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How does the weather affect public transit ridership? A model with weather-passenger variations

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  • Wei, Ming

Abstract

Drawing on transit smart card data and weather station records over a 12-month period, this study investigates the influence of local weather conditions on public transit ridership in Brisbane, Australia. Based on the statistical distribution of transit ridership, this study applies a suite of geographically weighted negative binomial regression models to capture the weather–transit ridership relationship at both daily and half-hourly levels. The results reveal that weather exerts significant effects on transit ridership; its effects vary by passenger type and are not fixed across locations and temporal periods. In general, senior passengers are the most sensitive group to variations in weather conditions, and their responses are quite similar across the public transit service area. Morning peak hours are the period when passengers have the strongest weather tolerance. Spatial heterogeneity of weather effects mainly occurs in adult, child, and tertiary student passengers, not seniors and secondary student passengers. In contrast to suburban areas, city centres and university campuses are the locales most likely to have distinct weather responses. The results suggest that it is important for transit operators to understand the spatiotemporal dynamics of the weather–transit ridership relationship to proactively (re)design and adjust scheduling to achieve weather-resilient performance.

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  • Wei, Ming, 2022. "How does the weather affect public transit ridership? A model with weather-passenger variations," Journal of Transport Geography, Elsevier, vol. 98(C).
    • Handle: RePEc:eee:jotrge:v:98:y:2022:i:c:s0966692321002957
    DOI: 10.1016/j.jtrangeo.2021.103242
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    2. Wei, Ming, 2022. "Investigating the influence of weather on public transit passenger’s travel behaviour: Empirical findings from Brisbane, Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 156(C), pages 36-51.
    3. Jiang, Ziran & Lei, Liping & Zhang, Jianzhen & Wang, Chengjin & Ye, Shilin, 2023. "Spatio-temporal evolution and location factors of port and shipping service enterprises: A case study of the Yangtze River Delta," Journal of Transport Geography, Elsevier, vol. 106(C).
    4. Li, Mengya & Kwan, Mei-Po & Hu, Wenyan & Li, Rui & Wang, Jun, 2023. "Examining the effects of station-level factors on metro ridership using multiscale geographically weighted regression," Journal of Transport Geography, Elsevier, vol. 113(C).
    5. Ma, Changxi & Zhang, Bowen & Li, Shukai & Lu, Youpeng, 2024. "Urban rail transit passenger flow prediction with ResCNN-GRU based on self-attention mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).

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