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Understanding the effects of complex seasonality on suburban daily transit ridership

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  • Kashfi, Syeed Anta
  • Bunker, Jonathan M.
  • Yigitcanlar, Tan

Abstract

Fluctuations in transit ridership pattern over the year have always concerned transport planners, operators and researchers. Predominantly, metrological elements have been specified to explain variability in ridership volume. However, the outcome of this research points to new direction to explain ridership fluctuation in Brisbane. It explored the relationship between daily bus ridership, seasonality and weather variables for a one-year period, 2012. Rather than segregating the entire year’s ridership into the four calendar seasons (summer, autumn, spring, and winter), this analysis distributed the yearly ridership into nine complex seasonality blocks. These represent calendar season, school/university (academic) period and their corresponding holidays, as well as other observant holidays such as Christmas. The dominance of complex seasonality over typical calendar season was established through analysis and using Multiple Linear Regression (MLR). This research identified a very strong association between complex seasonality and bus ridership. Furthermore, an expectation that Brisbane’s subtropical summer is unfavourable to transit usage was not supported by the findings of this study. A nil association of precipitation and temperature was observed in this region. Finally, this research developed a ridership estimation model, capable of predicting daily ridership within very limited error range. Following the application of this developed model, the estimated annual time series data of each suburb was analysed using Fourier Transformation to appreciate whether any cyclical effects remained, compared with the original data.

Suggested Citation

  • Kashfi, Syeed Anta & Bunker, Jonathan M. & Yigitcanlar, Tan, 2015. "Understanding the effects of complex seasonality on suburban daily transit ridership," Journal of Transport Geography, Elsevier, vol. 46(C), pages 67-80.
    • Handle: RePEc:eee:jotrge:v:46:y:2015:i:c:p:67-80
    DOI: 10.1016/j.jtrangeo.2015.05.008
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    References listed on IDEAS

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    1. Kalkstein, Adam J & Kuby, Michael & Gerrity, Daniel & Clancy, James J, 2009. "An analysis of air mass effects on rail ridership in three US cities," Journal of Transport Geography, Elsevier, vol. 17(3), pages 198-207.
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    Cited by:

    1. Karnberger, Stephan & Antoniou, Constantinos, 2020. "Network–wide prediction of public transportation ridership using spatio–temporal link–level information," Journal of Transport Geography, Elsevier, vol. 82(C).
    2. Duvarci, Yavuz & Yigitcanlar, Tan & Mizokami, Shoshi, 2015. "Transportation disadvantage impedance indexing: A methodological approach to reduce policy shortcomings," Journal of Transport Geography, Elsevier, vol. 48(C), pages 61-75.
    3. Diego Maria Barbieri & Baowen Lou & Marco Passavanti & Cang Hui & Inge Hoff & Daniela Antunes Lessa & Gaurav Sikka & Kevin Chang & Akshay Gupta & Kevin Fang & Arunabha Banerjee & Brij Maharaj & Louisa, 2021. "Impact of COVID-19 pandemic on mobility in ten countries and associated perceived risk for all transport modes," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-18, February.
    4. Chen, Yu & Lu, Yuqi & Jin, Cheng, 2024. "Spatiotemporal differentiation calendar for car and truck flow on expressways: A case study of Jiangsu, China," Journal of Transport Geography, Elsevier, vol. 116(C).
    5. Wei, Ming & Liu, Yan & Sigler, Thomas & Liu, Xiaoyang & Corcoran, Jonathan, 2019. "The influence of weather conditions on adult transit ridership in the sub-tropics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 125(C), pages 106-118.

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