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Modeling frequency of rural demand response transit trips

Author

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  • Sultana, Zohora
  • Mishra, Sabyasachee
  • Cherry, Christopher R.
  • Golias, Mihalis M.
  • Tabrizizadeh Jeffers, Saman

Abstract

Captive riders do not have many travel choices to meet the travel needs as fixed route transit services are not generally provided in rural areas. In many states, demand response transit (DRT) services are provided to meet such needs. However, state public agencies face the dilemma of whether to increase or decrease the service availability for on-call services. To enhance decision making of identifying what the causal factors related to DRT trips, the authors present a set of econometric models by integrating a sample DRT data with other explanatory variables such as land use, socio-economic, and demographic characteristics. Seven count data models including Poisson, Negative Binomial, Zero-inflated Poisson, Zero-inflated Negative Binomial (ZINB), Hurdle Poisson, Hurdle Negative Binomial, and ZINB Mixed Effect were developed to understand the factors that affect DRT trips. The ZINB Mixed Effect model that combines a zero-inflated negative binomial model with random effect was found to provide the best fit. A number of factors showed significant relationship with DRT trip frequency including distance, population density, elderly population, average income, and others. Further, the elasticity effects of these different factors were computed to quantify the magnitude of their impact on DRT. The proposed model can be helpful for transit agencies to predict the frequency of DRT trips and to provide adequate services in rural areas.

Suggested Citation

  • Sultana, Zohora & Mishra, Sabyasachee & Cherry, Christopher R. & Golias, Mihalis M. & Tabrizizadeh Jeffers, Saman, 2018. "Modeling frequency of rural demand response transit trips," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 494-505.
    • Handle: RePEc:eee:transa:v:118:y:2018:i:c:p:494-505
    DOI: 10.1016/j.tra.2018.10.006
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    References listed on IDEAS

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    1. Abbas Moghimbeigi & Mohammed Reza Eshraghian & Kazem Mohammad & Brian Mcardle, 2008. "Multilevel zero-inflated negative binomial regression modeling for over-dispersed count data with extra zeros," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(10), pages 1193-1202.
    2. Chao Wang & Mohammed Quddus & Marcus Enoch & Tim Ryley & Lisa Davison, 2014. "Multilevel modelling of Demand Responsive Transport (DRT) trips in Greater Manchester based on area-wide socio-economic data," Transportation, Springer, vol. 41(3), pages 589-610, May.
    3. Davison, Lisa & Enoch, Marcus & Ryley, Tim & Quddus, Mohammed & Wang, Chao, 2014. "A survey of Demand Responsive Transport in Great Britain," Transport Policy, Elsevier, vol. 31(C), pages 47-54.
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    5. Hongtai Yang & Christopher R. Cherry, 2017. "Use characteristics and demographics of rural transit riders: a case study in Tennessee," Transportation Planning and Technology, Taylor & Francis Journals, vol. 40(2), pages 213-227, February.
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    Cited by:

    1. Sharma, Ishant & Mishra, Sabyasachee & Golias, Mihalis M. & Welch, Timothy F. & Cherry, Christopher R., 2020. "Equity of transit connectivity in Tennessee cities," Journal of Transport Geography, Elsevier, vol. 86(C).
    2. Yu, Zhao & Zhao, Pengjun, 2021. "The factors in residents' mobility in rural towns of China: Car ownership, road infrastructure and public transport services," Journal of Transport Geography, Elsevier, vol. 91(C).
    3. Miwa, Tomio & Wang, Jianbiao & Morikawa, Takayuki, 2023. "Are seniors in mountainous areas able to realize their desired trips? A novel approach to estimate trip demand," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    4. Zhao, Pengjun & Yu, Zhao, 2020. "Investigating mobility in rural areas of China: Features, equity, and factors," Transport Policy, Elsevier, vol. 94(C), pages 66-77.

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