NightLight: Passively Mapping Nighttime Sidewalk Light Data for Improved Pedestrian Routing

要旨

Nighttime sidewalk illumination has a significant and unequal influence on where and whether pedestrians walk at night. Despite the importance of pedestrian lighting, there is currently no approach for measuring and communicating how humans experience nighttime sidewalk light levels at scale. We introduce NightLight, a new sensing approach that leverages the ubiquity of smartphones by re-appropriating the built-in light sensor ---traditionally used to adapt screen brightness---to sense pedestrian nighttime lighting conditions. We validated our technique through in-lab and street-based evaluations characterizing performance across phone orientation, phone model, and varying light levels demonstrating the ability to aggregate and map pedestrian-oriented light levels with unaltered smartphones. Additionally, to examine the impact of light level data on pedestrian route choice, we conducted a qualitative user study with 13 participants using a standard map vs. one with pedestrian lighting data from NightLight Our findings demonstrate that people changed their routes in preference of well-light routes during nighttime walking. Our work has implications for expanding personalized navigation and pedestrian route choice and passive urban sensing.

著者
Joseph Breda
University of Washington, Seattle, Washington, United States
Daniel Campos Zamora
University of Washington, Seattle, Washington, United States
Shwetak Patel
University of Washington, Seattle, Washington, United States
Jon E.. Froehlich
University of Washington, Seattle, Washington, United States
DOI

10.1145/3706598.3714299

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714299

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Social Good

Annex Hall F206
5 件の発表
2025-04-29 18:00:00
2025-04-29 19:30:00
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