LED Roadway Lighting: Impact on Driver Sleep Health and Alertness (2021)

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22 To address the project goals, two separate experiments were conducted: the Corneal Illu- minance Dosage Experiment and the Driver Sleep Health and Alertness (DSHA) Experiment. In the Corneal Illuminance Dosage Experiment, corneal illuminance was measured from roadway lighting sources with different spectral power distributions (LED and HID), consumer LED devices (tablets, televisions, and phones), and vehicle headlamps (HID and LED types). This experiment sought to determine typical levels of corneal illuminance and durations of exposure from roadway light sources of varying spectral power distributions (SPDs). The con- sumer electronic devices served as useful comparisons to street and vehicle lights. The results of this experiment were intended to determine the light levels and durations of exposure to be considered in the DSHA experiment. In the DSHA experiment, the effects of an LED lighting source and its associated light levels (measured by the corneal illuminance) and a traditional HID source (HPS) on the sleep health and alertness of drivers were investigated in a realistic nighttime roadway scenario. A scenario with no roadway lighting served as a negative control. Sleep health was measured based on melatonin levels in plasma and saliva. To measure driver alertness, the team considered reaction time, PERCLOS, and SDLP as objective measures and KSS as a subjective measure. Corneal Illuminance Dosage Experiment The goal of this experiment was to determine the typical levels of corneal illuminance from roadway lighting sources with different SPDs. These levels were compared to those experienced after similar exposure durations to consumer devices and vehicle headlamps (see Table 3). Lighting Type Corneal illuminance was evaluated for five different roadway lighting sources. Two of these light sources (2100  K HPS and 4000  K LED) are used conventionally in roadway lighting. Another three (2200 K, 3000 K, and 5000 K LED) are correlated color temperature (CCT) LEDs currently used in some regions of the United States for roadway/street lighting. The final condi- tion was a negative control (no roadway lighting). The light levels on the Smart Road (average roadway surface luminance) across all lighting conditions (except no lighting) were matched at 1.0 cd/m2 (approximate corneal irradiance of 2.28 µW/cm2). This is the highest level prescribed by IES North America for expressways. The uniformity ratio (UR) (average to minimum illu- minance) of the roadway for the 2100 K HPS was 7.0 and for all LEDs was 4.5. All of the LED luminaires were made by the same manufacturer and were of the same model. The Smart Road is located on the campus of the Virginia Tech Transportation Institute (VTTI) and is built C H A P T E R   3 Methods

Methods 23   to Virginia Department of Transportation (VDOT) and FHWA standards. The Smart Road contains a variable lighting testbed comprising 38 overhead light poles spanning a 1.1-mile section of the road. Combined with individual light control from the Smart Road Control Room, the pole-spacing pattern (40-20-20-40-40-20-20-40-40-20-20 meters) allows the evaluation of lighting systems with a spacing of 40, 60, 80, or 120 m. For the current study, a pole spacing of 80 m and a mounting height of 15 m were used. To add more validity to the corneal illuminance measurements, the research team collected lighting data (illuminance and luminance) using the Roadway Lighting Mobile Measurement System (RLMMS) to serve as reference light levels from public roads in the Commonwealth of Virginia. Lighting data were collected from a sample of roadway function classes (e.g., local, collector, arterial, freeway, and interstate). Only illuminance and luminance data from different roadway function classes were collected from all nine VDOT divisions (Figure 3). Lighting data were collected only on lighted roadways and aggregated over the entire lighted sections. Lighting data from unlighted locations were not used in the aggregation. Equipment Experimental Vehicle Devices Illuminance and luminance were measured using the RLMMS developed by VTTI, which enables rapid, accurate, and low-cost photometric measurement of lighting (Gibbons, Meyer, & Edwards, 2018; Jaskowski & Tomczuk, 2019). The system’s conceptual layout is shown on the left side of Figure 4. A four-armed apparatus is placed on top of the vehicle with four water- proof Minolta illuminance detector heads. Each head is positioned on the end (facing upward) of each of the four arms of the apparatus, also known as the “Spider.” This arrangement allows roadway illuminance to be measured at three positions in each lane with a redundant measure- ment (front and rear). These positions correspond to the left track, center of the lane, and the Independent Variable Types Roadway Lighting Type No roadway lighting, 2200 K LED, 3000 K LED, 4000 K LED, 5000 K LED, and 2100 K HPS Consumer Devices E-Reader (with and without night mode) Tablet (with and without night mode) Television Table 3. Experimental variables in the Corneal Illuminance Dosage Experiment. Figure 3. Data collection locations in Virginia.

24 LED Roadway Lighting: Impact on Driver Sleep Health and Alertness right track, and are approximately 32 in. (0.81 m) apart across the lane. Positioned in the center of the four arms is a NovaTel global positioning system (GPS) unit. A fifth Minolta illuminance meter is mounted to the forward windshield of the vehicle to detect oncoming glare (right side of Figure 4). The spectroradiometer could not be used for these measurements due to equipment malfunction. Consumer Electronic Devices For comparison with levels of corneal illuminance experienced while using a consumer elec- tronic device, a personal light dosimeter (see Figure 5) and an illuminance meter measured light from typical consumer devices (e-reader, tablets, and television) that use LED technolo- gies, as shown in Table 3. The personal corneal irradiance dosimeter recorded the dosage of lighting in multiple wavelength ranges within the visible spectrum. The sensor uses multiple band-pass filters to measure light within specific wavelength ranges. The calibrated sensor out- puts in µW/cm2 with a resolution of up to 45 counts per µW/cm2. The sensor was mounted on a pair of eyeglass frames (no lenses) between the eyes near the top of the nose, and had a cosine corrector optic to ensure accurate measurement. The embedded processor and storage were placed on a belt clip and battery powered. For the phones, tablets, and televisions, two exposure conditions were analyzed. The first condition was intended to be the most biologically potent. The screen color was set at white in the brightest level achievable with the device without the night mode being activated. The second condition was intended to be the least biologically potent. The screen brightness was at the lowest possible setting with the night mode (reduced Figure 4. Schematic of VTTI’s RLMMS (left) and vertical illuminance, color camera, and illuminance camera (right). (CAN = Controller Area Network)

Methods 25   blue content in the display’s spectral power distribution) on the device being activated for the same white-colored screen as the first condition. The duration of exposure was the same as in other conditions (2 hours). Experimental Procedure Corneal illuminance measurements were conducted for a wide range of roadway lighting sources based on the spectral power distributions observed in realistic roadway conditions and from LED consumer devices. The duration of exposure in this experiment was 2 hours, based on an ongoing DOE project by VTTI and Thomas Jefferson University (TJU) research teams (DOE Contract DE-EE0008207), and other published research (Nagare, Plitnick, & Figueiro, 2018; Wood et al., 2013). Naturalistic Driving Exposure For the naturalistic driving portion of the experiment, a research team member wore a VTTI-developed personal light dosimeter and drove an experimental vehicle—a 2016 Ford Explorer—on the Virginia Smart Road for 2 hours under different roadway lighting sources. Consumer Device Exposure In this portion of the experiment, a personal light dosimeter and an illuminance meter sepa- rately measured LED consumer devices (television, tablet computer, and a mobile phone) for the same duration of 2 hours. The experiment was conducted in conditioning rooms at VTTI from which extraneous light sources had been eliminated. Both meters were located at a dis- tance of 1 m from each screen. Daily Exposure Measurements Additionally, 10 VTTI employees volunteered to wear the personal light dosimeter for 1 day (24 hours) to determine the typical corneal illuminance exposure received from light sources encountered daily. This task provided information on the range of corneal illumi- nance exposures during a typical 24-hour period. Figure 5. Dosimeter glasses worn during experimental sessions.

26 LED Roadway Lighting: Impact on Driver Sleep Health and Alertness Driver Sleep Health and Alertness Experiment In this experiment, the effect of an LED roadway lighting type at three corneal illuminances on subjective and objective measures of driver sleep health and alertness were assessed, to determine the corneal illuminance that produces a measurable effect. The same objective and subjective measures of driver sleep health and alertness were also measured for a positive con- trol, a negative control (no roadway lighting), and a traditional HID source (a single corneal illuminance matched to that of an LED source). In the current experiment, driver sleep health was assessed based on melatonin levels in the plasma and saliva. Exposure to bright light can suppress melatonin; thus, melatonin levels can serve as a separate quantitative index related to sleep health. Driver alertness was determined objectively by measuring reaction time, PERCLOS, and SDLP, and subjectively based on KSS. Positive laboratory and negative roadway controls were established for comparison with the other roadway experimental scenarios. The DSHA experi- ment utilized a within-subjects, repeated-measures design to include 10 healthy male and female volunteers between the ages of 18 and 30 years. All participants were required to have steady sleep-wake patterns, a valid driver’s license, and a binocular visual acuity of 6/12 (20/40). The participants were exposed to outdoor lighting environments at light levels that are typically encountered on the Virginia Smart Road. Each participant drove one of the two identically instrumented experimental vehicles (2016 Ford Explorers) equipped with data acquisition systems (DASs) connected to the vehicles’ Controller Area Network (CAN) and the camera systems onboard the vehicles. The DAS col- lected data from the vehicle’s CAN system, including vehicle speed, differential GPS coordinates, four video images (driver’s face, forward roadway, left side of the roadway, and right side of the roadway), audio from the driver, manual button presses, and input from the in-vehicle experimenter. The DAS was also equipped with a VTTI-developed lane tracker called Road Scout. Road Scout is a custom machine-vision process that grabs video frames from the forward camera feed. Rather than being stored, the grabbed video frames are processed algorithmically in real time to calculate vehicle position relative to road lane markings. Experimental Design A within-subjects, repeated-measures experimental design was used to assess the effects of spectral power distribution, corneal illuminance or intensity, and exposure duration on the objective and subjective measures of sleep health and alertness. The independent variables are listed in Table 4. Lighting Type Two roadway lighting types were evaluated in the DSHA experiment. The most common LED light source currently used for lighting roadways in the U.S. is 4000 K LED; 2100 K HPS Independent Variable Levels Light Condition 2100 K HPS–High (1.5 cd/m2) 4000 K LED–High (1.5 cd/m2) 4000 K LED–Medium (1.0 cd/m2) 4000 K LED–Low (0.7 cd/m2) No roadway lighting–(less than 0.05 cd/m2) Exposure Time 1 AM to 3 AM (Five saliva samples collected at 30-minute intervals) Table 4. Independent variables and their experimental values.

Methods 27   is the traditional HID roadway lighting source. These two light sources have different SPDs (see Figure 6), which allowed the research team to assess the impacts of two SPD distributions on the objective and subjective measures of driver sleep health and alertness. A no-roadway- lighting condition was used as a negative control. Corneal Illuminance Corneal illuminance affects melatonin suppression in humans and subsequently sleep health (Brainard et al., 1988; Stevens et al., 2013). Corneal illuminance has also been shown to affect nocturnal alertness (Cajochen et al., 2005; Lockley et al., 2006). Participants were exposed to three illuminances to measure the effects of corneal illuminance on driver sleep health and alertness. Three light levels were identified based on light levels achievable on the Virginia Smart Road and IES RP-8-18–specified light levels. The lighted section of the Virginia Smart Road has both concrete and asphalt sections, so using variable dimming methods for both these sections, the average luminance was matched across both pavement types. A high light level of 1.5 cd/m2 average luminance was selected, which is 25% higher than the average luminance specified for major streets with high pedestrian activity classification (1.2 cd/m2) and 50% higher than the average luminance specified for expressways (1.0 cd/m2). It should be noted that 1.2 cd/m2 is the highest light level recommended by IES RP-8-18 for any kind of roadway. A medium light level of 1.0 cd/m2 and a low light level of 0.7 cd/m2 were selected and could be achieved across all pavement types. The HPS lighting was matched to the highest light level of the 4000 K LED rather than the lowest light level, as the lowest level could not be achieved on the concrete section of the Smart Road even at the lowest dim setting. Exposure Time The duration of light exposure is a major factor in determining the level of melatonin secretion in humans (Brainard et al., 1997). Participants were exposed to the chosen light- ing conditions for 2 hours to understand the effects of exposure time on sleep health and alertness. The duration of exposure was selected based on the VTTI and TJU research teams’ ongoing DOE project (DOE Contract DE-EE0008207), which is evaluating the effect of dif- ferent sources of roadway lighting on melatonin suppression of three cohorts of road users— drivers, pedestrians, and residents—and on other published research (Nagare, Plitnick, & Figueiro, 2018; Wood et al., 2013). 0 0.25 0.5 0.75 1 400 450 500 550 600 650 700 750 800 R el at iv e En er gy Wavelength (nm) 4000 K LED 2100 K HPS Figure 6. SPDs of the light sources used in this study.

28 LED Roadway Lighting: Impact on Driver Sleep Health and Alertness Experimental Approach Participants were recruited from the public to be tested at VTTI under the selected experi- mental conditions. In the positive laboratory control, a high-intensity LED luminaire at close range was used to suppress the dim-light melatonin onset and peak melatonin secretion. After completion of control testing, the realistic lighting scenarios were carried out by exposing par- ticipants to each of the test conditions while they drove on the Smart Road. During the expo- sures, participants’ melatonin levels were quantitatively determined at 30-minute intervals. In addition to melatonin levels, participants’ reaction time (as measured by detection distances of objects on the roadway), PERCLOS, SDLP, and self-reported measures of drowsiness using the KSS were measured at 30-minute intervals as measures of driver alertness. Participants Ten people were recruited to participate in the experiment. The participant sample was gender-balanced and included participants between the ages of 18 and 30 years. Participants were instructed to maintain a normal sleeping schedule, avoid substances containing alcohol and caffeine (after midday), and refrain from napping after 6:00 PM on the day of the study. Participants were also nonsmokers. Participants were required to have steady sleep-wake pat- terns to control for confounding effects. Participants had to have a valid driver’s license and a binocular visual acuity of at least 6/12 (20/40). Visual acuity was measured using an Early Treatment Diabetic Retinopathy Study chart with an illuminator cabinet. There was at least 1 week between participants’ exposure to different lighting conditions. All experimental sessions were completed at night and only in clear weather conditions. Participants were paid for their project-related activities at the rate of $30 per hour. All project activities were approved by the institutional review boards of Virginia Tech and TJU. Participants’ sleep-wake cycles were surveyed for a week before participation in an experi- mental session. The monitoring was performed using sleep logs and actigraphy. Actigraphy was performed using a wrist-worn sensor that tracked sleep-wake cycles and participants’ sleep (see Figure 7). The participant wore the sensor throughout the experiment, Figure 7. ActiGraph wGT3X-BT actigraphy monitor used during the study.

Methods 29   and the data were reviewed at the beginning of each experimental session. This ensured that each participant had maintained a normal sleep cycle throughout the experimental period. If the sleep cycle had not been maintained, a participant session was postponed. Previous studies (Cellini et al., 2013; Takano, Boddez, & Raes, 2016) have confirmed the validity and reliability of the actigraphy monitor model used in this study to estimate sleep-wake cycles. Participants were recruited using a telephone screening based on the protocol developed as part of the institutional review board submission. The participants were screened to ensure they met the requirements of the study. After recruitment, the participants were scheduled to take part in the positive control group and then participated in the naturalistic exposure testing. Each participant encountered one light type and level in each of the five experimental sessions, which were separated by a min- imum of 1 week. Two participants were scheduled for each experimental session. The presentation of the light sources and levels was counterbalanced across the participants to account for order-related confounding effects. Dependent Measures Melatonin Sampling—Sleep Health. The team employed two approaches for melatonin sampling. Blood and saliva were collected during the positive laboratory control session, and saliva was collected during all driving experiments. A certified phlebotomist was hired to perform the plasma sampling. Blood and saliva were also sampled as a part of the team’s DOE project. In the positive control session, blood samples (2–3  ml) were collected every 30  minutes through an indwelling intravenous catheter located in a forearm vein. Plasma was separated by refrigerated centrifugation and stored at −20°C until assay. This routine sampling technique was frequently employed by the TJU team members (Brainard et al., 2001). Saliva samples (1–2 ml) were taken at the same time as the blood draws and served as a backup for measuring plasma melatonin, should the indwelling intravenous line fail to flow. During the naturalistic testing conditions only saliva samples were collected, using a Salivette sampling device. After collection, the Salivette was centrifuged and stored at –20°C. Again, this sampling technique is routinely employed by the TJU team members (Hanifin et al., 2019; Lockley et al., 2003). Blood samples were analyzed for melatonin by radioimmunoassay by SolidPhase, Inc. (Portland, ME) using the Kennaway G280 antibody (Vaughan, 1993). Blood sampling is a more sensitive technique for the quantitative evaluation of melatonin (West et al., 2011); saliva sampling is also effective and offers the greatest safety to participants under driving conditions (Voultsios et al., 1997). For this reason, only saliva samples were taken during the driving experiments to minimize the potential for issues in the field. Objective Measures of Driver Alertness. Reaction time has been used to evaluate par- ticipant alertness in past research (Figueiro et al., 2009; Lockley et al., 2006). In this study, participants were asked to detect objects appearing on the roadway in each experimental session. Detection distance, or the distance at which objects were first seen by the participants, was used as a direct measure of participant reaction time. The objects were placed on the Smart Road with matching levels of vertical illuminance and were interspersed with blank presenta- tions. Detection distance was calculated using GPS coordinates from the experimental vehicle at the instant the participant detected the object. In addition to the detection distance, the distance at which the color of the object was recognized was determined. This technique has been used in previously published research (Bhagavathula & Gibbons, 2013; Bhagavathula, Gibbons, & Nussbaum, 2018).

30 LED Roadway Lighting: Impact on Driver Sleep Health and Alertness PERCLOS—the percentage of time a driver’s eyelids are closed over a given period, gen- erally 1 to 3 minutes (Dinges & Grace, 1998; Wierwille et al., 1994)—has been determined to be a reliable objective measure of driver drowsiness (Dinges et al., 1998). PERCLOS was more highly correlated (r = 0.9) with performance lapses on a psychomotor vigilance task than self- ratings of fatigue. When evaluating PERCLOS, eye closure is defined as any frame of the video in which the driver’s eyelids are at least 80% closed and obscuring the pupil (excluding ordinary blinking). A threshold of 12% (i.e., eyelids at least 80% closed for at least 12% of the time) has previously been associated with moderate or greater drowsiness and has been successfully used in drowsiness-detection algorithms (Dasgupta et al., 2013; Hanowski et al., 2008; Wierwille et al., 1994). PERCLOS coding was conducted by trained data reductionists on video data collected from the experimental vehicles driven by the participants during data collection. The data reductionists did not know the participant’s lighting condition for which they were performing PERCLOS coding. PERCLOS coding was performed for this study using video of the driver’s face in which the driver’s eyes were visible (i.e., not occluded by glare, or poor video quality) for at least 75% of the coding period. PERCLOS coding was conducted for all participants’ videos that met this criterion using 3 minutes of video every 30 minutes during the driving task, beginning at 1:15 AM and ending at 2:45 AM. Thus, the PERCLOS segments analyzed for the study are at the following time segments: 1:15 to 1:18 AM, 1:45 to 1:48 AM, 2:15 to 2:18 AM, and 2:45 to 2:48 AM. Before beginning coding, trained data reductionists viewed the video segment at regular speed to become familiar with the participant and their behavior. After familiarizing themselves with the participants’ behavior, the reductionist began to view the video frame by frame (15-Hz capture rate). For each frame, the reductionist selected one of three options: Eyes Open, Eyes Closed, or Unknown. Consistent with the PERCLOS definition, Eyes Closed was operationally defined as the eyelid being at least 80% closed and covering the pupil. Video frames where a driver exhibited squinting were considered Eyes Open if the pupil was visible, to avoid miscoding these cases as drowsy. In situations such as eye rubbing, frequent blinking, and the like, which may or may not represent true drowsiness, video frames were coded as Eyes Closed to avoid subjectivity. After completion of a 3-minute segment, reductionists reviewed their work by watching the video at half-time playback speed to ensure accuracy. If the driver’s eyes were coded Eyes Closed for more than 12% of the valid video frames in a segment, the driver was considered drowsy. PERCLOS coding was conducted on all video segments that met the experiment’s criteria. SDLP has been used in previous driving research as an objective measure of driver drowsi- ness (Gaspar et al., 2017; Louwerens et al., 1987; Owens & Ramaekers, 2009). SDLP is a measure of vehicle control; as drivers become drowsier, their control over the vehicle’s lateral position decreases, and SDLP increases. SDLP values were measured using the Road Scout machine- vision system included in the DAS mounted on each experimental vehicle. SDLP was calculated at 30-minute intervals starting at 1:30 AM for three time segments. SDLP was only calculated when the vehicle was moving and speed was at the specified speed limit (35 mph ±5 mph). The change in SDLP over time indicated the drowsiness level of each participant under different roadway lighting conditions. Subjective Measure of Driver Alertness. Scores on the KSS, a nine-point Likert scale (Table 5) developed to measure sleepiness (Åkerstedt & Gillberg, 1990), have been shown to be closely related to electroencephalographic and electrooculographic activities along with behav- ioral variables (Kaida et al., 2006). In the current study, participants reported their levels of sleepiness on the KSS at regular intervals during data collection. The TJU team has used KSS successfully in some studies assessing subjective alertness (Lockley et al., 2006). KSS was admin- istered every 30 minutes during the driving task beginning at 12:50 AM and ending at 2:50 AM.

Methods 31   Procedure Positive Control Positive control laboratory studies of nocturnal melatonin secretion served as reference points to compare melatonin levels in the experimental driving scenarios on the Smart Road. The objective of the positive control was to strongly suppress the earlier melatonin onset and peak melatonin secretion. Participants were asked to maintain an upright posture with feet on the floor while remaining awake. Participants were allowed to talk to each other, listen to music, or read books. No devices that emitted light (e.g., cell phones, e-readers) were permitted. For the positive control scenario (see Table  6), participants arrived at the test facility at 11:00  PM, where they were exposed to 200  lux of typical indoor residential lighting from 11:00 PM to 1:00 AM. The ambient light levels were then raised to a corneal irradiance of at least 1000 µW/cm2 (3500 lux). From 1:00 AM to 3:00 AM, participants viewed a light-exposure system consisting of a desk-mounted light panel containing a 4000 K solid-state lamp used in the roadway scenario, until their release at 3:00 AM. Plasma and saliva samples were collected every 30 minutes. As shown by the TJU team, 1000 µW/cm2 light exposure should result in the strong suppression of plasma and salivary melatonin (Brainard et al., 2015). Test rooms were set up for the positive control light exposures. A laboratory was designated with space for up to three participants to be exposed at once. Three lighting exposure panels were built and characterized with stands for each of the three adaptation stations (see Figure 8). A comfortable chair and table were provided for each participant. The chairs allowed the par- ticipant to remain vertical during the exposures. In the adaptation stations, the lighting seen by each participant was isolated from the other participants by blackout curtains (see Figure 8). These reached from floor to ceiling and enclosed the ceiling. The room had individual access to each of the stations, which allowed staging of the participants so that a single nurse phlebotomist could take the blood samples. An additional technician collected the saliva samples. Table 5. KSS scale used in the study. Rating Description 9 Extremely sleepy, fighting sleep 8 Sleepy, some effort to keep alert 7 Sleepy, but no difficulty remaining awake 6 Some signs of sleepiness 5 Neither alert nor sleepy 4 Rather alert 3 Alert 2 Very alert 1 Extremely alert Time of Exposure Duration of Exposure Light Level/ Corneal Irradiance Light Source Blood/Saliva Sampling 11 PM to 1 AM 2 hours 200 lux 4000 K LED One blood and saliva sample at 12:30 AM 1 AM to 3 AM 2 hours 3500 lux(1000 µW/cm2) 4000 K LED Blood and saliva at 30- minute intervals Table 6. Positive laboratory control conditions for the DSHA experiment.

32 LED Roadway Lighting: Impact on Driver Sleep Health and Alertness Naturalistic Driving Experiment Once the positive laboratory control sessions were completed, the exposure sessions to determine the effects of roadway lighting on DSHA in a naturalistic environment began. Only one lighting condition was experienced during each experimental session, and at least 1 week passed between experimental sessions. The objective of this experiment was to understand the effect of a light source’s spectral power distribution, duration, and illuminance on objective and subjective measures of driver sleep health and alertness in realistic lighted roadway environments. For the driving exposure experi- ment (see Table 7), participants arrived at 11:00 PM. From 11:00 PM to 1:00 AM, similar to the control conditions, participants were exposed to 200 lux, which is typical of indoor residential lighting. Two participants were scheduled for each experimental session. At 1:00 AM, partici- pants were escorted to the experimental vehicles (both vehicles were identical). The light level during transit was less than 1 lux. Participants were given time to orient themselves to the vehicles and asked to drive on the Smart Road. In each experimental driving session, partici- pants were exposed to a single illuminance condition for a particular light type. The driving task took place entirely in the existing lighted section of the Smart Road (the Lighted Section in Figure 9). The vehicles started at Turn 2 and drove until the eleventh light, then used the entire roadway width to make a U-turn and perform a return trip. This ensured that their exposure to optical radiation remained relatively constant. Figure 8. Desk-mounted light panel for the positive control study. Time of Exposure Duration of Exposure Corneal Irradiance Light Condition Saliva Sampling 11 AM to 1 AM 2 hours 200 lux 4000 K LED One saliva sample at 12:30 AM 1 AM to 3 AM 2 hours 1.8 lux 2100 K HPS–High Saliva samples at 30-minute intervals starting from 1 AM 1.9 lux 4000 K LED–High 1.4 lux 4000 K LED–Medium 1.1 lux 4000 K LED–Low 0.8 lux No roadway lighting Table 7. Lighting conditions for DSHA Experiment.

Methods 33   Participants drove the experimental vehicle from 1:00 AM to 3:00 AM at a speed of 35 mph while using low-beam headlamps. Vehicle speed was actively monitored by the in-vehicle exper- imenter, who was always seated in the back seat of the experimental vehicle. The in-vehicle experimenter was responsible for the participant maintaining the speed limit (35 mph) estab- lished for the study. During the driving portion of the study, participants were given detection tasks to measure their reaction times. Participants were asked to look for and identify targets located on the shoulder of the road. Twelve-inch (0.30 m) letter Cs were used as objects for the detection task (see Figure 10). The targets were made of plywood painted in seven colors (blue, cyan, green, gray, yellow, magenta, and red), and were propped up using small wooden stands. The participants identified which direction the gap was oriented; left, up, or right (see Figure 10). Participants also had to recognize the color of the target. The spectral reflectance of each target color is shown in Figure 11. Saliva samples were collected from participants at 30-minute inter- vals starting at 1:00 AM. Self-reported KSS scores were collected from participants at the same intervals, starting at 1:20 AM. At the end of each driving session (3:00 AM), participants were instructed to stop driving, which signaled the end of the data collection session. During this experiment, participants were not allowed to look at their phones, tablets, or computers, Figure 9. Experimental area layout concept on the Smart Road, not to scale. Figure 10. Examples of targets that participants looked for on the Smart Road shoulder.

34 LED Roadway Lighting: Impact on Driver Sleep Health and Alertness as these activities might have confounding effects on melatonin level and alertness. Overall, participants completed five such driving sessions separated by a minimum of 1 week between sessions. To ensure participant safety, an experimenter picked participants up from their homes before each experimental session, and they were dropped off by an experimenter at the end of each session. Biological Sample Processing Equipment A small laboratory centrifuge was used for processing the plasma and saliva samples. The centrifuge included an automatic speed control and timer so the nurse phlebotomist or labora- tory technician could sample, prepare, and centrifuge simultaneously during the positive con- trols and roadway sampling. The centrifuge included space for eight or more samples and had the necessary adapters for different test tube sizes. On the experiment nights, due to logistical concerns, all of the saliva samples were centrifuged and frozen within 4 hours of collection to prevent melatonin degradation. For storage before analysis, the samples were placed in a −20°C biological freezer acquired specifically for this project. The centrifuge and sample freezer were located in a small access- controlled laboratory adjacent to the positive exposure rooms. The saliva samples collected during the Smart Road experiment required temporary storage until the end of each night, when they were centrifuged and then placed in the −20°C freezer. To prevent degradation, the samples were stored in a biological sample cooler on wet ice. This cooler was stocked with fresh ice and transported to the Smart Road for each experimental night. Blood and saliva samples were analyzed for melatonin by radioimmunoassay by SolidPhase, Inc. (Portland, ME). TJU has used this assay successfully over the past year in several studies. The samples were shipped to TJU monthly and then grouped for shipment to the assay provider via express shipping in insulated packaging with dry ice. The shipping containers were prepared per all required biohazard safety features for ship- ment of blood plasma. The saliva samples were packaged similarly. The lab technicians were trained by the Virginia Tech Environmental Health and Safety and Thomas Jefferson Univer- sity Environmental Health and Safety groups on proper handling and packaging procedures. 0 0.2 0.4 0.6 0.8 1 400 500 600 700 800 Wavelength (nm) Red Gray Blue Green Cyan Magenta Yellow R el at iv e Sp ec tra l R ef le ct iv ity Figure 11. Spectral reflectance of the colored targets used in this study.

Methods 35   Analyses Linear mixed-model (LMM) analysis was used to assess the effects of lighting type, level, and duration on melatonin level, reaction time, PERCLOS, SDLP, and KSS. Overall, six linear mixed models were conducted. For the reaction time analysis, two mixed models were used, one for the detection distance and the other for color recognition distance. For the reaction time model, the lap number was used as a covariate. The use of lap number as covariate allowed the participants’ performance to be captured during each data collection session across all light conditions. This enabled the research team to determine if any lighting condition hindered or helped participants’ alertness. The level of significance was established at p < 0.05. Where relevant, Tukey’s honest signifi- cant difference was used for post hoc analyses.