Using segmented regression to estimate the optimal illuminance for pedestrian reassurance

CIE 115:2010 gives guidance for the design of road lighting that feeds into the design standards of many nations. There is, however, an emerging awareness that the guidance in CIE 115:2010 needs amendment, and this is being targeted through rapid and long-term revisions. One aim of the rapid revision is to provide an empirical basis for the guidance, with that to be founded in what is already known rather than waiting for further research to be conducted. For P-class lighting, applied to situations where pedestrians are the target user, it has been suggested that pedestrian reassurance (the feeling of safety) is a suitable basis for establishing optimal lighting conditions. This article analyses the relationship between measures of pedestrian reassurance (after-dark evaluations and the difference between daylight and after-dark evaluations) and the conventional measures of lighting in the P class (mean, minimum and uniformity of horizontal illuminance) using the results from previous field studies investigating this. For these data, the day-dark difference provided a better association with illuminance than did after-dark ratings. For the day-dark difference, the association with mean illuminance was better than that with minimum illuminance or uniformity. Segmented regression suggested an optimum mean illuminance of 8.76 lx. To fit within the existing P classes, this could be rounded down to class P3 (mean illuminance = 7.5 lx).

Authors

Institutions

Publication Details

Journal
Lighting Research & Technology
Published
2026-04-07
DOI
https://doi.org/10.1177/14771535261431190
Primary Topic
Impact of Light on Environment and Health
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Using segmented regression to estimate the optimal illuminance for pedestrian reassurance

Steve Fotios
Lighting Research & Technology
Impact of Light on Environment and Health
article

Using segmented regression to estimate the optimal illuminance for pedestrian reassurance

Steve Fotios
article en

Abstract

CIE 115:2010 gives guidance for the design of road lighting that feeds into the design standards of many nations. There is, however, an emerging awareness that the guidance in CIE 115:2010 needs amendment, and this is being targeted through rapid and long-term revisions. One aim of the rapid revision is to provide an empirical basis for the guidance, with that to be founded in what is already known rather than waiting for further research to be conducted. For P-class lighting, applied to situations where pedestrians are the target user, it has been suggested that pedestrian reassurance (the feeling of safety) is a suitable basis for establishing optimal lighting conditions. This article analyses the relationship between measures of pedestrian reassurance (after-dark evaluations and the difference between daylight and after-dark evaluations) and the conventional measures of lighting in the P class (mean, minimum and uniformity of horizontal illuminance) using the results from previous field studies investigating this. For these data, the day-dark difference provided a better association with illuminance than did after-dark ratings. For the day-dark difference, the association with mean illuminance was better than that with minimum illuminance or uniformity. Segmented regression suggested an optimum mean illuminance of 8.76 lx. To fit within the existing P classes, this could be rounded down to class P3 (mean illuminance = 7.5 lx).

Lighting Research & Technology
University of Sheffield (GB)
Sustainable cities and communities
Openalex Percentile: Top 16%
Impact of Light on Environment and Health
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.