IDEAS home Printed from https://ideas.repec.org/p/nex/wpaper/stationchoice.html

Which Station? Access Trips and Bike Share Route Choice

Author

Listed:
  • Jessica Schoner
  • David Levinson

    (Nexus (Networks, Economics, and Urban Systems) Research Group, Department of Civil Engineering, University of Minnesota)

Abstract

Bike share systems are an emerging technology in the United States and worldwide, but little is known about how people integrate bike share trip segments into their daily travel. Through this research, we attempt to fill this knowledge gap by studying how people navigate from place to place using the Nice Ride Minnesota bike share system in Minneapolis and St. Paul. We develop a theoretical model for bike share station choice inspired by research on transit route choice literature. We then model people’s choice of origin station using a conditional logit model to evaluate their sensitivity to time spent walking, deviation from the shortest path, and a set of station amenity and neighborhood control variables. As expected, people prefer to use stations that do not require long detours out of the way to access. However, commuters and non-work travelers differ in how they value the walking portion of their trip, and what station amenities and neighborhood features increase a station’s utility. The results from this study will be important for planners who need a better understanding of bike share user behavior in order to design or optimize their system. The findings also provide a strong foundation for future study about comprehensive route choice analysis of this new bicycling technology.

Suggested Citation

  • Jessica Schoner & David Levinson, 2013. "Which Station? Access Trips and Bike Share Route Choice," Working Papers 000117, University of Minnesota: Nexus Research Group.
    • Handle: RePEc:nex:wpaper:stationchoice
    DOI: 10.25910/Z07C-KX08
    as

    Download full text from publisher

    File URL: https://hdl.handle.net/11299/179838
    File Function: First version, 2013; Second version, 2021
    Download Restriction: no
    File URL: https://libkey.io/10.25910/Z07C-KX08?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
    ---><---

    References listed on IDEAS

    as
    1. Wardman, Mark, 2004. "Public transport values of time," Transport Policy, Elsevier, vol. 11(4), pages 363-377, October.
    2. Piet Bovy & Sascha Hoogendoorn-Lanser, 2005. "Modelling route choice behaviour in multi-modal transport networks," Transportation, Springer, vol. 32(4), pages 341-368, July.
    3. Guo, Zhan, 2011. "Mind the map! The impact of transit maps on path choice in public transit," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(7), pages 625-639, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kumar Dey, Bibhas & Anowar, Sabreena & Eluru, Naveen, 2021. "A framework for estimating bikeshare origin destination flows using a multiple discrete continuous system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 119-133.
    2. Qian, Xiaodong & Jaller, Miguel & Niemeier, Debbie, 2020. "Enhancing equitable service level: Which can address better, dockless or dock-based Bikeshare systems?," Journal of Transport Geography, Elsevier, vol. 86(C).
    3. Martina Fazio & Nadia Giuffrida & Michela Le Pira & Giuseppe Inturri & Matteo Ignaccolo, 2021. "Planning Suitable Transport Networks for E-Scooters to Foster Micromobility Spreading," Sustainability, MDPI, vol. 13(20), pages 1-18, October.
    4. Martin, Elliot W. & Shaheen, Susan A., 2014. "Evaluating public transit modal shift dynamics in response to bikesharing: a tale of two U.S. cities," Journal of Transport Geography, Elsevier, vol. 41(C), pages 315-324.
    5. Martin, Elliot PhD & Shaheen, Susan PhD, 2014. "Evaluating Public Transit Modal Shift Dynamics In Response to Bikesharing: A Tale of Two U.S. Cities," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6x29n876, Institute of Transportation Studies, UC Berkeley.
    6. Teusch, Julian & Saavedra, Bruno Neumann & Scherr, Yannick Oskar & Müller, Jörg P., 2025. "Strategic planning of geo-fenced micro-mobility facilities using reinforcement learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 194(C).
    7. Akbari Majid & Zarghamfard Moslem & Hajisharifi Arezoo & Amir Entekhabi Shahram & Goodarzipour Sadrallah, 2022. "Modelling the Obstacles to using Bicycle Sharing Systems in the Tehran Metropolis: A Structural Analysis," Quaestiones Geographicae, Sciendo, vol. 41(2), pages 109-124, June.

    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. Frappier, Alexis & Morency, Catherine & Trépanier, Martin, 2018. "Measuring the quality and diversity of transit alternatives," Transport Policy, Elsevier, vol. 61(C), pages 51-59.
    2. Menno Yap & Howard Wong & Oded Cats, 2025. "Public transport crowding valuation in a post-pandemic era," Transportation, Springer, vol. 52(1), pages 287-306, February.
    3. Hadas, Yuval & Ranjitkar, Prakash, 2012. "Modeling public-transit connectivity with spatial quality-of-transfer measurements," Journal of Transport Geography, Elsevier, vol. 22(C), pages 137-147.
    4. Liu, Yang & Feng, Tao & Shi, Zhuangbin & He, Mingwei, 2022. "Understanding the route choice behaviour of metro-bikeshare users," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 460-475.
    5. Kumar, Anshuman Anjani & Kang, Jee Eun & Kwon, Changhyun & Nikolaev, Alexander, 2016. "Inferring origin-destination pairs and utility-based travel preferences of shared mobility system users in a multi-modal environment," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 270-291.
    6. Wu, Laiyun & Kang, Jee Eun & Chung, Younshik & Nikolaev, Alexander, 2021. "Inferring origin-Destination demand and user preferences in a multi-modal travel environment using automated fare collection data," Omega, Elsevier, vol. 101(C).
    7. Ikki Kim & Hyoung-Chul Kim & Dong-Jeong Seo & Jung In Kim, 2020. "Calibration of a transit route choice model using revealed population data of smartcard in a multimodal transit network," Transportation, Springer, vol. 47(5), pages 2179-2202, October.
    8. Ding Luo & Oded Cats & Hans Lint, 2020. "Can passenger flow distribution be estimated solely based on network properties in public transport systems?," Transportation, Springer, vol. 47(6), pages 2757-2776, December.
    9. Zhou, Yuyang & Wang, Peiyu & Zheng, Shuyan & Zhao, Minhe & Lam, William H.K. & Chen, Anthony & Sze, N.N. & Chen, Yanyan, 2024. "Modeling dynamic travel mode choices using cumulative prospect theory," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    10. Jacqueline Arriagada & C. Angelo Guevara & Marcela Munizaga & Song Gao, 2025. "An experiential learning-based transit route choice model using large-scale smart-card data," Transportation, Springer, vol. 52(4), pages 1543-1568, August.
    11. Mulley, Corinne & Clifton, Geoffrey Tilden & Balbontin, Camila & Ma, Liang, 2017. "Information for travelling: Awareness and usage of the various sources of information available to public transport users in NSW," Transportation Research Part A: Policy and Practice, Elsevier, vol. 101(C), pages 111-132.
    12. Zahra Navidi & Nicole Ronald & Stephan Winter, 2018. "Comparison between ad-hoc demand responsive and conventional transit: a simulation study," Public Transport, Springer, vol. 10(1), pages 147-167, May.
    13. Tao, Xuezong & Zhu, Lichao, 2020. "Meta-analysis of value of time in freight transportation: A comprehensive review based on discrete choice models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 213-233.
    14. Daniel A Rodriguez & Jennifer Rogers, 2014. "Can Housing and Accessibility Information Influence Residential Location Choice and Travel Behavior? An Experimental Study," Environment and Planning B, , vol. 41(3), pages 534-550, June.
    15. Al-Ayyash, Zahwa & Abou-Zeid, Maya & Kaysi, Isam, 2016. "Modeling the demand for a shared-ride taxi service: An application to an organization-based context," Transport Policy, Elsevier, vol. 48(C), pages 169-182.
    16. Yin-Yen Tseng & Piet Rietveld & Erik Verhoef, 2012. "Unreliable trains and induced rescheduling: implications for cost-benefit analysis," Transportation, Springer, vol. 39(2), pages 387-407, March.
    17. Bickel, Julia & Geržinič, Nejc & van Oort, Niels & de Bruyn, Menno & Molin, Eric, 2026. "Heterogeneity in route choice behaviour during unplanned train disruptions considering the possibility of teleworking," Transportation Research Part A: Policy and Practice, Elsevier, vol. 203(C).
    18. Krčál, Ondřej & Peer, Stefanie & Staněk, Rostislav & Karlínová, Bára, 2019. "Real consequences matter: Why hypothetical biases in the valuation of time persist even in controlled lab experiments," Economics of Transportation, Elsevier, vol. 20(C).
    19. Marie Karen Anderson & Otto Anker Nielsen & Carlo Giacomo Prato, 2017. "Multimodal route choice models of public transport passengers in the Greater Copenhagen Area," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 6(3), pages 221-245, September.
    20. Huda, Fuad Yasin & Currie, Graham & Pimenta, Allan & Kamruzzaman, Liton (Md), 2025. "Valuing driverless commuting – Downtown parking impacts," Transport Policy, Elsevier, vol. 173(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning
    • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:nex:wpaper:stationchoice. 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: David Levinson The email address of this maintainer does not seem to be valid anymore. Please ask David Levinson to update the entry or send us the correct address (email available below). General contact details of provider: https://edirc.repec.org/data/nexmnus.html .

    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.