A large number of physical dimensions and different criteria make choosing the optimal luminance distribution for outdoor lighting difficult. The optimisation becomes even more complex for light-emitting diode-based lighting installations that can be intelligently operated, providing different luminance distributions around a moving traffic participant. This study aims at establishing the main subjective factors for the assessment of the luminance distribution of an intelligent light-emitting diode-based outdoor lighting installation for pedestrians. Semantic-differential scales and Likert scales have been used to identify subjective impressions and to find the main factors for the optimisation of the control of the luminance distribution. Our results reveal two factors that need to be considered when assessing intelligent outdoor lighting installations: a major factor that is related to the subjective feeling of well-being and a minor factor that is related to the physical properties of the environment.
Get full access to this article
View all access options for this article.
References
1.
BoycePRFotiosSRichardsM. Road lighting and energy saving. Lighting Research and Technology2009; 41: 245–260.
2.
Li F, Chen D, Song X, Chen Y. LEDs: A promising energy-saving light source for road lighting: Proceedings of the Asia-Pacific Power and Energy Engineering Conference, Wuhan, China, Mar 28–31: 2009: 2798–2800.
3.
ŽukauskasAVaicekauskasRVittaP. Optimization of solid-state lamps for photobiologically friendly mesopic lighting. Applied Optics2012; 51: 8423–8432.
4.
TaguchiTUchidaYSetomotoTKobashiK. Application of white LED lighting to energy-saving-type street lamps. Proceedings of the SPIE2001; 4278: 7–12.
5.
De Kort YAW, Haans A, Geerdnick L, van Gennip D, Horst M, Servaes J. Psychological building blocks for dynamic road lighting: Understanding light’s role in feelings of safety at night: LS-WLED 2010. Proceedings of the 12 International Symposium on Science and Technology of Light Sources and 3rd International Conference on White LEDs and Solid State Lighting, Eindhoven, The Netherlands, Jul 11–16: 2010: 529–530.
6.
Mendalka M, Gadaj M, Lukasz K, Nyka K. WSN for intelligent lighting system: Proceedings of the 2nd International Conference on Information Technology, Gdansk, Poland, Jun 28–30: 2010: 99–100.
7.
AticiÇÖzçelebiTLukkienJJ. Exploring user-centered intelligent road lighting design: A road map and future research directions. IEEE Transactions on Consumer Electronics2011; 57: 788–793.
8.
VittaPStanikūnasRTuzikasAReklaitisIStonkusAPetrulisAVaitkevičiusHŽukauskasA. Energy-saving approaches to solid state street lighting. Proceedings of the SPIE2011; 8123: 81231H-1– 81231H-11.
9.
SchreuderD. Outdoor Lighting: Physics, Vision and Perception, Berlin: Springer, 2008.
10.
BoycePR. Lighting for Driving, Boca Raton: CRC Press, 2009.
11.
ShinarD. Traffic Safety and Human Behaviour, Bingley: Emerald, 2008.
WanvikPO. Effects of road lighting: An analysis based on Dutch accident statistics 1987–2006. Accident Analysis and Prevention2009; 41: 123–128.
14.
Commission Internationale de l’Eclairage. Road Lighting as an Accident Countermeasure, CIE Publication 93, Vienna: CIE, 1992.
15.
FotiosSAChealC. Predicting lamp spectrum effects at mesopic levels. Part 1: Spatial brightness. Lighting Research and Technology2011; 43: 143–157.
16.
FotiosSAChealC. Predicting lamp spectrum effects at mesopic levels. Part 2: Preferred appearance and visual acuity. Lighting Research and Technology2011; 43: 159–172.
17.
BoycePREklundNHHamiltonBJBrunoLD. Perceptions of safety at night in different lighting conditions. Lighting Research and Technology2000; 32: 79–91.
18.
WalkeyHOrreveteläinenPBarburJHalonenLGoodmanTAlferdinckJFreidingASzalmásA. Mesopic visual efficiency II: Reaction time experiments. Lighting Research and Technology2007; 39: 335–354.
19.
LewisAL. Visual performance as a function of spectral power distribution of light sources at luminances used for general outdoor lighting. Journal of the Illuminating Engineering Society1999; 28: 37–42.
20.
HeYReaMBiermanABulloughJ. Evaluating light source efficacy under mesopic conditions using reaction times. Journal of the Illuminating Engineering Society1997; 26: 125–138.
21.
LinYChenWChenDShaoH. The effect of spectrum on visual field in road lighting. Building and Environment2004; 39: 433–439.
22.
IshidaT. Color identification data obtained from photopic to mesopic illuminance levels. Color Research and Application2002; 27: 252–259.
23.
ShinJCYaguchiHShioiriS. Change of color appearance in photopic, mesopic and scotopic vision. Optical Review2004; 11: 265–271.
24.
YaoQSunYLinY. Research on facial recognition and color identification under CMH and HPS lamps for road lighting. Leukos2009; 6: 169–178.
25.
MayeurABrémondRBastienJMC. Effects of the viewing context on target detection. Implications for road lighting design. Applied Ergonomics2010; 41: 461–468.
26.
HouserKWTillerDK. Measuring the subjective response to interior lighting: paired comparisons and semantic differential scaling. Lighting Research and Technology2003; 35: 183–195.
27.
ZhouBYaoY. Evaluating information retrieval system performance based on user preference. Journal of Intelligent Information Systems2010; 34: 227–248.
28.
CoxEP III. The optimal number of response alternatives for a scale: A review. Journal of Marketing Research1980; 17: 407–422.
29.
CoombsCH. A Theory of Data, New York: John Wiley and Sons, 1964.
30.
SniderGOsgoodCE. Semantic Differential Technique: A Sourcebook, Chicago: Aldine, 1969.
31.
TillerDKReaMS. Semantic differential scaling: Prospects in lighting research. Lighting Research and Technology1992; 24: 43–51.
32.
TrochimWMKDonnellyJP. The Research Methods Knowledge Base, 3rd. Cincinnati: Atomic Dog, 2006.
33.
LoeLMansfieldKPRowlandsE. Appearance of lit environment and its relevance in lighting design: Experimental study. Lighting Research and Technology1994; 26: 119–133.
34.
BoycePRVeitchJANewshamGRJonesCCHeerwagenJMyerMHunterCM. Lighting quality and office work: Two field simulation experiments. Lighting Research and Technology2006; 38: 191–223.
35.
MumcuSDüzenliTÖzbilenA. Prospect and refuge as the predictors of preferences for seating areas. Scientific Research and Essays2010; 5: 1223–1233.
36.
FotiosSAtliD. Comparing judgments of visual clarity and spatial brightness through an analysis of studies using the category rating procedure. Leukos2012; 8: 261–281.
37.
Himmelfarb S. The measurement of attitudes. In Eagly AH and Chaiken S, editors. Psychology of Attitudes. Fort Worth, TX: Harcourt Brace Jovanovich, 1993: 23–88.
38.
AppletonJ. The Experience of Landscape, London: John Wiley and Sons, 1975.
39.
LorenzK. King Solomon’s Ring, London: Methuen, 1964.
40.
Appleton J. Prospects and refuges revisited. In Nasar JL editor. Environmental Aesthetics: Theory, Research and Applications. Cambridge, MA: Cambridge University Press, 1988: 27–44.
SiegelSCastellanNJ. Nonparametric Statistics for the Behavioral Sciences, 2nd. New York: McGraw–Hill, 1988.
43.
KaiserHF. The varimax criterion for analytic rotation in factor analysis. Psychometrika1958; 23: 187–200.
44.
KaiserHF. The application of electronic computers to factor analysis. Educational and Psychological Measurement1960; 20: 141–151.
45.
NunnallyJCBernsteinIH. Psychometric Theory, New York: McGraw-Hill, 1994.
46.
HaansAde KortYAW. Light distribution in dynamic street lighting: Two experimental studies on its effects on perceived safety, prospect, concealment, and escape. Journal of Environmental Psychology2012; 32: 342–352.
