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Exploring Walking Behavior in the Streets of New York City Using Hourly Pedestrian Count Data

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  • Jae Min Lee

    (School of Architecture, University of Ulsan, Ulsan 44610, Korea)

Abstract

This paper explores hourly automated pedestrian count data of seven locations in New York City to understand pedestrian walking patterns in cities. Due to practical limitations, such patterns have been studied conceptually; few researchers have explored walking as a continuous, long-term activity. Adopting an automated pedestrian counting method, we documented and observed people walking on city streets and found that unique pedestrian traffic patterns reflect land use, development intensity, and neighborhood characteristics. We observed a threshold of thermal comfort in outdoor activities. People tend to seek shade and avoid solar radiation stronger than 1248 Wh/m 2 at an average air temperature of 25 °C. Automated collection of detailed pedestrian count data provides a new opportunity for urban designers and transportation planners to understand how people walk and to improve our cities to be less dependent on the automobile.

Suggested Citation

  • Jae Min Lee, 2020. "Exploring Walking Behavior in the Streets of New York City Using Hourly Pedestrian Count Data," Sustainability, MDPI, vol. 12(19), pages 1-16, September.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:19:p:7863-:d:417967
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    References listed on IDEAS

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    Cited by:

    1. Yuanyuan Guo & Linchuan Yang & Wenke Huang & Yi Guo, 2020. "Traffic Safety Perception, Attitude, and Feeder Mode Choice of Metro Commute: Evidence from Shenzhen," IJERPH, MDPI, vol. 17(24), pages 1-20, December.
    2. Avital Angel & Achituv Cohen & Sagi Dalyot & Pnina Plaut, 2023. "Impact of COVID-19 policies on pedestrian traffic and walking patterns," Environment and Planning B, , vol. 50(5), pages 1178-1193, June.

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