IDEAS home Printed from https://ideas.repec.org/a/kap/transp/v47y2020i2d10.1007_s11116-018-9931-2.html
   My bibliography  Save this article

Influencing factors and heterogeneity in ridership of traditional and app-based taxi systems

Author

Listed:
  • Wenbo Zhang

    (Purdue University)

  • Tho V. Le

    (Purdue University)

  • Satish V. Ukkusuri

    (Purdue University)

  • Ruimin Li

    (Tsinghua University)

Abstract

The growth of app-based taxi services has disrupted the urban taxi market. It has seen significant demand shift between the traditional and emerging app-based taxi services. This study explores the influencing factors for determining the ridership distribution of taxi services. Considering the spatial, temporal, and modal heterogeneity, we propose a mixture modeling structure of spatial lag and simultaneous equation model. A case study is designed with 6-month trip records of two traditional taxi services and one app-based taxi service in New York City. The case study provides insights on not only the influencing factors for taxi daily ridership but also the appropriate settings for model estimation. In specific, the hypothesis testing demonstrates a method for determining the spatial weight matrix, estimation strategies for heterogeneous spatial and temporal units, and the minimum sample size required for reliable parameter estimates. Moreover, the study identifies that daily ridership is mainly influenced by number of employees, vehicle ownership, density of developed area, density of transit stations, density of parking space, bike-rack density, day of the week, and gasoline price. The empirical analyses are expected to be useful not only for researchers while developing and estimating models of taxi ridership but also for policy makers while understanding interactions between the traditional and emerging app-based taxi services.

Suggested Citation

  • Wenbo Zhang & Tho V. Le & Satish V. Ukkusuri & Ruimin Li, 2020. "Influencing factors and heterogeneity in ridership of traditional and app-based taxi systems," Transportation, Springer, vol. 47(2), pages 971-996, April.
    • Handle: RePEc:kap:transp:v:47:y:2020:i:2:d:10.1007_s11116-018-9931-2
    DOI: 10.1007/s11116-018-9931-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11116-018-9931-2
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11116-018-9931-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Felipe F. Dias & Patrícia S. Lavieri & Venu M. Garikapati & Sebastian Astroza & Ram M. Pendyala & Chandra R. Bhat, 2017. "A behavioral choice model of the use of car-sharing and ride-sourcing services," Transportation, Springer, vol. 44(6), pages 1307-1323, November.
    2. Chen, T. Donna & Wang, Yiyi & Kockelman, Kara M., 2015. "Where are the electric vehicles? A spatial model for vehicle-choice count data," Journal of Transport Geography, Elsevier, vol. 43(C), pages 181-188.
    3. Camille Kamga & M. Anil Yazici & Abhishek Singhal, 2015. "Analysis of taxi demand and supply in New York City: implications of recent taxi regulations," Transportation Planning and Technology, Taylor & Francis Journals, vol. 38(6), pages 601-625, August.
    4. Qian, Xinwu & Zhang, Wenbo & Ukkusuri, Satish V. & Yang, Chao, 2017. "Optimal assignment and incentive design in the taxi group ride problem," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 208-226.
    5. Jeanty, P. Wilner & Partridge, Mark & Irwin, Elena, 2010. "Estimation of a spatial simultaneous equation model of population migration and housing price dynamics," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 343-352, September.
    6. Jun Guan Neoh & Maxwell Chipulu & Alasdair Marshall, 2017. "What encourages people to carpool? An evaluation of factors with meta-analysis," Transportation, Springer, vol. 44(2), pages 423-447, March.
    7. Shaheen, Susan PhD & Chan, Nelson & Gaynor, Theresa, 2016. "Casual Carpooling in the San Francisco Bay Area: Understanding User Characteristics, Behaviors, and Motivations," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt4dh2h0rf, Institute of Transportation Studies, UC Berkeley.
    8. Wenbo Zhang & Satish V. Ukkusuri & Chao Yang, 2018. "Modeling the Taxi Drivers’ Customer-Searching Behaviors outside Downtown Areas," Sustainability, MDPI, vol. 10(9), pages 1-23, August.
    9. Zhou, Bin (Brenda) & Kockelman, Kara M., 2009. "Predicting the distribution of households and employment: a seemingly unrelated regression model with two spatial processes," Journal of Transport Geography, Elsevier, vol. 17(5), pages 369-376.
    10. Shaheen, Susan A. & Chan, Nelson D. & Gaynor, Teresa, 2016. "Casual carpooling in the San Francisco Bay Area: Understanding user characteristics, behaviors, and motivations," Transport Policy, Elsevier, vol. 51(C), pages 165-173.
    11. Yang, Kai & Lee, Lung-fei, 2017. "Identification and QML estimation of multivariate and simultaneous equations spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 196(1), pages 196-214.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiaowei Chen & Hongyu Zheng & Ze Wang & Xiqun Chen, 2021. "Exploring impacts of on-demand ridesplitting on mobility via real-world ridesourcing data and questionnaires," Transportation, Springer, vol. 48(4), pages 1541-1561, August.
    2. André de Palma & Lucas Javaudin & Patrick Stokkink & Léandre Tarpin-Pitre, 2021. "Modelling Ridesharing in a Large Network with Dynamic Congestion," THEMA Working Papers 2021-16, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    3. Prateek Bansal & Akanksha Sinha & Rubal Dua & Ricardo Daziano, 2019. "Eliciting Preferences of Ridehailing Users and Drivers: Evidence from the United States," Papers 1904.06695, arXiv.org.
    4. Tho V. Le & Satish V. Ukkusuri, 2019. "Influencing factors that determine the usage of the crowd-shipping services," Papers 1902.08681, arXiv.org.
    5. Omer Faruk Aydin & Ilgin Gokasar & Onur Kalan, 2020. "Matching algorithm for improving ride-sharing by incorporating route splits and social factors," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-23, March.
    6. Anne Aguiléra & Eléonore Pigalle, 2021. "The Future and Sustainability of Carpooling Practices. An Identification of Research Challenges," Sustainability, MDPI, vol. 13(21), pages 1-16, October.
    7. Ye Ma & Biying Yu & Meimei Xue, 2018. "Spatial Heterogeneous Characteristics of Ridesharing in Beijing–Tianjin–Hebei Region of China," Energies, MDPI, vol. 11(11), pages 1-21, November.
    8. Lavieri, Patrícia S. & Bhat, Chandra R., 2019. "Modeling individuals’ willingness to share trips with strangers in an autonomous vehicle future," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 242-261.
    9. Yi, Xu & Lian, Feng & Yang, Zhongzhen, 2022. "Research on commuters’ carpooling behavior in the mobile internet context," Transport Policy, Elsevier, vol. 126(C), pages 14-25.
    10. Anfeng Xu & Jiming Chen & Zihui Liu, 2021. "Exploring the Effects of Carpooling on Travelers’ Behavior during the COVID-19 Pandemic: A Case Study of Metropolitan City," Sustainability, MDPI, vol. 13(20), pages 1-19, October.
    11. Cláudia A. Soares Machado & Nicolas Patrick Marie De Salles Hue & Fernando Tobal Berssaneti & José Alberto Quintanilha, 2018. "An Overview of Shared Mobility," Sustainability, MDPI, vol. 10(12), pages 1-21, November.
    12. María del Carmen Rey-Merchán & Antonio López-Arquillos & Manuela Pires Rosa & Jesús Manuel Gómez-de-Gabriel, 2022. "Proposal for an Institutional Carpooling System among Workers from the Public-Education Sector," Sustainability, MDPI, vol. 14(21), pages 1-10, November.
    13. Leonidas G. Anthopoulos & Dimitrios N. Tzimos, 2021. "Carpooling Platforms as Smart City Projects: A Bibliometric Analysis and Systematic Literature Review," Sustainability, MDPI, vol. 13(19), pages 1-29, September.
    14. André de Palma & Lucas Javaudin & Patrick Stokkink & Léandre Tarpin-Pitre, 2022. "Ride-sharing with inflexible drivers in the Paris metropolitan area," Post-Print hal-03880692, HAL.
    15. Manuel Sánchez-Pérez & Nuria Rueda-López & María Belén Marín-Carrillo & Eduardo Terán-Yépez, 2021. "Theoretical dilemmas, conceptual review and perspectives disclosure of the sharing economy: a qualitative analysis," Review of Managerial Science, Springer, vol. 15(7), pages 1849-1883, October.
    16. Dawei Li & Yuchen Song & Dongjie Liu & Qi Cao & Junlan Chen, 2023. "How carpool drivers choose their passengers in Nanjing, China: effects of facial attractiveness and credit," Transportation, Springer, vol. 50(3), pages 929-958, June.
    17. Xiao Lingling & Zhou Zhitian, 2020. "Traveler’s Willingness to Accept and Provide Carpooling Services: A Case Study in Beijing," Journal of Systems Science and Information, De Gruyter, vol. 8(4), pages 356-366, August.
    18. Shaheen, Susan & Cohen, Adam, 2020. "Chapter 3 - Mobility on demand (MOD) and mobility as a service (MaaS): early understanding of shared mobility impacts and public transit partnerships," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt5030f0cd, Institute of Transportation Studies, UC Berkeley.
    19. Amirmahdi Tafreshian & Neda Masoud & Yafeng Yin, 2020. "Frontiers in Service Science: Ride Matching for Peer-to-Peer Ride Sharing: A Review and Future Directions," Service Science, INFORMS, vol. 12(2-3), pages 44-60, June.
    20. He, Zhengbing, 2021. "Portraying ride-hailing mobility using multi-day trip order data: A case study of Beijing, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 146(C), pages 152-169.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:transp:v:47:y:2020:i:2:d:10.1007_s11116-018-9931-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.