55 Transportation Research Record: Journal of the Transportation Research Board, No. 2384, Transportation Research Board of the National Academies, Washington, D.C., 2013, pp. 55–64. DOI: 10.3141/2384-07 J. C. Medina, Room 3150; K. A. Avrenli, Room B106; and R. F. Benekohal, Room 1213, Department of Civil and Environmental Engineering, Newmark Civil Engineering Laboratory, University of Illinois at Urbana–Champaign, Urbana, IL 61801. Corresponding author: J. C. Medina, [email protected]. (29.2%), mercury vapor (13.5%), halogen quartz (7.5%), fluorescent (5.7%), or incandescent (2.4%) light sources (3). The use of energy-efficient technology is required in roadway lighting to help reduce energy consumption costs. Light-emitting diodes (LEDs) are the fourth generation of light sources that have appeared as an energy-efficient alternative to high-intensity discharge street lighting, following incandescent lamps, arc and gas pres- sure sodium lamps, and light-emitting phosphors. LEDs also save costs because of the reduced frequency of maintenance. As recent technological advancements have increased the quality of LEDs by approximately 10 times and reduced their production cost by approximately 90%, the use of LEDs as a light source for roadway lighting fixtures has become feasible (4). The market share of LEDs is continuously increasing. For example, in 2010, the Chinese LED market scale reached 4.85 billion yuan (about $710 million in May 2010), a 43.5% increase compared with the market scale in 2006 (4). It is expected that high-brightness LEDs will prevail in general lighting applications, which currently account for about 15% of the world’s total energy consumption (5), and will gradually replace about 25% to 30% of incandescent lighting applications by 2025 (6–8). The use of LEDs in roadway lighting applications has been assessed in the GATEWAY demonstration projects supported by the U.S. Department of Energy as well as in projects of different agencies (9–17 ). These projects have showcased successful LED installations for street and highway lighting and have gained significant attention in the lighting community and the general public. However, given the rapid pace at which LED luminaires are evolving, continuous testing in both the laboratory and the field is needed to determine the performance of newer products. The objectives of this study were threefold: • To compare the field illuminance values of LED roadway luminaires with their expected performance, as shown by analytical computation with software; • To determine the location and magnitude of discrepancies between the field data and software data; and • To determine how to relate the software and field data. Repeated field illuminance measurements were taken for each of three sets of recently developed LEDs and one set of high-pressure sodium (HPS) roadway luminaires. These measurements were compared with the luminaires’ performance in computer analysis with AGi32 lighting analysis software; photometric characteristics provided by the manufacturers of the luminaires were used in the computer analysis. This paper presents the complete set of illuminance Field and Software Evaluation of Illuminance from LED Luminaires for Roadway Applications Juan C. Medina, Kivanc A. Avrenli, and Rahim (Ray) F. Benekohal Field measurements and lighting analysis software were used to con- duct an evaluation of the illuminance of LED roadway luminaires. Three sets of recently developed LEDs and one set of high-pressure sodium luminaires were installed in the field, and data were collected separately for each luminaire according to Illuminating Engineering Society of North America (IESNA) LM-50-99. Photometric character- istics provided by the manufacturers of the luminaires were used to assess their performance with AGi32 lighting analysis software, and the results were compared with the field illuminance data. Results showed that field illuminance was sometimes lower than the illuminance indicated by the software; these differences varied in both magnitude and location, depending on the LED luminaire. Multiplicative factors to describe the differences were estimated and ranged from 0.96 (relatively small) to 0.57 (relatively large). Larger differences were located near the light poles and at the middle and third points of the span. These findings suggest that quick checks can be performed by taking illuminance measurements at key points along the span to provide an approximation of the differences between field and software data. Discrepancies between software and field data may be very important in the selection of luminaires for a given road and pedestrian conflict classification, as the field illuminance may not reach minimum levels required by public agencies. This point is illustrated with an example that uses minimum requirements from the Illinois Department of Transportation. Full sets of repeated field data points for state-of-the-art LED roadway luminaires, collected according to IESNA LM-50-99, are presented. Roadway lighting can improve personal security and traffic flow operation and safety. With proper roadway lighting, drivers can more easily recognize the condition and geometry of the roadway. A study has also shown that lighting can contribute to reducing nighttime traffic crashes (1). In addition, proper roadway lighting contributes considerably to highway safety by increasing driver visual comfort and reducing driver fatigue (2). There were approximately 131 million installed bases of street and area lights in the United States in 2007, and the total annual electricity consumption per fixture was 178.3 kW-h. Approximately 54.7 million (41.7%) of those bases contained high- pressure sodium (HPS) light sources. The rest used metal halide
Transcript
55
Transportation Research Record: Journal of the Transportation Research Board, No. 2384, Transportation Research Board of the National Academies, Washington, D.C., 2013, pp. 55–64.DOI: 10.3141/2384-07
J. C. Medina, Room 3150; K. A. Avrenli, Room B106; and R. F. Benekohal, Room 1213, Department of Civil and Environmental Engineering, Newmark Civil Engineering Laboratory, University of Illinois at Urbana–Champaign, Urbana, IL 61801. Corresponding author: J. C. Medina, [email protected].
The use of energy-efficient technology is required in roadway lighting to help reduce energy consumption costs. Light-emitting diodes (LEDs) are the fourth generation of light sources that have appeared as an energy-efficient alternative to high-intensity discharge street lighting, following incandescent lamps, arc and gas pres-sure sodium lamps, and light-emitting phosphors. LEDs also save costs because of the reduced frequency of maintenance. As recent technological advancements have increased the quality of LEDs by approximately 10 times and reduced their production cost by approximately 90%, the use of LEDs as a light source for roadway lighting fixtures has become feasible (4).
The market share of LEDs is continuously increasing. For example, in 2010, the Chinese LED market scale reached 4.85 billion yuan (about $710 million in May 2010), a 43.5% increase compared with the market scale in 2006 (4). It is expected that high-brightness LEDs will prevail in general lighting applications, which currently account for about 15% of the world’s total energy consumption (5), and will gradually replace about 25% to 30% of incandescent lighting applications by 2025 (6–8).
The use of LEDs in roadway lighting applications has been assessed in the GATEWAY demonstration projects supported by the U.S. Department of Energy as well as in projects of different agencies (9–17). These projects have showcased successful LED installations for street and highway lighting and have gained significant attention in the lighting community and the general public. However, given the rapid pace at which LED luminaires are evolving, continuous testing in both the laboratory and the field is needed to determine the performance of newer products.
The objectives of this study were threefold:
• To compare the field illuminance values of LED roadway luminaires with their expected performance, as shown by analytical computation with software;
• To determine the location and magnitude of discrepancies between the field data and software data; and
• To determine how to relate the software and field data.
Repeated field illuminance measurements were taken for each of three sets of recently developed LEDs and one set of high-pressure sodium (HPS) roadway luminaires. These measurements were compared with the luminaires’ performance in computer analysis with AGi32 lighting analysis software; photometric characteristics provided by the manufacturers of the luminaires were used in the computer analysis. This paper presents the complete set of illuminance
Field and Software Evaluation of Illuminance from LED Luminaires for Roadway Applications
Juan C. Medina, Kivanc A. Avrenli, and Rahim (Ray) F. Benekohal
Field measurements and lighting analysis software were used to con-duct an evaluation of the illuminance of LED roadway luminaires. Three sets of recently developed LEDs and one set of high-pressure sodium luminaires were installed in the field, and data were collected separately for each luminaire according to Illuminating Engineering Society of North America (IESNA) LM-50-99. Photometric character-istics provided by the manufacturers of the luminaires were used to assess their performance with AGi32 lighting analysis software, and the results were compared with the field illuminance data. Results showed that field illuminance was sometimes lower than the illuminance indicated by the software; these differences varied in both magnitude and location, depending on the LED luminaire. Multiplicative factors to describe the differences were estimated and ranged from 0.96 (relatively small) to 0.57 (relatively large). Larger differences were located near the light poles and at the middle and third points of the span. These findings suggest that quick checks can be performed by taking illuminance measurements at key points along the span to provide an approximation of the differences between field and software data. Discrepancies between software and field data may be very important in the selection of luminaires for a given road and pedestrian conflict classification, as the field illuminance may not reach minimum levels required by public agencies. This point is illustrated with an example that uses minimum requirements from the Illinois Department of Transportation. Full sets of repeated field data points for state-of-the-art LED roadway luminaires, collected according to IESNA LM-50-99, are presented.
Roadway lighting can improve personal security and traffic flow operation and safety. With proper roadway lighting, drivers can more easily recognize the condition and geometry of the roadway. A study has also shown that lighting can contribute to reducing nighttime traffic crashes (1). In addition, proper roadway lighting contributes considerably to highway safety by increasing driver visual comfort and reducing driver fatigue (2). There were approximately 131 million installed bases of street and area lights in the United States in 2007, and the total annual electricity consumption per fixture was 178.3 kW-h. Approximately 54.7 million (41.7%) of those bases contained high-pressure sodium (HPS) light sources. The rest used metal halide
56 Transportation Research Record 2384
data points collected according to Illuminating Engineering Society of North America (IESNA) LM-50-99 standard procedures as well as the location and magnitude of discrepancies between the field data and software calculations.
Given that complete sets of field measurements are rarely col-lected in common practice, this information can be highly valuable to researchers and practitioners, as it gives a perspective on the actual light output of LED luminaires and compares it with their expected performance. In addition, the importance of differences between field and software results, as well as the suitability of the LED luminaires for general roadway lighting applications, is illustrated by using design requirements from the Illinois Department of Transportation (DOT).
The next section of this paper discusses the procedure for selecting the luminaires and is followed by a description of the test site and the data collection. The field illuminance measurements and results of the AGi32 analysis are then presented and compared. These data are also used to find multiplicative factors to approximate field values on the basis of the AGi32 calculations. The implications of the differences between the field and AGi32 data are then highlighted, followed by conclusions and recommendations.
SELECTION OF LED ROADWAY LUMINAIRES
The first step in the selection of the LED luminaires was the identi-fication of manufacturers and major distributors with products suit-able for roadway lighting applications. Fifty-seven companies were contacted through a survey designed by the research team. The sur-vey included questions regarding the characteristics of the products, warranties, past experience, and quality control.
The survey results were used to select potential products for field testing. After further conversation with product distributors and
manufacturers regarding their interest in the project, the researchers selected three LED luminaires for the study. In addition to the LEDs, a set of HPS luminaires typically used in installations similar to the test site was selected as a reference for current practice. Basic infor-mation about the LEDs and HPS luminaires is shown in Table 1. Sample images of the LED luminaires are shown in Figure 1.
DATA COLLECTION
Field testing was conducted near the facilities of the Illinois Center for Transportation in Rantoul. The test site was on a straight stretch of a two-lane road segment with 11-ft-wide lanes. Four wood poles were installed on the east side of the road. The mounting height and pole spacing were set at 30 ft and 150 ft, respectively. The poles were installed at a 12-ft setback from the outer edge of the road. The arm length of each pole was 12 ft, so that the center of each light source projected over the outer edge of the traveled lane. Because there were four light poles, the test section of the road had three luminaire cycles. No other significant man-made light source existed within the study site. Figure 2 shows the layout of the test section.
Four luminaires of the same kind were tested at a time. After all measurements for a set of the same luminaires were taken, those luminaires were replaced with a set of four luminaires from a differ-ent company. This process was repeated until all three sets of LED luminaires and the set of HPS luminaires had been tested. At no time in the testing process were two different luminaire models installed simultaneously.
The luminaires were installed by professional technicians from the Village of Rantoul who were instructed as to the exact height of the luminaires as well as the zero tilt and rotation desired for this
TABLE 1 General Characteristics of Selected Roadway Luminaires
Type Luminaire ModelSystem Wattage
Fixture Lumens
Lateral Distribution
Vertical Distribution Cutoff
HPS GE M-400 305 28,000 Type III Medium Full
LED GE Evolve 454239 157 9,600 Type III Medium Semi
LED Relume Vue 320 HE 173 12,475 Type II Medium None
LED Cooper Ventus VSTA08 206 15,114 Type III Short Full
(a) (b) (c)
FIGURE 1 Sample images of tested LED luminaires: (a) GE Evolve 454239, (b) Relume Vue 320 HE, and (c) Cooper Ventus VSTA08.
Data collection point number
SW LANE
Outer Row
Outer RowSpan 1
Span 2
Span 3
Pole 1
Pole 2
150’
150’
150’Pole 3
Pole 4
Centerline Row
Centerline Row
NE LANE
1
5
10
15
20
25
30
(a)
(b)
Span 1
150’150’
150’
Span 2 Span 3
1Data collection point number
GRID POINT
POLES
LIGHTS
SW LANE Outer Row
Outer Row
Centerline RowCenterline RowNE LANE
5 10 15 20 25 30
11’5.5’
15’12’ armlength
FIGURE 2 Schematic layout of Rantoul study site: (a) perspective view and (b) top view (SW = southwest; NE = northeast).
58 Transportation Research Record 2384
study. The installation process was done carefully, with ample time allocated for positioning and leveling the fixtures horizontally and vertically.
The grid points shown in Figure 2 are the locations at which the field illuminance was measured. The field tests were conducted in accordance with IESNA LM-50-99 (18), such that (a) there were 10 longitudinal points equally spaced along each luminaire cycle, (b) the longitudinal spacing between two neighboring grid points was 15 ft, and (c) the transverse points were at the quarter points of each lane.
The first longitudinal grid point was located 7.5 ft from the edge of the first light pole. Because the lane width was 11 ft, the transverse spacing between the first row of grid points and the edge of the road-way was 11 ft/4 ft = 2.75 ft. The transverse spacing of the grid points was 11 ft/2 ft = 5.5 ft. Each luminaire cycle included 20 grid points per lane, for a total of 40 grid points in both lanes. Given that there were three luminaire cycles, the study site had a total of 120 grid points, as shown in Figure 2. [For a discussion of good data collection practices and the use of different light meters, see Levin (19).]
Illuminance is defined as “the density of luminous flux incident on a surface area, and it is the quotient of the luminous flux by the area of the surface when the latter is uniformly illuminated” (2). The U.S. customary unit for illuminance is the foot-candle (fc), which equals a light flux of 1 lumen uniformly distributed on a surface 1 ft2 in area. Illuminance data sets were collected for each type of roadway luminaire with a multipoint arrangement of four sensors attached to a Konica Minolta T-10 meter. To follow the gridlike data collection recommended in IESNA LM-50-99, sensors were placed 15 ft apart longitudinally along the road and one-quarter of the lane width across. The four sensors were aligned at the four corners of a moving rectangular frame, so that the illuminance of four points could be measured at a time.
Data were collected during nights without full moon when the pavement was dry and no other visible factors affected its surface. On a given data collection day, one set of data points was collected for the installed LED or HPS; one set of data points constituted three separate readings for each grid point, so as to increase confidence about the accuracy of the measurements. Data were collected for the same luminaires on two different days (often consecutive days); thus, two sets of data (a total of six different measurements) were collected for each type of LED and the HPS. The average discrepancy in field illuminance between Day 1 and Day 2 was 0.07, 0.03, 0.03 and 0.06 fc for the HPS, LED 1, LED 2, and LED 3, respectively; these findings show consistency in the measurements.
In addition to obtaining the field measurements, the researchers modeled the study site with the AGi32 lighting analysis software to compare the field data with software calculations. The AGi32 soft-ware was used to compute expected illuminance levels on the basis of photometric data (IESNA files) provided directly by the manu-facturers. No reductions were applied to the initial luminaire outputs in the AGi32 models because the luminaires were new. Therefore, all luminaire maintenance (i.e., light loss) factors were input as 1.0 in the software calculations, allowing for direct comparison of the field data with the AGi32 results.
RESULTS AND ANALYSIS
The results presented in this paper are for the middle span of the test site (i.e., Span 2 in Figure 2). The middle span was selected because it is more representative of a homogeneous section under the provided
lighting, even though the contribution from luminaires beyond the span edges is not expected to be significant (on the basis of software calculations). The middle span is lit by Poles 2 and 3, as shown in Figure 2. Pole 2 is located between Grid Points 10 and 11, and Pole 3 is located between Grid Points 20 and 21. For each row of grid points in the middle span, a separate comparison of the field illuminance data and AGi32 results was made.
The focus of this paper is not on individual products, but on high-lighting the differences in the performance of state-of-the-art LED luminaires. Thus, the luminaires are not identified by their company name or model, but rather as LED 1, LED 2, or LED 3, without any explicit association of labels to companies.
Comparison of Field Data and Software Results
The comparisons between field illuminance measurements and software results for each luminaire are presented in this section.
HPS
Field illuminance and AGi32 estimations for the HPS luminaires are shown in Figure 3. Both the field data and software results locate the maximum illuminance values at the points adjacent to the light poles and the minimum values at the midpoints of the span. In general, the field data matched the AGi32 results closely, except for Grid Points 11 and 20, which were located in the immediate vicinity of the light poles. Differences in illuminance at these points were on the order of 0.9 to 1.8 fc, with field illuminance being higher than the software results.
LED 1
Similar results were found for LED 1: the maximum illuminance was located near the light poles and the minimum values were in the middle of the span (Figure 4). The field and software data matched very well along the whole span, including the edge points, where the differences were less than 0.1 fc.
LED 2
For LED 2, the illuminance values followed a different pattern. The maximum illuminance remained at the span edges, but the mini-mum values were at the third points from the light poles (Points 13 and 18 in Figure 5). A similar trend was observed in the data from both the field and the software, but with significant discrepancies. These discrepancies were mostly at the points near the poles, where the field illuminance was lower by up to 2.4 fc as compared with the AGi32 results.
LED 3
The illuminance from LED 3 had a flatter, more even distribution than that of the other luminaires (Figure 6). As with the HPS and LED 1, the maximum illuminance values were located near the light poles and the minimum values at the midpoints of the span. However, the match between the field and software data was unlike the match for the other luminaires, with field values that were continuously lower
Medina, Avrenli, and Benekohal 59
Illuminance(ft-candle)
6.0
5.0
4.0
3.0
2.0
1.0
0.011 12 13 14
FIELD SOFTWARE
15 16 17 18 19 20Point Number
Illuminance(ft-candle)6.0
5.0
4.0
3.0
2.0
1.0
0.011 12 13 14
FIELD SOFTWARE
15 16 17 18 19 20Point Number
Illuminance(ft-candle)6.0
5.0
4.0
3.0
2.0
1.0
0.011 12 13 14
FIELD SOFTWARE
15 16 17 18 19 20Point Number
Point Number
Illuminance(ft-candle)
6.0
5.0
4.0
3.0
2.0
1.0
0.011 12 13 14
FIELD SOFTWARE
15 16 17 18 19 20
(a) (b)
(c) (d)
FIGURE 3 Comparison of field illuminance with AGi32 results for HPS: (a) NE lane, outer row; (b) NE lane, center row; (c) SW lane, center row; and (d) SW lane, outer row.
Illuminance(ft-candle)6.0
5.0
4.0
3.0
2.0
1.0
0.011 12 13 14
FIELD SOFTWARE
15 16 17 18 19 20
Point Number Point Number
Illuminance(ft-candle)
6.0
5.0
4.0
3.0
2.0
1.0
0.011 12 13 14
FIELD SOFTWARE
15 16 17 18 19 20
(a) (b)
FIGURE 4 Comparison of field illuminance with AGi32 for LED 1: (a) NE lane, outer row, and (b) NE lane, center row.(continued on next page)
60 Transportation Research Record 2384
19 20
Illuminance(ft-candle)6.0
5.0
4.0
3.0
2.0
1.0
0.011 12 13 14
FIELD SOFTWARE
15 16 17 18
Point Number
(c)
Illuminance(ft-candle)6.0
5.0
4.0
3.0
2.0
1.0
0.011 12 13 14
FIELD SOFTWARE
15 16 17 18 19 20
Point Number
(d)
FIGURE 4 (continued) Comparison of field illuminance with AGi32 for LED 1: (c) SW lane, center row, and (d) SW lane, outer row.
Illuminance(ft-candle)6.0
5.0
4.0
3.0
2.0
1.0
0.011 12 13 14
FIELD SOFTWARE
15 16 17 18 19 20
Point Number
Illuminance(ft-candle)6.0
5.0
4.0
3.0
2.0
1.0
0.011 12 13 14
FIELD SOFTWARE
15 16 17 18 19 20
Point Number
Point Number
Illuminance(ft-candle)6.0
5.0
4.0
3.0
2.0
1.0
0.011 12 13 14
FIELD SOFTWARE
15 16 17 18 19 20
(a) (b)
(c)
Illuminance(ft-candle)6.0
5.0
4.0
3.0
2.0
1.0
0.011 12 13 14
FIELD SOFTWARE
15 16 17 18 19 20
Point Number
(d)
FIGURE 5 Comparison of field illuminance with AGi32 for LED 2: (a) NE lane, outer row; (b) NE lane, center row; (c) SW lane, center row; and (d) SW lane, outer row.
Medina, Avrenli, and Benekohal 61
than those from the AGi32 analysis. The magnitude of the differences between the field data and AGi32 results was 0.7 fc on average.
Estimating Field Illuminance from Software Results
Comparisons between field illuminance and AGi32 results revealed discrepancies that were product specific and difficult to anticipate. Although the comparison between the field data and software results found discrepancies of less than 0.1 fc for one set of luminaires (LED 1), discrepancies as high as 2.4 fc were found for a different
set of luminaires. The discrepancies between the field data and soft-ware results are not believed to be the result of field measurement errors, because the repeated measurements taken in the field were consistent in all cases.
To obtain a relative measure of the software data to the field measurements, multiplicative factors were calculated such that when these factors were applied to the AGi32 results, a better approximation of the field data was found. A factor was estimated for the average illuminance value of a given point along the grid, so that the modified software results in all four rows across the roadway section provided the closest agreement to field data. The final multiplicative values are given in Table 2.
Illuminance(ft-candle)6.0
5.0
4.0
3.0
2.0
1.0
0.011 12 13 14
FIELD SOFTWARE
15 16 17 18 19 20
Point Number
Illuminance(ft-candle)6.0
5.0
4.0
3.0
2.0
1.0
0.011 12 13 14
FIELD SOFTWARE
15 16 17 18 19 20
Point Number
Point Number
Illuminance(ft-candle)6.0
5.0
4.0
3.0
2.0
1.0
0.011 12 13 14
FIELD SOFTWARE
15 16 17 18 19 20
(a) (b)
(c)
Illuminance(ft-candle)6.0
5.0
4.0
3.0
2.0
1.0
0.011 12 13 14
FIELD SOFTWARE
15 16 17 18 19 20
Point Number
(d)
FIGURE 6 Comparison of field illuminance with AGi32 for LED 3: (a) NE lane, outer row; (b) NE lane, center row; (c) SW lane, center row; and (d) SW lane, outer row.
TABLE 2 Multiplicative Factors for Estimating Field Illuminance from AGi32 Results
As mentioned above, the match between the software and field data resulted in a series of patterns that were specific for each product; consequently, these patterns are reflected in the multiplicative factors. Factors for LED 1 had a constant value of 0.96 for the whole span, but for LED 3 they varied between 0.57 and 0.76, depending on the point along the span. Although the suggested factors are valid for this test condition and may be used for similar luminaires and sites, they may differ significantly for different mounting heights, pole spacings, lane widths, and lane configurations.
After the factors were applied to the AGi32 results, the adjusted AGi32 results matched the field illuminance data closely. Figure 7 shows a sample of the rows that originally had the most critical dif-ferences for the HPS, LED 1, LED 2 and LED 3 (discussed in the previous section), but after the multiplicative factors had been applied to the AGi32 results.
These results show that in certain cases, the performance obtained in the field may be very similar to that expected from the software results (multiplicative factor of 0.96), but in others, it could be signifi-cantly different (multiplicative factor of 0.57). In addition, the illumi-nance from the LED luminaires in the field was generally lower than would be expected from the software results. This can be of concern when the lighting system is designed for a given site with a specific road type and pedestrian conflict level, as explained in the next section.
Potential Use of LEDs for General Roadway Lighting Applications
An example that uses the requirements from the Illinois DOT highway lighting design illustrates the importance of the differences between software-calculated performance and field performance. The Illinois DOT highway lighting design requires the use of both illuminance and luminance. The illuminance criterion, which is the oldest and the simplest, is used to
• “Determine the combined amount of luminous flux reaching critical pavement locations from contributing luminaires” and
• “Calculate how uniformly the luminaires’ combined luminous flux is horizontally distributed over the pavement surface” (19).
Therefore, the illuminance design criteria set the minimum thresh-old value for average maintained horizontal illuminance as well as the maximum allowed value for uniformity ratio. The uniformity ratio is defined as the average maintained horizontal illuminance divided by the minimum horizontal illuminance. The Illinois DOT illuminance design criteria are based on ANSI/IESNA RP-8 and vary according to road type and the level of pedestrian conflict. The average main-tained illuminance is based on all the points in the subject span, but
Illuminance(ft-candle)6.0
5.0
4.0
3.0
2.0
1.0
0.011 12 13 14
FIELD SOFTWARE
15 16 17 18 19 20
Point Number
19 20
Illuminance(ft-candle)6.0
5.0
4.0
3.0
2.0
1.0
0.011 12 13 14
FIELD SOFTWARE
15 16 17 18
Point Number
Point Number
Illuminance(ft-candle)6.0
5.0
4.0
3.0
2.0
1.0
0.011 12 13 14
FIELD SOFTWARE
15 16 17 18 19 20
(a) (b)
(c)
Illuminance(ft-candle)6.0
5.0
4.0
3.0
2.0
1.0
0.011 12 13 14
FIELD SOFTWARE
15 16 17 1819 20 19 20
Point Number
(d)
FIGURE 7 Comparison of field illuminance with adjusted AGi32 results: (a) HPS, SW lane, outer row; (b) LED 1, SW lane, center row; (c) LED 2, NE lane, center row; and (d) LED 3, NE lane, outer row.
Medina, Avrenli, and Benekohal 63
a single minimum illuminance value is used as the denominator in the uniformity ratio and may have great influence in the value of this criterion.
Table 3 shows the comparison of field data and AGi32 results with the Illinois DOT lighting design requirements for illuminance. A roadway facility characterized as a major roadway with medium pedestrian conflicts was assumed for this example (see Table 3). The high, medium, and low pedestrian conflict areas stand for the area classifications of commercial, intermediate, and residential, respectively (19).
Table 3 shows that the differences between software and field data in terms of the average illuminance (for the whole span) could be significant enough to result in noncompliance with minimum requirements. Taking the case of LED 3 as an example, while the average illuminance from the software (2.0 fc) met the requirements for medium (≥1.3 fc) and high (≥1.7 fc) pedestrian conflicts, the actual average from the field was significantly lower, at 1.4 fc. Therefore, in the field installation, LED 3 would just meet the level for medium pedestrian conflicts but would fall short for high pedestrian conflicts, contrary to expectations based on the software results. The field results from two of the three selected LED luminaires (LED 2 and LED 3) met the requirements for major roads with medium and high pedes-trian conflicts, but LED 1 only met the requirements for low pedestrian conflicts.
Light loss factors have not been applied to this simple example, but in practice they would have to be accounted for. Light loss factors are very important for LED arrangements, in that the light output of a luminaire decreases over time without a sudden failure of the light source, even after the luminaire exceeds its expected lifetime. However, accurate light loss factors for LED luminaires are difficult to estimate, given that relamping is not needed and maintenance does not necessarily require cleaning the lenses periodically. Thus, questions remain regarding the value of some of these factors over time.
CONCLUSIONS AND RECOMMENDATIONS
This paper presents the results of repeated field illuminance measure-ments for three different sets of LEDs and one set of HPS roadway luminaires. These measurements were compared with the luminaires’ performance in computer analysis with AGi32 lighting analysis software that used photometric characteristics provided by the man-
ufacturers of the luminaires. The comparisons resulted in patterns that were product specific. In some cases, there was close agreement between the expected illuminance values (those derived from the software) and the field data, but in some others, the differences were more significant.
Multiplicative factors to match the software results to field measure-ments were found to illustrate the magnitude of the differences along the span. The values of these multiplicative factors ranged from 0.96 (relatively small differences) to 0.57 (relatively large differences) for all LEDs. Therefore, to different degrees, the field illuminance levels for the three LED luminaires were overall lower than expected.
In addition, given the location of the discrepancies, the results also suggest that quick checks can be performed by taking illuminance measurements at key points along the span (the span edges and the middle and third points of the cycle) to provide an approximation of the overall differences between field and software results for the whole grid. Differences between software calculations and field mea-surements may play a very important role in the use of the luminaires for a given road and pedestrian conflict classification, as the illumi-nance in the field may not reach the minimum illuminance levels required by public agencies.
The value of this paper is not limited to the comparisons between illuminance field data and software calculations or to the illustration of the importance of such differences. The full sets of repeated field data points for state-of-the-art LED roadway luminaires presented in this paper, which were obtained by following IESNA LM-50-99, are not commonly collected, and, therefore, these data are expected to be useful for both practitioners and researchers alike.
ACKNOWLEDGMENTS
The authors thank the Illinois Department of Transportation for sponsoring this research through the Illinois Center for Transportation.
REFERENCES
1. Kodisinghe, A. Design and Implementation of a Solid State Street Light-ing System. ENEL 698 Graduate Project, 2008. http://www.lutw.org/files/SSL_Street_Lighting_for_Base_Of_Pyramid_from_ENEL_698_Arjuna_Kodisinghe_Apr_2008.pdf. Accessed Nov. 2012.
TABLE 3 Comparison of Field Illuminance Data and AGi32 Results with Illinois DOT Lighting Design Requirements
Illinois DOT Requirement
Criterion HPS LED 1 LED 2 LED 3Major–Higha
Major–Mediumb
Major–Lowc
Field Illuminance Data
Average illuminance (fc) 2.4 1.2 1.5 1.4 ≥1.7 ≥1.3 ≥0.9
Uniformity ratiod 3.5 3.0 2.4 1.8 ≤3.0 ≤3.0 ≤3.0
AGi32 Results
Average illuminance (fc) 2.2 1.2 2.0 2.0 ≥1.7 ≥1.3 ≥0.9
Uniformity ratiod 2.6 2.5 2.2 1.4 ≤3.0 ≤3.0 ≤3.0
aMajor road, high pedestrian conflict.bMajor road, medium pedestrian conflict.cMajor road, low pedestrian conflict.dAverage illuminance divided by minimum illuminance in whole span.
64 Transportation Research Record 2384
2. Chapter 56, Highway Lighting. In Bureau of Design and Environment Manual, Illinois Department of Transportation, Springfield, 2010. http:// www.dot.state.il.us/desenv/BDE%20Manual/BDE/pdf/chap56.pdf. Accessed Nov. 2012.
3. Navigant Consulting, Inc. Energy Savings Estimates of Light Emitting Diodes in Niche Lighting Applications. Building Technologies Program, Office of Energy Efficiency and Renewable Energy, U.S. Department of Energy, 2003. http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/niche_final_report.pdf. Accessed Nov. 2012.
4. Xiaoyun, F., L. Xiaojian, and W. Yan. Research and Analysis of the Design Development and Perspective Technology for LED Lighting Products. Proc., 2009 IEEE 10th International Conference on Computer-Aided Industrial Design and Conceptual Design. http://ieeexplore.ieee.org/search/searchresult.jsp?newsearch=true&queryText=Research+and +Analysis+of+the+Design+Development+and+Perspective+Technology +for+LED+Lighting+Products.
5. Luo, X., W. Xiong, T. Cheng, and S. Liu. Temperature Estimation of High-Power Light Emitting Diode Street Lamp by a Multi-Chip Analyti-cal Solution. IET Optoelectronics, Vol. 3, No. 5, 2009, pp. 225–232.
6. Wang, K., X. Luo, Z. Liu, S. Liu, B. Zhou, and Z. Gan. Optical Analysis of an 80-W Light-Emitting-Diode Street Lamp. Optical Engineering, Vol. 47, No. 1 (013002), 2008, pp. 1–13.
7. Luo, X., T. Cheng, W. Xiong, Z. Gan, and S. Liu. Thermal Analysis of an 80 W Light-Emitting Diode Street Lamp. IET Optoelectronics, Vol. 1, No. 5, 2007, pp. 191–196.
8. Ton, M., S. Foster, C. Calwell, and K. Conway. LED Lighting Tech-nologies and Potential for Near-Term Applications. ECOS Consulting Report E03-114. ECOS Consulting and LED Consulting, 2003. http://www.cee1.org/eval/db_pdf/1128.pdf. Accessed Nov. 2012.
9. Henderson, R. L. LED Street Lighting Test Project Report, April 13, 2009. https://www.progress-energy.com/assets/www/docs/home/LED-streetlight-test-project-report.pdf. Accessed Nov. 2012.
10. Liu, Y., D. L. Ding, C. H. Leung, Y. K. Ho, and M. Lu. Optical Design of a High Brightness LED Street Lamp. Proc., SPIE—The International Society for Optical Engineering, Vol. 7635, Shanghai, China, 2009, pp. 763508-1 to 763508-9.
11. Cook, T., A. Sommer, and T. Pang. Demonstration Assessment of Light Emitting Diode (LED) Street Lighting. Host Site: City of Oakland, Cali-fornia. Application Assessment Report 07142008. Pacific Gas and Electric Company, 2008. http://apps1.eere.energy.gov/buildings/publications/pdfs/ ssl/emerging_tech_report_led_streetlighting.pdf. Accessed Nov. 2012.
12. Outdoor Lighting with LEDs: City of Oakland, CA Street Lighting Report Brief. PNNL-SA-60356. U.S. Department of Energy, 2008. http:// apps1.eere.energy.gov/buildings/publications/pdfs/ssl/oakland_demo_brief.pdf. Accessed Nov. 2012.
13. Pollution Prevention Opportunity Assessment, Retrofitting Street and Park-ing Lot Lights with LED Lights. Aberdeen Proving Ground, Aberdeen, Md., 2009. http://www.apg.army.mil/APGHome/sites/directorates/DPW/environment/AP2G/PDF/led.pdf. Accessed Nov. 2012.
14. Whitaker, T. LED Streetlights Help Toronto Become Brighter and Greener. LEDs Magazine, April 2007. http://ledsmagazine.com/features/4/4/5. Accessed Nov. 2012.
15. Kinzey, B. R., and M. A. Myer. Demonstration Assessment of Light-Emitting Diode (LED) Street Lighting. Host Site: Lija Loop, Portland, Oregon. Pacific Northwest National Laboratory, Richland, Wash., 2009. http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/gateway_lija-loop.pdf. Accessed Nov. 2012.
16. Kinzey, B. R., and M. A. Myer. Demonstration Assessment of Light-Emitting Diode (LED) Roadway Lighting. Host Site: I-35W Bridge, Minneapolis, Minnesota. Pacific Northwest National Laboratory, Rich-land, Wash., 2009. http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/gateway_i-35w-bridge.pdf. Accessed Nov. 2012.
17. Cook, T., J. Shackelford, M. Johnson, and T. Pang. Demonstration Assessment of Light Emitting Diode (LED) Street Lighting, Phase III Continuation. Host Site: City of Oakland, California. Application Assess-ment Report 0726. Pacific Gas and Electric Company, 2008. http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/gateway_oakland-phase3.pdf. Accessed Nov. 2012.
18. IESNA Testing Procedures Committee. Guide for Photometric Measure-ment of Roadway Lighting Installations. IESNA LM-50-99. Illuminating Engineering Society of North America, N.Y. 1999.
19. Levin, R. E. The Photometric Connection—Part 3. Meters and Field Measurements Are Discussed. Lighting Design and Application, Nov. 1982. http://www.ies.org/PDF/100Papers/062c.pdf.
DISCUSSION
John D. BulloughLighting Research Center, Rensselaer Polytechnic Institute
The authors’ study demonstrated the caution that should be exercised in the interpretation of lighting software simulations based on photo-metric data. The National Lighting Product Information Program found that manufacturer-provided photometric data differed from independent test data for individual luminaires enough so that the classification type of as many as half or more of the luminaires would be different from their rated values, regardless of the light source technology they used (20–22). For most of the luminaires tested, the field-measured values of those whose light levels tended to be below the maximums were closer in agreement to the simulated values; this finding may be fortunate, in that it is at these locations that the local-ized light level should be most critical to visibility. The lack of agree-ment for LED 3 in the authors’ sample is a bit disconcerting. Does the photometric data file for this particular luminaire match the data sheet for the model tested regarding total luminaire lumens, maxi-mum luminous intensity, luminaire wattage, and the overall shape of the intensity distribution? Such an investigation could be useful in identifying whether a particular photometric file might inadvertently have been mislabeled on a product’s website.
REFERENCES
20. McColgan, M., J. Van Derlofske, J. D. Bullough, and S. Vasconez. Specifier Reports: Parking Lot and Area Luminaires. National Lighting Product Information Program, Lighting Research Center, Rensselaer Polytechnic Institute, Troy, N.Y., 2004.
21. Radetsky, L. Specifier Reports: Streetlights for Collector Roads. National Lighting Product Information Program, Lighting Research Center, Rensselaer Polytechnic Institute, Troy, N.Y., 2010.
22. Radetsky, L. Specifier Reports: Streetlights for Local Roads. National Lighting Product Information Program, Lighting Research Center, Rensselaer Polytechnic Institute, Troy, N.Y., 2011.
The Visibility Committee peer-reviewed this paper.
