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<p>Rising urban temperatures are increasing heat-related risks for active transportation users, particularly cyclists. Yet the hyperlocal heat burden experienced by bike-share users remains poorly understood. This study integrates 1-m resolution hourly Universal Thermal Climate Index (UTCI) maps with 1,189,667 Citi Bike trips across 767 stations in New York City during August 2018. Using generalized linear models, K-means clustering, and non-parametric tests, we examine how user attributes, trip characteristics, and temporal patterns are associated with accumulated heat stress, defined as route-mean UTCI multiplied by trip duration. Short-term Customers and weekend riders accumulate higher heat stress than Subscribers and weekday riders, primarily because of longer trip durations, although route-level thermal intensity also contributes. Gender- and age-related differences are also observed, but these patterns reflect cumulative thermal burden rather than thermal intensity or physiological vulnerability alone. Clustering identifies three behaviorally distinct trip profiles: High-Burden Long-Duration Riders, Low-Burden Routine Riders, and Standard Weekday Riders. These findings show that cyclist heat burden is shaped by both urban microclimates and travel behavior, offering evidence for prioritizing shade, cooling infrastructure, and heat-risk communication in bike-share systems.</p>

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High-resolution urban microclimate modeling reveals hyperlocal cycling heat stress disparities in New York City

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月5日

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  • Shengao Yi,
  • Ziyi Guo,
  • Xiaojiang Li,
  • Tianhong Zhao,
  • Wei Tu,
  • Ziyi Tang,
  • Zhongjie Lin

摘要

Rising urban temperatures are increasing heat-related risks for active transportation users, particularly cyclists. Yet the hyperlocal heat burden experienced by bike-share users remains poorly understood. This study integrates 1-m resolution hourly Universal Thermal Climate Index (UTCI) maps with 1,189,667 Citi Bike trips across 767 stations in New York City during August 2018. Using generalized linear models, K-means clustering, and non-parametric tests, we examine how user attributes, trip characteristics, and temporal patterns are associated with accumulated heat stress, defined as route-mean UTCI multiplied by trip duration. Short-term Customers and weekend riders accumulate higher heat stress than Subscribers and weekday riders, primarily because of longer trip durations, although route-level thermal intensity also contributes. Gender- and age-related differences are also observed, but these patterns reflect cumulative thermal burden rather than thermal intensity or physiological vulnerability alone. Clustering identifies three behaviorally distinct trip profiles: High-Burden Long-Duration Riders, Low-Burden Routine Riders, and Standard Weekday Riders. These findings show that cyclist heat burden is shaped by both urban microclimates and travel behavior, offering evidence for prioritizing shade, cooling infrastructure, and heat-risk communication in bike-share systems.