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Street Lighting Survey for Commercial Areas in

the Municipality of Anchorage

Final Report, October 02, 2009

Prepared by:

Michael Mutmansky, Todd Givler, and Nancy ClantonClanton & Associates

With sections by:

Virginia Tech Transportation Institute, Chris Edwards

Cadmus Group, Alan Lee


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Street Lighting Survey for Commercial Areas in the Municipality of Anchorage

Clanton & Associates Page i

October 2, 2009

Project Management

Michael Barber with the Municipality of Anchorage managed this project. Michael Mutmansky of

Clanton & Associates, Inc. of Boulder, Colorado developed and executed the survey portion ofthe project with the support of Todd Givler and Nancy Clanton. Dr. Ron Gibbons and Chris

Edwards of the Virginia Tech Transportation Institute developed, performed, and reported thevisibility performance tests described in this report. The Cadmus Group, Inc. of Portland,

Oregon performed the statistical data analysis. This team included Allen Lee, Steve Chamberlin,

and Elizabeth Daykin.

Disclaimer

This report was prepared by Clanton & Associates with technical support and assistance from the

Virginia Tech Transportation Institute and the Cadmus Group for the Municipality of Anchorage(MOA). Reproduction or distribution of the whole or any part of the contents of this document

without the express written permission of the MOA is prohibited. This work was performed withreasonable care and in accordance with professional standards. However, Clanton & Associates,

Virginia Tech, Cadmus Group, nor the MOA nor any entity performing the work pursuant to

MOAs authority make any warranty or representation, expressed or implied, with regard to thisreport, the merchantability or fitness for a particular purpose of the results of the work, or any

analyses, or conclusions contained in this report. The results reflected in the work are generallyrepresentative of operating conditions; however, the results in any other situation may vary

depending upon particular operating conditions.


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Clanton & Associates Page ii

October 2, 2009

Table of Contents

1.0 Introduction ....................................................................................... 6

1.1 History and Background ................................................................................................................... 6

1.2 Approach ........................................................................................................................................... 6

1.3

Project Goals and Objectives ............................................................................................................ 7

1.4 Market Potential ................................................................................................................................ 7

1.5 Prior Work ........................................................................................................................................ 7

2.0 Test Procedures ................................................................................. 9

2.1 Experiment Setup .............................................................................................................................. 9

2.2 Luminaires Utilized........................................................................................................................... 9

2.3 Light Sources .................................................................................................................................. 11

2.4 Subjective Survey ........................................................................................................................... 12

2.5 Objective Performance Visibility Test ......................................................................................... 12

2.6 Photos .............................................................................................................................................. 13

3.0

Results ............................................................................................. 15

3.1 Subjective Survey ........................................................................................................................... 15

3.2 Objective Visibility Study ............................................................................................................... 20

3.3 Detection Distance and Design Stopping Distance ......................................................................... 23

3.4 Calculated Lighting Results ............................................................................................................ 25

3.5 Energy Implications ........................................................................................................................ 27

3.6 Applications Interpretation.............................................................................................................. 32

3.7 Dimming Controls .......................................................................................................................... 32

4.0 Discussion ........................................................................................ 36

4.1 Limitations of Study........................................................................................................................ 36

4.2

Subjective Survey ........................................................................................................................... 36

4.3 Objective Survey ............................................................................................................................. 36

4.4 Correlations between Subjective and Objective Results ................................................................. 36

4.5 Light Color ...................................................................................................................................... 37

4.6 Energy Implications ........................................................................................................................ 37

4.7 Lighting Criteria .............................................................................................................................. 37

4.8 Dimming Controls Implications ...................................................................................................... 37

5.0 Future Research Recommendations ................................................. 37

6.0 References ....................................................................................... 38

7.0 Appendix A: Subjective Lighting Evaluation Report (CADMUS Group)39

8.0 Appendix B: Visibility Evaluation Report (VTTI) ............................... 50

9.0 Appendix C: Luminaire Cutsheets ..................................................... 90


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Clanton & Associates Page iii

October 2, 2009

Abbreviations and Acronyms

CCT Correlated Color Temperature in degrees Kelvin

CRI Color Rendering Index

FC Footcandles

HID High Intensity Discharge

HPS High Pressure Sodium

IESNA Illuminating Engineering Society of North America

IND Induction

LED Light Emitting Diode

MH Metal Halide

MOA Municipality of Anchorage

W Watts

List of Figures

Figure 1: System 1 Luminaire .................................................................................... 9

Figure 2: System 2 Luminaire .................................................................................. 10





Figure 7: Experiment Location and Set-Up ................................................................. 13

Figure 8: Nighttime photo of test area #1 with survey group. ...................................... 13

Figure 9: Nighttime photo of test area #4 with survey group. Note the 7x7 green targetin the snowbank to the left of the surveyor group. ...................................................... 14

Figure 10: View of respondents completing surveys at test area #5. ............................ 14

Figure 11: Confidence intervals for statements S2, S3, and S4. ................................... 15

Figure 12: Confidence intervals for statements S5, S6, and S7. ................................... 16

Figure 13: Confidence intervals for statements S8, S9, and S10. ................................. 16

Figure 14: Confidence intervals for statements S11, S12, and S13. .............................. 17

Figure 15: Intensity comparison for Statement S2. .................................................... 18




Figure 19: Mean Illuminance Levels for Each Lighting Section based on Front, Left, and Right

Data Collection Sensors .......................................................................................... 20

Figure 20: Mean detection distances for each lighting section ...................................... 21

Figure 21: Detection distance differences between high and low light levels .................. 22

Figure 22: Detection Distance comparisons for High and Low Lighting levels .................. 23

Figure 23: Power consumption comparisons for High and Low Lighting levels ................. 28


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Clanton & Associates Page 2October 2, 2009

Figure 24: Comparison as a Percentage of the 400W HPS System of Mean Calculated

Illuminance and the Mean Detection Distance (for High Level Settings Only) .................. 29

Figure 25: Comparison as a Percentage of the 400W HPS System of Mean Power

Consumption and the Mean Detection Distance (for High Level Settings Only) ................ 30

Figure 26: Improvement in the Ratio of the Mean Power Consumption and the MeanDetection Distance, using the 250W HPS Improvement (130%) as a Baseline (for High Level

Settings Only) ....................................................................................................... 31

Figure 27: Annual daylight hours for Anchorage, AK. .................................................. 33

Figure 28: Winter Solstice streetlight operation scenarios ........................................... 34

Figure 29: Spring/Fall Equinox streetlight operation scenarios ..................................... 34

Figure 30: Summer Solstice streetlight operation scenarios ......................................... 34


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List of Tables

Table 1: Lighting system power consumption ............................................................... 9

Table 2: Light source color characteristics ................................................................. 12

Table 3: AASHTO Design Stopping Sight Distance (ft and m) for various design speeds .... 25

Table 4: Lighting system calculations ........................................................................ 26

Table 5: Lighting system IESNA criteria met .............................................................. 26

Table 6: IESNA RP-8 criteria table ............................................................................ 27

Table 7: Average pole spacing and power consumption per lighting system .................... 28

Table 8: Streetlighting system energy consumption comparison ................................... 35


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Street Lighting Survey for Commercial Areas in the Municipality of Anchorage Executive Summary

Clanton & Associates Page 4

October 2, 2009

Executive Summary

A subjective lighting survey and object visibility detection, performance, test were

administered to two groups of individuals for six different outdoor lighting systems. Eacharea used a different type of light source as shown in the table below:

System Description Watts/

Lamp

Watts/Luminaire

1 Dimming HPS 250 257

2 BetaLED 234 234

3 Kim Induction 165 165

4 Lumecon 160 160

5 Kim LED 146 146

6 Existing HPS (non-dim) 400 460

The MOA Design Criteria Manual (DCM), Chapter 5, mandates the use of white light for streetlighting applications. As stated in the DCM manual, the human eye can better perceive

objects in low light levels when the source spectrum is broad, including both short and long

wavelength light, commonly perceived as white light.

The performance results indicate that while none of the lighting alternatives in the tests

match the performance of System #6 (the existing lighting equipment on Lake OtisParkway), at full power, they all outperform a 250 watt HPS lighting system. It also confirms

that for the specific visual tasks used in the performance tests, the white light systems do

outperform comparable-wattageHPS lighting systems with greater target detection distance.Interestingly, while the 400W HPS system did have the highest detection distance, several of

the broad-spectrum systems have detection distances that overlap considerably in the 95%range, making them statistically equivalent. These lighting systems were also subjectively

preferred to an equal extent compared to the 400W HPS, despite the fact that they deliverabout one-fourth the light levels of the existing system.

When the actual light levels and power consumption relative to the target detection

performance of the white light lighting systems is considered, a case is made that thetraditional calculations for roadway lighting design (illuminance method and luminance

method) do not adequately consider the variable of light spectrum in the guidelines.

The results also identify some potential inconsistencies among the IESNA lighting guidelines,

the actual performance of the lighting system, and the AASHTO recommendations for DesignStopping Sight Distance. Further research into these discrepancies needs to be performed to

determine whether these differences are inconsequential or meaningful.

The difference between roadway and street lighting is becoming more important to

consider in the lighting design. The current lighting research supports the premise that they

require different visual tasks to perform adequately. Street driving tasks involve lowerspeeds and more edge and hazard detection in the periphery of the field of view, whereas

roadway driving tasks are at higher speeds and are more focused in a smaller zone of

influence further down the drive lane.Future research recommendations include repeating the study in different locations to build a

larger database of results from similar testing. Future studies will incorporate process lessonslearned in this project including:

better control of target locations for equal illuminance

better control of target and background conditions

control or elimination of opposing vehicle headlights


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Street Lighting Survey for Commercial Areas in the Municipality of Anchorage Executive Summary

Clanton & Associates Page 5

October 2, 2009

improve the testing sequence to increase the number of subjects through theperformance portion


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1.0 Introduction

The Municipality of Anchorage Commercial Street Lighting Pilot Survey is intended to providefeedback to the Municipality for potential lighting changes that will impact the quality,

aesthetic, and efficiency of the street lighting systems used commonly in commercial areas.

1.1 History and Background

The MOA Design Criteria Manual (DCM), Chapter 5, mandates the use of white light for streetlighting applications. As stated in this manual, the human eye can better perceive objects in

low light levels when the source spectrum is broad with both short and long wavelengthlight, commonly perceived as white light. Metal halide, induction, and LED technologies

with a CRI of 65 or greater can more closely reproduce white light than a typical high-

pressure sodium lamp (with a CRI of approximately 20). In previous research (Lewin, 1999)broad-spectrum light sources have been found to improve perception-reaction time by

providing roadway users better peripheral vision. While the DCM requires white light sources,it does not specify a lamp technology or wattage. Multiple technologies generate white light

and with somewhat different efficiencies and overall visual results.

A similar study for the MOA performed a subjective survey for residential areas. However, inresidential areas, specific lighting performance requirements are generally not necessary due

to the warranting process established for roadway lighting. Therefore, subjective surveys andcomments from the participants made up the majority of that study.

This experiment contains both an objective (performance) component as well as a

subjective component and is meant to provide insight into the functional visibility providedby various lighting systems and the public preferences for these technologies. It is also

intended to discover variations in preference and visibility under various light sources andprovide a field test for proof-of-concept application of Adaptive Lighting Standards.

The Adaptive Lighting Standard concept recognizes that lighting criteria sets light levels anduniformity requirements based on worst-case conditions for a particular section of roadway.

However, the worst-case conditions may only occur at a particular time of night (such as

rush hour, times of increased pedestrian traffic, inclement weather, etc.). Adaptive lightingstandards allow the lighting system to be dimmed from the worst case design levels to meet

the criteria appropriate for the current roadway conditions.

1.2 Approach

The study subjectively and objectively evaluated six different luminaire systems at twodifferent light levels.

Subjective Evaluation:

1. Two groups of participants evaluated each system using a thirteen-questionsurvey.

2. Questions were asked to evaluate the perception of safety of the lighting system,

the preference for the color of the light, and other general impressions of thelighting system.

3. The results were analyzed for statistically significant differences in responseamong the various lighting systems.

Objective Performance Evaluation:

1. Some of the participants from the subjective survey groups rode in a vehicle that

traveled through each test site (three participants at a time).

2. Participants pushed a logger button when they spotted a 7x7 target along theside of the road.


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3. Equipment on the car recorded its location, the target location, the luminous

scene at the time the target was recognized, as well as the illuminance andluminance conditions along the roadway.

4. These results were analyzed to establish the average detection distance to the

target under each of the lighting systems.

The results of the two different evaluations were then compared to find correlations between

how people view the different lighting conditions and how these same conditions are ratedwith current visibility criteria, specifically, detection distance.

1.3 Project Goals and Objectives

1. Evaluate subjective opinions of citizens toward various light sources and two levels

of source output.

2. Collect and analyze target detection distance data under various light sources andtwo levels of source output.

3. Determine if there is a method to correlate the results from the subjective surveyswith the objective tests in a manner to predict the performance of future lighting

systems through subjective surveys alone.

4. Test the proof-of-concept of the application of a streetlighting control system thatwill allow the MOA to begin to implement a dynamic lighting system to meet

Adaptive Lighting Standards.

5. Provide the MOA with recommendations on the suitability of the lighting

equipment in the test for application on the streets in normal design and

application conditions.

1.4 Market Potential

The information gathered through this study will provide direction throughout the MOA for

commercial street lighting applications. Further, other cities are contemplating similar streetlighting systems and the results of this project can inform other cities regarding the issues of

performance related to white light. The publication of this study can provide insight for

planning departments into public perception and nighttime visibility variables worth

considering.It also will inform other municipalities of the potential for adaptive lighting standards.

Adaptive standards have the potential to significantly reduce the energy consumption ofstreet lighting systems in municipalities across the globe. Since adaptive standards asses

the actual lighting necessary for the lighting conditions on the roadway, the application ofthis approach fundamentally results in the reduction, and possibly elimination, of excess

lighting; yielding greater energy efficiency, reduced light pollution, and reduced power-generation related greenhouse gas emissions.

This report represents early study results in the research into the white light impact on

street and roadway lighting. This research is part of a bellwether body of knowledge thatcan impact the IESNA recommendations for roadway lighting, and ultimately greatly impact

the design practices of the lighting engineering community as a whole.

1.5 Prior Work

To the best of the researchers knowledge there has not been any similar evaluation ofinduction street lighting systems. Previous studies of LED luminaires have been conducted by

the Pacific Gas & Electric, Emerging Technologies Program with the Department of Energy inOakland and San Francisco. These studies evaluated a smaller number of LED luminaires in

residential neighborhoods and focused primarily on energy consumption and economic

performance. The Oakland study contacted residents to see if they noticed the new lightingand if so, get preference feedback from them. It did not take a set number of people through


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the test site at the same time. Neither of these studies included the objective visibility

component of this study to simulate driving and study target detection performance. Noother performance studies of this kind are known to include white light sources.


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2.0 Test Procedures

2.1 Experiment Setup

Approximately one mile of Lake Otis Parkway in the commercial section of SoutheastAnchorage was selected for this research project. The road section is consistent from 61 to

63 feet wide, and typically has two travel lanes and a center-left turn lane through the entire

length of the test area.Six different lighting systems were evaluated in this experiment. In each test area,

approximately ten (10) existing luminaires were replaced with the test luminaires. One ofthe tested lighting systems is the existing 400W HPS

The six different systems evaluated in the study are indicated in Table 1 below.

System Description Watts/Lamp

Watts/Luminaire

1 Dimming HPS 250 257

2 BetaLED 234 234

3 Kim Induction 165 165

4 Lumecon 160 160

5 Kim LED 146 146

6 Existing HPS (non-dim) 400 460

Table 1: Lighting system power consumption

2.2 Luminaires Utilized

The following paragraphs describe in detail each of the lighting systems:

System 1: 250 watt HPS with dimming electronic ballast (Romlight mfg.). The luminaire uses

265 watts at full power. It uses an electronic dimming ballast, and is more efficient than thetraditional core in coil magnetic ballast, which is normally approximately 295 watts per

luminaire. The lamp life is rated at 24,000 hours. The HPS cobrahead luminaire of System 1represents a typical existing roadway lighting design for the Municipality of Anchorage.

Figure 1: System 1 Luminaire

System 2: Beta LED Ledway Streetlight. This luminaire uses 234 watts at full power. TheLED lamps are rated for 70,000 hours (L70 rating; at which point 70% of the LEDs will be

operating properly still). The Beta LED luminaires house strips of LEDs and small opticallenses which are angled in different directions to create a wide light distribution. The strips


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are housed within a weather-tight lens and use an aluminum heat sink to dissipate the heat

generated by the LEDs. The drivers for the fixture are capable of operating at differentlevels based on the leads connected to the power. For this test, the leads were connected so

that a relay would switch the output from high to low output.


System 3: Kim Induction. This luminaire uses 165 watts at full power. The lamp and driverfor this luminaire were provided by Fulham. The driver is capable of dimming. The lamp israted for 100,000 hours. Induction lamps are relatively large and difficult to control optically,

however, the large size makes them produce light with lower apparent brightness, whichresults in a lower sensation of glare under most circumstances.


System 4: Lumecon LED. This luminaire uses 160 watts at full power. The LED lamps are

rated for 70,000 hours. This product also uses small lenses to control the distribution of thelight from the LEDs to the road surface. The shape of this luminaire is somewhat unique in

the industry, and there is concern whether the luminaire meets all the light trespass and

light pollution requirements for application in communities that wish to limit these problems.



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System 5: Kim LED. This luminaire is a prototype for a product that has since been releasedto the public. The prototype luminaire uses 146 watts at full power. The final version is

subtly different and uses 175 watts per luminaire rather than 146. The LED lamps are rated

for 60,000 hours. This LED product is somewhat different from the typical LED luminaire. Ithas a flat glass lens and all the LEDs and the reflector optics are contained within a sealed

enclosure. It has very low glare compared to many LED luminaires.


System 6: 400W Cobra Head High Pressure Sodium. This luminaire uses 460 watts. The HPS

cobrahead luminaire of System 6 represents a typical high-level roadway luminaire for theMunicipality of Anchorage. It has a flat lens, but depending on the light distribution, may not

be considered full cutoff. It was also used as the existing system baseline for theexperiment. The lamp is rated for 24,000 hours.


2.3 Light Sources

This study primarily evaluated different light sources. These sources are commonly

characterized by their color temperature and color rendering ability. Color temperature

(rated in Kelvin temperature) identifies the warmness or coolness of the light color. 2700Krepresents a very red, almost incandescent, looking light. As the temperature ratingincreases, it represents a cooler light. For example, a source rated at 5500K or 6500K

appears very blue.

The color rendering index (CRI) describes a different characteristic of the light source nothow the source itself appears, but rather how well object colors appear under that light

source. This rating ranges from 1 100 where the higher score represents a better colorrendering.


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The sources considered for this test vary considerably in both of these characteristics. In

general, HPS produces a warm color temperature (although, more orange than red) but avery low CRI. The LED, white light sources are much cooler in color temperature (4300K and

below), but have a much higher color rendering near 80 CRI.

System Description ColorTemperature

Color Rendering Index (CRI)

1 Dimming HPS 2100K 212 BetaLED 4300K 75

3 Kim Induction 4000K 80

4 Lumecon 4100K 80

5 Kim LED 3500K 75

6 Existing HPS (non-dim) 2100K 21

Table 2: Light source color characteristics

2.4 Subjective Survey

The subjective lighting survey consists of thirteen statements which the participants rated on

a 1-5 scale (strongly disagree to strongly agree respectively) and administered to two groupsof individuals comprised of two sub-groups each. The groups evaluated the streetlighting insix different areas. The two groups contained 47 and 27 individuals. The surveys were

completed as the individuals rotated through the six areas in a specific order. Within eachgroup, one sub-group started with System (1) and proceeded in order: 1, 2, 3, 4, 5, 6 while

the other sub-group started with System (4) and proceeded in order: 4, 5, 6, 1, 2, 3. Bothgroups rotated through the lighting order until returning to the Segment they began with.

Figure 7 shows the experiment setup areas. For additional information on the testingprocedure, see the full Cadmus report in Appendix A.

2.5 Objective Performance Visibility Test

The visibility portion of the experiment involved participants (three at a time) riding in a

vehicle equipped with a Roadway Lighting Mobile Measurement System. The RLMMS,developed by Virginia TECH Transportation Institute (VTTI), measures illuminance,

luminance, color, participant response data, and GPS location. Additionally, a video camera

captures images of the view through the windshield throughout the test. Participants wereasked to press a button when they located a target along the right side of the road. Although

the targets varied in color (grey, blue, green, and red) this variable was not directly analyzedin the study. The system recorded all of the measurement data at the time of the participant

response. By identifying the GPS location of the car at both the response location and thetarget location, detection distances are extracted. The VTTI team then compared these

detection distances and illuminance levels between the six lighting systems. For additionalinformation on the test procedure, see the full report in Appendix B.


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Figure 7: Experiment Location and Set-Up

2.6 Photos

The team took photos of the experiment area on the night of the survey.

Figure 8: Nighttime photo of test area #1 with survey group.


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Figure 9: Nighttime photo of test area #4 with survey group. Note the 7x7 green target in the

snowbank to the left of the surveyor group.

Figure 10: View of respondents completing surveys at test area #5.


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3.0 Results

This report discusses selected results of the two portions of the study separately in thefollowing two sections. The full reports in Appendices A and B list all of the results found.

Further, an interpretation section provides guidance for how this information can be applied

to current streetlighting design approaches, and another section discusses the energy andenvironmental implications of the various lighting systems, and how that may factor into the

overall approach that the MOA can use going forward.


Cadmus analyzed the survey results to determine subjective differences between types oflighting systems as well as the intensity of these systems.

Ninety five percent confidence intervals were constructed around the mean score for each

statement and these intervals were compared across the lighting types. Significantdifferences for responses to the systems are determined by comparing confidence intervals.

When the intervals do not overlap, the difference is considered statistically significant at the95% confidence level.

The following figures are taken directly from the Cadmus report found in Appendix A. These

figures identify significant differences between lighting types. Even though both intensitiesare shown in the graph, this first portion of the analysis does not address the differences

between lighting intensities.

Figure 11: Confidence intervals for statements S2, S3, and S4.

95% Confidence Intervals

0

1

2

3

4

5

250WH

PS

BetaLED

KimInduction

LumeconLED

KimLED

Control

250WH

PS

BetaLED

KimInduction

LumeconLED

KimLED

Control

250WH

PS

BetaLED

KimInduction

LumeconLED

KimLED

Control

250WH

PS

BetaLED

KimInduction

LumeconLED

KimLED

Control

250WH

PS

BetaLED

KimInduction

LumeconLED

KimLED

Control

250WH

PS

BetaLED

KimInduction

LumeconLED

KimLED

Control

MeanQuestionValue

300 400 400 400300 300

S2 S3 S4


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Figure 14: Confidence intervals for statements S11, S12, and S13.

Cadmus conducted a second analysis that evaluated significant differences in perceptionbetween lighting intensities. Because two different groups of people evaluated the lighting at

the two different intensities, the use of the control (undimmed 400 watt HPS) served as anadjustment factor to eliminate this bias. In the following graphs, all alternate systems are

shown in comparison to System 6 the 400 watt HPS control. Only four statements differed

significantly in the subjective survey between high and low intensities:1. S2: It would be safe to walk here, alone, during darkness hours.

2. S4: There is too much light on the street.

3. S7: The light sources are glaring.

4. S9: I cannot tell the color of things due to the lighting.

The following figures show the results for these statements only.

95% Confidence Intervals

0

1

2

3

4

5

25 0 W

HPS

Be t a

LED

Kim

Induc tio n

Lume c o nLED

Ki m

LED

Co ntro l

25 0 WHPS

Be taLED

Ki m

I nduc t io n

Lume c o

nLED

Kim

LED

Co ntro l

25 0 W

HPS

Be t a

LED

Ki m

Induc tio n

Lume c o

nLED

Kim

LED

Co ntro l

25 0 WHPS

Be taLED

Ki m

I nduc tio n

Lume c o nLED

Ki m

LED

Co ntro l

25 0 WHPS

Be taLED

Ki m

Induc tio n

Lume c o

nLED

Kim

LED

Co ntro l

25 0 W

HPS

Be t a

LED

Kim

Induc tio n

Lume c o nLED

KimLED

Co ntro l

MeanQuestionValue

300 400 400 400300 300

S11 S12 S13


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Figure 15: Intensity comparison for Statement S2.


It would be safe to walk here, alone, during darkness hours

-3

-2

-1

0

1

DifferenceFromArea6 300

400

300

300

300

300

400

400

400

400

1-6 2-6 3-6 4-6 5-6

There is too much light on the street

-3

-2

-1

0

1

DifferenceFromArea6

300

400

300

300 300

300

400

400

400

400

1-6 2-6 3-6 4-6 5-6


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The light sources are glaring

-3

-2

-1

0

1

DifferenceFromArea

300

400

300

300300

300

400400

400

400

1-6 2-6 3-6 4-6 5-6

I cannot tell the colors of things due to the lighting

DifferenceFromArea6

300

400

300

300 300

300

400

400

400

400

1-6 2-6 3-6 4-6 5-6


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3.2 Objective Visibility Study

Like the subjective portion of the analysis, VTTI looked for significant differences in detectiondistance between lighting types and lighting intensity. The following figures are taken directly

from the VTTI report found in Appendix B.

Figure 19 shows the mean illuminance values between each system and clearly illustratesthat illuminance (and uniformity) were not normalized for the evaluation. The three bars for

each system represent data from three different illuminance meters with differentorientations on the RLMMS. Note that while all of the alternate lighting systems produce a

mean illuminance between 5 and 10 lux, the existing control system produces illuminancebetween 15 and 20 lux.

Figure 19: Mean Illuminance Levels for Each Lighting Section based on Front, Left, and Right

Data Collection Sensors

Figure 20 compares the differences in detection distance between lighting systems only, not

at various intensities. The letters on the top of each column indicate a significant difference

with the other columns (system types). The columns with the same letters do not differsignificantly from each other. For example, the 400 watt HPS system is labeled with an A.No other column in the chart has the A label. This indicates that all other systems differed

significantly from the 400 watt HPS. This same example illustrates that the 400 watt HPS

had the longest detection distance of any of the other systems. Similarly, the chart showsthat the 250 watt HPS differed from the Lumecon LED and the 400 watt HPS (those columns

do not contain the label C) but not from the remaining systems.

0

5

10

15

20

25

250WDimming

HPS

BetaLED

KimInduction

Lumecon KimLED

250WDimming

HPS

BetaLED

KimInduction

Lumecon KimLED

400W Non-Dimming

HPS

Low High Control

MeanIlluminance(lux)

Lighting Class

Mean Illuminance Levels For Each Lighting Section

Left

Front

Right


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Figure 20: Mean detection distances for each lighting section

In another round of analysis, the 400 watt HPS system was removed. This considered onlythe alternate systems at their high and low intensities. Figure 21 shows each of the alternate

systems in the same format as discussed for the previous figure. The first five columnsrepresent the systems at their low intensities while the second set represents the detection

distance at the high intensity. Again, columns with the same letter do not differ significantlyfrom one another.

C

B, C B, C

B

B, C

A

0

10

20

30

40

50

60

70

250W Dimming

HPS

Beta LED Kim Induction Lumecon LED Kim LED 400W Non-

Dimming HPS

MeanDetectionDistance(m)

Lighting Section

Lighting Section Comparison


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Figure 22: Detection Distance comparisons for High and Low Lighting levels

The above charts raise the question of how much light do drivers need at night to safelydetect obstacles in the road and make decisions to avoid those obstacles. Clearly, increased

illuminance increases detection distance. The IESNA provides lighting criteria for nighttimedriving. Likewise, the American Association of State Highway and Transportation Officials

(AASHTO) compiles reaction time and braking time to come up with recommended stoppingdistances when traveling various speeds.

3.3 Detection Distance and Design Stopping Distance

The previous figures raise the issue of detection distance and its importance to the design ofroadway lighting. The IESNA provides lighting criteria for nighttime driving while the

American Association of State Highway and Transportation Officials (AASHTO) defines theclear sight distance required for a stretch of roadway. The data of this study clearly show

that increased illuminance increases detection distance. The relationship between IES

criteria, design sight distance of the roadway, and the actual detection distance at night isnot clearly established, however.

The IES establishes criteria for illuminance on a roadway surface and the luminance of ascene. When calculating the luminance on the pavement, the motorist position is 1.45

meters above the road and looking at a point 83 meters ahead. (The CIE calculatesluminance from a perspective of 1.5 meters above the road and 60 meters away from the

calc point). This same calculation distance is used for all roadway types and the

corresponding luminance requirements increase with traffic speed and potential pedestrianconflicts.

0

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70

High Low

MeanDetectionDistance(m)

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Lighting Intensity Comparison


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The focus distances in the luminance calculations are greater than a typical driver operating

a vehicle on a local street may actually need for safe navigation when pedestrians andintersection navigation are present. Table 3: AASHTO Design Stopping Sight Distance (ft

and m) for various design speeds shows the Design Stopping Sight Distance for a roadway at

various speeds. The two distances used for the calculations (83M and 60M), approximatelymatch to the speeds of 35mph and 30mph. The posted speed on the stretch of Lake Otis

Parkway is 35mph. The test area has regular intersections with pedestrian crosswalks, busstop locations, curb cuts for commercial entry and exit, and a continuous left-turn lane.

These all represent locations where the driver focus could be considerably shorter than the83 and 60 meters.

AASHTO defines sight distance as, the length of roadway ahead that is visible to the driver,and states that the Available sight distance should be sufficiently long to enable a vehicle

traveling at or near the design speed to stop before reaching a stationary object in its path.

To determine this sight distance, AASHTO combines reaction time (how long it takes a driverto realize that they must apply the brakes) with braking time (how long it takes the vehicle

to come to a stop once the breaks are applied) to come up with recommended stoppingdistances at various speeds.

A few characteristics of the AASHTO design sight stopping distances should be noted:

All distances are based on the design speed selected for the particular roadway, and

this speed should not be confused with the posted speed of the roadway, which will

typically be a lower limit.

The design sight distance is used to guide the design of the roadway geometry, not

necessarily the nighttime lighting requirements. This may be used as criteria forclear line-of-sight from the drivers position to a specific geometry change on the

road, but may not be intended for situations where a hazard is introduced to the

road in a dynamic condition.

The design standard does not state the size of the object that must be detected at

the stopping distance.

Table 3 shows the AASHTO design stopping sight distance for speeds from 15 mph to 80mph in both feet and meters.


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Design Speed (mph)Design Stopping Sight Distance

(ft) (m)

15 80 24.4

20 115 35.1

25 155 47.2

30 200 61.0

35 250 76.2

40 305 93.0

45 360 109.7

50 425 129.5

55 495 150.9

60 570 173.7

65 645 196.6

70 730 222.5

75 820 249.9

80 910 277.4

Table 3: AASHTO Design Stopping Sight Distance (ft and m) for various design speeds

For the speed considered in this study (35mph), detection distances ranged fromapproximately 35 meters to 65 meters. Refer to Figure 20: Mean detection distances for

each lighting section for the average detection distance for a 7x7 target on the side of the

test road.

3.4 Calculated Lighting Results

The six lighting systems in the test were modeled in AGI to simulate the lighting for eachsegment and to provide calculated light levels, uniformity and glare analysis. These values

are shown in Table 4. Such calculations are important for the design community becausewhile these tested road segments are already installed, the design of a new road must rely

on the calculation procedures detailed in the IES Recommended Practice for RoadwayLighting (RP-8) or the AASHTO guidelines that essentially draw from the IES RP-8

documents.


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Road and Pedestrian ConflictArea

Criteria for R3Pavement

Uniformity RatioVeiling

LuminanceRatio

RoadPedestrianConflict A rea

Illuminance (fc) Eavg/Emin Lvmax/Lavg

Expressway High 1.4 3.0 0.3

Medium 1.2 3.0 0.3

Low .9 3.0 0.3

Major High 1.7 3.0 0.3

Medium 1.3 3.0 0.3

Low .9 3.0 0.3

Collector High 1.2 4.0 0.4

Medium .9 4.0 0.4

Low .6 4.0 0.4

Local High .9 6.0 0.4

Medium .7 6.0 0.4

Low .4 6.0 0.4

Table 6: IESNA RP-8 criteria table

With 5.3 footcandles average illuminance, System 6 far exceeds the light levels

recommended for the Major:Highcategory in the criteria table above. The other systems

meet all of the individual criterion for the categories shown in Table 5. Note that there isconsiderable overlap between the average illuminance levels in the Majorcategory of

roadway type, and the Collectorcategory. Likewise, there is overlap between CollectorandLocal.

The study measured an average detection distance for the 400W HPS existing system ofapproximately 67 meters, which is slightly below the recommended AASHTO design distance

for a 35 MPH roadway. However, the difference between the designandpostedspeeds

factors in considerably in this situation. The design speed will typically be 5 to 10 MPHhigher than the posted speed, and the existing lighting system does not meet the 93 to

109.7 meter guideline for the likely design (higher) speed.

The 250W HPS system meets the Major:Mediumcriteria. However, even though it meets the

second highest criteria level, and by the IESNA design criteria perspective is very suitable forroads similar to Lake Otis Parkway, the measured, mean detection distance is 25 meters.

This distance is compatible with a 15 MPH design speed from the AASHTO guidelines. Thisindicates a clear discrepancy between the lighting based purely on the design criteria and the

actual visibility performance.

Conversely, the broad-spectrum sources all fall into to lower road categories. The KimInduction and the Kim LED both meet Collector:Low, which is a considerable departure from

the existing or the 250W HPS systems. However, both outperform the 250W HPS in the high

tests, and essentially match the high setting of the 250W HPS while in the low setting.

3.5 Energy Implications

Due to the nature of LED light sources, the various lighting systems tested reflect differentenergy consumption partially because there are no standard LED lamping packages. This

will continue to be the case with LED technology as the industry advances.

However, one benefit of LED technology is the possibility that the lumen package can be

adjusted quickly to meet the needs of the roadway conditions more easily and in smaller

increments than the traditional HPS and MH lamp wattage increments. This may result in


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the ability for a lighting designer to meet a standard without exceeding it too greatly; which

will result in energy savings throughout the life of the lighting design.

In the test area, the road width was constant through the entire length of the test zones, so

the power for each system is dependent on the wattage of the luminaire and the spacing of

the luminaires within each system.

The linear power density of the roadway is calculated in Table 7 below. While the wattages

of the fixtures are within a similar range (with the exception of the existing 400W HPSsystem), the pole spacing does vary enough on the test roadway segments to add additional

variability to the energy calculations. Because of this, the alternate lighting systems thathave the highest power consumption per head are not the highest consumption per linear

foot of roadway.

System Descript ion Average PoleSpacing

Full Output Power(Watts per Linear Foot)

1 Dimming 250W HPS 213 1.21

2 BetaLED 161 1.45

3 Kim Induction 110 1.5

4 Lumecon 126 1.27

5 Kim LED 132 1.1

6 Existing HPS (non-dim) 107 4.3

Table 7: Average pole spacing and power consumption per lighting system

Figure 23: Power consumption comparisons for High and Low Lighting levels

1.241.45 1.50

1.271.11

4.30

0.62

0.97 0.91

0.63 0.55

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Dimming

HPS

BetaLED Kim

Induction

Lumecon

LED

KimLED 400w

Non

dimHPS

PowerConsumption

(WattsperLinearFootof

Roadway)

FullPower

LowPower


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The difference in energy consumption for the lighting system options is quite considerable.

While there is a decrease in detection distance for all of the tested lighting systems, they donot show a decrement in detection distance that is proportional to the power consumption,

with the exception of the 250W HPS system, which performs slightly better than the energy

reduction might indicate.

Figure 24 shows this difference in detection distance performance compared to calculated

illuminance as a percentage of the existing 400W HPS lighting system. This shows thedifference in the HPS and the broad-spectrum systems as defined in light levels. Figure 25

shows the same relationship as it relates to energy consumption for the roadway test areas.

Figure 24: Comparison as a Percentage of the 400W HPS System of Mean Calculated Illuminance

and the Mean Detection Distance (for High Level Settings Only)

29%

17%

11%

18%

9%

37%

66%69%

78%

69%

0%

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20%

30%

40%

50%

60%

70%

80%

90%

100%

250WDimmingHPS BetaLED KimInduction LumeconLED KimLED

Avg.Illuminance%

Avg.DetectionDistance%


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Figure 25: Comparison as a Percentage of the 400W HPS System of Mean Power Consumption

and the Mean Detection Distance (for High Level Settings Only)

Conversely, the percentage better that the broad-spectrum lighting options performcompared directly to the 250W HPS baseline is shown in Figure 26. The baseline is the

130% improvement in detection distance vs. power that the 250W HPS system shows whencompared to the existing 400W HPS system in Figure 25.

This calculation represents the increase in efficiency of task performance over the 250W HPS

system. Viewed this way, the broad-spectrum lighting system options show a considerableimprovement over the 250W HPS.

29%

34% 35%

30%26%

37%

66%69%

78%

69%

0%

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40%

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250WDimmingHPS BetaLED KimInduction LumeconLED KimLED

Avg.PowerConsumption%

Avg.DetectionDistance%


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3.6 Applications Interpretation

The subjective survey and performance test results provide a reasonable starting point to

consider a reinterpretation of traditional streetlighting approaches based on new lightingtechnologies and a better understanding of the visual performance of these various systems.

While the IESNA is slow to adopt new design philosophies based on changing technology,there is movement in that organization to address some of the performance and design

discrepancies that have become apparent in the current streetlighting standards.

The Roadway Lighting committee, (the authors of RP-8), is developing an approach to

separate Roadand StreetLighting into two separate sets of recommendations, because thereare subtly different visual tasks associated with these two activities, as well as differing

speeds, risks for conflict, and navigational issues.

There is a technical and research committee in the IESNA that is currently charged with newresearch for the questions associated with the broad-spectrum influence visibility

calculations; the Mesopic Light Committee. There is new research currently being definedand funded as part of that committees focus that will help identify appropriate procedures

for the IESNA design committees to adopt to create lighting design standards that

appropriately address the full impact of spectrum and color rendering. This research includesthe Lumen Effectiveness Multipliers developed by Dr. Ian Lewin. These multipliers can be

used to determine an equivalent light output based on the spectral output of the lamp.

The MOA has the option to choose to utilize the guidelines, but with a modification to the

calculation procedures to accommodate the broad-spectrum impact that is known to exist.

3.7 Dimming Controls

The proof-of-concept trial for the dimming controls was a success. The lighting equipment

was activated under the operation of a wireless controller via a linked cell phone, and thelighting was responsive to the commands.

There are a number of controls issues that must be considered in the implementation of alighting control system that includes dimming controls, infrastructural limitations of the

existing electrical systems in the MOA, billing arrangements with the utilities, and specific

tasks that the MOA wants the control system to accomplish. All of these are going toinfluence the selection of an Adaptive Streetlighting Control (ASC) system.

A compelling case for ASC can be made based on several factors. Much of the lightingequipment used can exhibit longer lamp life when the lighting system is dimmed. This will

require less maintenance and increase the life of the installation beyond the presumed lifeexpectancy. It will also consume less energy.

Figure 27 shows the annual daylight hours for the Anchorage area. The yellow region

represents the time of the day that the sun is above the horizon. The blue is dawnconditions, and pink is dusk condition. Dawn and dusk are defined as sunset or sunrise until

the end of civil twilight (6.5 below the horizon). Months are represented as roman numeralsalong the x-axis, and hours of the day are labeled along the y-axis.


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Darkness Dawn Sunshine Dusk

Figure 27: Annual daylight hours for Anchorage, AK.

Most photocell based streetlighting will function midway through civil twilight until midwaythrough twilight on the other end of the night. Figure 28, Figure 29, and Figure 30 show a

typical streetlighting system in operation for the winter solstice, spring/fall equinox, andsummer solstice conditions. The red portion represents the operation of the lighting with a

dimming setback. The blue represents the difference between the dim setting and the

standard full output setting.

The presumed low-use period is from 9:00 PM until 6:00AM, but these details should bedetermined through a traffic study, and the period can be set in different parts of thecommunity depending on the use observed. The downtown area may need a later setback,

but more suburban zones may be able to apply a setback earlier in the evening.

The dimming should not occur during twilight periods because these lighting conditions are

the most hazardous for vehicular operation. The summer equinox does not get dark enoughto go past civil twilight (see June and July (VI and VII) in Figure 27 above), so this condition

should not have dimming actuated. However, dimming can be applied as soon as darkness

is complete, and Figure 27(above) shows that there is ample opportunity to apply ASC to themajority of the year, with the exception of about one months time surrounding the summer

solstice.


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Figure 28: Winter Solstice streetlight operation scenarios

Figure 29: Spring/Fall Equinox streetlight operation scenarios

Figure 30: Summer Solstice streetlight operation scenarios

0

0.1

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The hours of operation for the three months in the graphs above is represented by Table 8.

Further, the percentage reduction is calculated to show the possible energy reduction thatcan be expected from an ASC control strategy.

Hours of Operation

PhotocellControl

ASC Control % Reduct ion

Winter Solstice 18 13.5 25 %Spring/Fall Equinox 11 6.5 41 %

Summer Solstice 3.25 3.25 0 %

Table 8: Streetlighting system energy consumption comparison


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4.0 Discussion

4.1 Limitations of Study

All of the results are affected by several factors that could not be addressed given the

constraints of the experiment. It cannot be overstated that locations for all of the luminairesare fixed and designed for the existing condition a 400 watt high pressure sodium

luminaire. In other words, the spacing and mounting height for all of the alternate systems

were not optimized for that particular luminaire, but rather installed in place of the existinghigh pressure sodium luminaire. The alternate systems evaluated produce significantly lesslight at a much lower wattage than the existing system. The existing luminaires provide (in

some cases) several times the illuminance of the alternate systems. Not surprisingly,detection distance increases with an increase in illuminance. The results shown in the

previous section were not normalized for these differences in illuminance and uniformity.

Additionally, the entire experiment was conducted with other traffic on the streets. Oncomingheadlights were not controlled during the testing and could have had an impact on all of the

results.


The survey respondents showed no strong preference for the alternate lighting systems,

even though the light levels are considerably lower for most of the alternates compared tothe existing 400W HPS system. They did note that they felt the 400W HPS is more glaring

than the other alternates, and there is a greater number of people who felt there was toomuch light on the street as well.

4.3 Objective Survey

The objective measurements indicate that while none of the alternate lighting systems match

the target detection distance, there are several that come close, and when the actual lightlevels are compared, there is a considerable discrepancy in the performance of the broad-

spectrum lighting systems and the HPS systems.

While this phenomenon is not fully understood, this finding was anticipated based on

previous research. These results will help define future research into the effect of broad-spectrum light sources on driving task performance.

4.4 Correlations between Subjective and Objective Results

The results from the objective portion of the test illustrates that the 400W HPS had thelongest detection distance of any of the other systems. This also correlates to the system

with the highest illuminance levels in the test, and also the highest energy use per linear foot

of roadway.

However, the overall preference in the subjective surveys does not show a preference for

this lighting system. It is mostly even in preference with the much lower wattage alternatesstudied in this project.

Interestingly, while the 400W HPS system did have the highest detection distance, several of

the broad-spectrum systems have detection distances that overlap considerably in the 95%range, making them statistically equivalent. These lighting systems were also subjectively

preferred to an equal extent compared to the 400W HPS, despite the fact that they deliverabout one-fourth the light levels of the existing system.

The correlation between these two portions of the studies seems to be that the perception ofthe desirability of the lighting system and the actual detection distance appear to be at

similar levels of proportion with respect to the other options. Further research may show

this to be a good predictor of detection distance without the intensive objective testingprocedures.


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4.5 Light Color

The color temperature of the lighting system is a variable that needs careful consideration if

a broad-spectrum source is to be considered. While it is outside the scope of this report,

some studies have shown that avoiding short-wavelength light is preferable for the flora andfauna and also for human impacts as well. It is possible to have white light that has less of

the wavelengths that are of concern. This is the reason that attention is being paid to thecolor temperature of the moon (4125K), and the basis for our preliminary recommendation

that all the light sources tested have a color temperature comparable to or lower (warmer)than the moon.

More research will be required to fully understand these interactions, but recent researchseems to indicate that this is a solid approach to the question.

4.6 Energy Implications

The energy aspect of this research shows that there are some considerable possibilities toreduce energy consumption and also connected load if a change in lighting philosophy is

instituted, and also through the adoption of an Adaptive Streetlighting Control system.

4.7 Lighting Criteria

The results also appear to identify some potential conflicts among the IESNA lightingguidelines, the actual performance of the lighting system, and the AASHTO recommendations

for stopping distance. Further research into these discrepancies needs to be performed to

determine whether these differences are inconsequential or meaningful.

The difference between roadway lighting and street lighting is becoming more important to

consider, as the current belief is that they require different visual tasks to performadequately.

Considering that the current IESNA recommendations were established when the de factostandard for street lighting and roadway lighting was High Pressure Sodium (HPS)

technology, a review of the recommendations and inclusion of this recommendation issuggested.

4.8 Dimming Controls Implications

Dimming controls are a viable opportunity to improve the overall reliability of the lightingsystem and also reduce energy consumption considerably. This is a new approach in the

industry, and there are numerous barriers and questions to be answered before a good ASCsystem is implemented. However, the logic and benefits of an ASC system are sound, and

research into a suitable control system for the MOA is recommended.

5.0 Future Research Recommendations

Future recommendations include repeating the study in different locations to compare for

similar results. Additionally, future studies should better control for target contrast andvehicle headlights.


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6.0 References

Lewin, Ian. Visivbility Factors in Outdoor Lighting Design, part 2. The Lighting Journal,

Early 2000. Institution if Lighting Engineers, Rugby, UK.

IESNA RP-8 Recommended Practice for Street Lighting Illuminating Engineering Society of

North America, New York, New York.

RLW Analytics, Inc.October 2003. PIER CA Outdoor Lighting Baseline Assessment Study

P500-03-082-A-18. California Energy Commission, CA and New Buildings Institute, WhiteSalmon, Washington.

U.S. DOE Solid-State Lighting Technology Demonstration Gateway Program and PG&E

Emerging Technologies Program, Demonstration Assessment of Light Emitting Diode (LED)Street Lighting, Phase III Continuation, Oakland, CA, November 2008.

U.S. DOE Solid-State Lighting Technology Demonstration Gateway Program and PG&E

Emerging Technologies Program, LED Street Lighting, San Francisco, CA, December 2008.


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7.0 Appendix A: Subjective Lighting Evaluation Report (CADMUS Group)


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