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<p>Traffic signs convey regulatory, warning, and guidance information, ensuring road safety. Often, their visibility is compromised under varying environmental conditions, affecting driver response times. Moreover, a thorough understanding of sign performance across various environmental conditions, particularly under sunlight glare, rain, twilight, and nighttime, has yet to be fully achieved. Besides, there has been insufficient focus on suburban arterial roads, characterised by entirely different vehicle dynamics, mixed-traffic composition, inadequate external illumination and heightened visibility challenges. This study aims to evaluate the visibility of traffic signs on a two-lane road under different conditions. A methodology incorporating video data, machine learning-assisted sign detection, and optical-based visibility estimation was employed. The results revealed significant visibility deficits, with sunlight glare and rain reducing distances by 40–90 percent. Due to their smaller surface area, warning signs exhibited the highest deficits (~ 80 percent). Conversely, night-time retro-reflectivity improved visibility, minimising deficits to as low as 1&#xa0;m, though effectiveness varied with headlight angles and sign materials. A consistent decline in visibility with increasing speed was also observed. The findings emphasise the need for optimised sign placement and improved materials to enhance legibility across diverse conditions, contributing to evidence-based road safety strategies.</p>

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Assessment of Traffic Sign Visibility in Various Conditions

  • 期刊论文

  • 发表日期：

    2025年7月2日

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  • Ayan Kumar Naskar,
  • Pritam Saha

摘要

Traffic signs convey regulatory, warning, and guidance information, ensuring road safety. Often, their visibility is compromised under varying environmental conditions, affecting driver response times. Moreover, a thorough understanding of sign performance across various environmental conditions, particularly under sunlight glare, rain, twilight, and nighttime, has yet to be fully achieved. Besides, there has been insufficient focus on suburban arterial roads, characterised by entirely different vehicle dynamics, mixed-traffic composition, inadequate external illumination and heightened visibility challenges. This study aims to evaluate the visibility of traffic signs on a two-lane road under different conditions. A methodology incorporating video data, machine learning-assisted sign detection, and optical-based visibility estimation was employed. The results revealed significant visibility deficits, with sunlight glare and rain reducing distances by 40–90 percent. Due to their smaller surface area, warning signs exhibited the highest deficits (~ 80 percent). Conversely, night-time retro-reflectivity improved visibility, minimising deficits to as low as 1 m, though effectiveness varied with headlight angles and sign materials. A consistent decline in visibility with increasing speed was also observed. The findings emphasise the need for optimised sign placement and improved materials to enhance legibility across diverse conditions, contributing to evidence-based road safety strategies.