GIS system with complete descriptions of the asset. If an adaptive lighting system is installed, mapping can be done by installing the adaptive lighting system nodes on the luminaires to self-identify location, wattage, operational status, and control scheme. Once this baseline information and mapping are done for the system, structural and electrical assessments can be made for the pole, arms, and distribution system. Strict standardization may be difficult from state to state, but recommendations can be made part of these guidelines and future research into other areas. Approximate Research Cost - $150,000 Potential Revenue Streams from Data Collection and Barriers to their Implementation Several methods of providing revenue streams have been investigated using street lighting assets, including simply considering the street lighting system as real estate that can be leased for equipment like 4G wireless. Other methods look ahead to V2V and V2I systems using the streetlighting assets as a network base for collecting and transmitting data, monitoring a variety of security or urban management systems, and integrating with Smart City components, including water monitoring and other city system management. These methods are more concepts at this point but may become more viable as more technology is deployed. They also depend on the infrastructure in place like pole/breakaway devices, switched or unswitched power, and pole designs. Approximate Research Cost - $50,000 Real-time Asset Management within the Context of ITS Framework Like the potential revenue streams noted above, it is likely that the ITS framework will be integrated with the streetlighting systems and control. In essence, the ITS can serve as an integrated system to maintain inventories of lighting systems and interacting asset classes (e.g., high-mast poles, signs) with geo-spatial locations, monitor key attributes and identify faulty conditions in real-time for corrective actions, track maintenance histories and associated costs, and track life-expectancies of lighting systems. In addition, the real-time data capture has the potential to supply other performance metrics, such as safety, mobility, and performance of interacting asset classes and operating conditions (e.g., wet conditions). The NCHRP 08-36, Task 114 project provides guidance on incorporating ancillary assets into an integrated asset management program and can serve as a starting point to explore real-time management of lighting systems within the ITS framework. Future research will be needed to address integration with SMART City and connected and autonomous vehicle systems. Approximate Research Cost - $70,000

88 Metrics to Characterize the Environment and Health Effects of Roadway Light Spectrum, Amount, Distribution, and Timing A fair amount of research is available on the issue of light at night and the environment and effect on people’s health. Unfortunately, opinions on the results of this research and how recommendations are crafted from it vary. A cohesive plan, directly correlating roadway lighting to safety needs and the impact on health and the environment, would be valuable. The precise limits at which impacts can occur is likely to change as further research in this area is done. Approximate Research Cost - $350,000 Metrics for Assessing Light Depreciation and Means of Determining End of Useful Life Currently LED life is based on the point in time when the lumen output of the source depreciates by 30%. It does not consider the expected life of the equipment or of the drivers and controllers, which have a much shorter life. These components are often the true indicator of when a luminaire will stop operating. Some existing metrics include the use of MTBF data, which may be better suited to SSL. Research in this area to validate this and other metrics would be valuable. Approximate Research Cost - $300,000 Crash Data Analysis of HID Technologies vs SSL Technologies A review of historical crash data for various roadway types, interchanges, intersections, and roundabouts to determine if any changes in day-to-night crash rates occur with the use of SSL lighting is required SSL luminaires have not been in the field long enough that statistical analyses may show differences. Approximate Research Cost - $100,000

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Providing light beyond the limits of the roadway travel lanes benefits drivers’ visual performance, spectral content of light-emitting diode (LED) sources should be a design consideration, and there are not currently any health impacts from properly designed roadway lighting are among the findings of this survey report.

The TRB National Cooperative Highway Research Program's pre-publication draft of NCHRP Research Report 940: Solid-State Roadway Lighting Design Guide: Volume 2: Research Overview determines the current guidance for the use of Solid State Lighting (SSL); identifies the research that still needs to be accomplished to assist in its proper implementation; and develops a comprehensive, easy to use, set of guidelines using currently available information and new research being proposed as part of this project.

Also see this guide's accompanying pre-publication draft, NCHRP Research Report 940: Solid-State Roadway Lighting Design Guide: Volume 1: Guidance.

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