IDEAS home Printed from https://ideas.repec.org/a/kap/transp/v48y2021i4d10.1007_s11116-020-10116-w.html
   My bibliography  Save this article

Relocating shared automated vehicles under parking constraints: assessing the impact of different strategies for on-street parking

Author

Listed:
  • Konstanze Winter

    (Technion - Israel Institute of Technology)

  • Oded Cats

    (Delft University of Technology)

  • Karel Martens

    (Delft University of Technology)

  • Bart Arem

    (Delft University of Technology)

Abstract

With shared mobility services becoming increasingly popular and vehicle automation technology advancing fast, there is an increasing interest in analysing the impacts of large-scale deployment of shared automated vehicles. In this study, a large fleet of shared automated vehicles providing private rides to passengers is introduced to an agent-based simulation model based on the city of Amsterdam, the Netherlands. The fleet is dimensioned for a sufficient service efficiency during peak-hours, meaning that in off-peak hours a substantial share of vehicles is idle, requiring vehicle relocation strategies. This study assesses the performance of zonal pro-active relocation strategies for on-demand passenger transport under constrained curbside parking capacity: (1) demand-anticipation, (2) even supply dispersion and (3) balancing between demand and supply of vehicles. The strategies are analysed in regard to service efficiency (passenger waiting times, operational efficiency), service externalities (driven mileage, parking usage) and service equity (spatial distribution of externalities and service provision). All pro-active relocation strategies are outperformed by a naïve remain-at-drop off-location strategy in a scenario where curbside parking capacity is in abundance. The demand-anticipation heuristic leads to the highest average waiting times due to vehicle bunching at demand-hotspots which results in an uneven usage of parking facilities. The most favourable results in regard to service efficiency and equity are achieved with the heuristics balancing demand and supply, at the costs of higher driven mileage due to the relocation of idle vehicles. These results open up opportunities for municipalities to accompany the introduction of large fleets of shared automated vehicles with suitable curbside management strategies that mitigate undesired effects.

Suggested Citation

  • Konstanze Winter & Oded Cats & Karel Martens & Bart Arem, 2021. "Relocating shared automated vehicles under parking constraints: assessing the impact of different strategies for on-street parking," Transportation, Springer, vol. 48(4), pages 1931-1965, August.
    • Handle: RePEc:kap:transp:v:48:y:2021:i:4:d:10.1007_s11116-020-10116-w
    DOI: 10.1007/s11116-020-10116-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11116-020-10116-w
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11116-020-10116-w?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. Arentze, Theo A. & Timmermans, Harry J. P., 2004. "A learning-based transportation oriented simulation system," Transportation Research Part B: Methodological, Elsevier, vol. 38(7), pages 613-633, August.
    2. Sayarshad, Hamid R. & Chow, Joseph Y.J., 2017. "Non-myopic relocation of idle mobility-on-demand vehicles as a dynamic location-allocation-queueing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 106(C), pages 60-77.
    3. Ben-Elia, Eran & Benenson, Itzhak, 2019. "A spatially-explicit method for analyzing the equity of transit commuters' accessibility," Transportation Research Part A: Policy and Practice, Elsevier, vol. 120(C), pages 31-42.
    4. Bösch, Patrick M. & Becker, Felix & Becker, Henrik & Axhausen, Kay W., 2018. "Cost-based analysis of autonomous mobility services," Transport Policy, Elsevier, vol. 64(C), pages 76-91.
    5. Börjesson, Maria & Eliasson, Jonas & Franklin, Joel, 2012. "Valuations of travel time variability in scheduling versus mean-variance models," Working papers in Transport Economics 2012:2, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    6. Cetin, Tamer & Deakin, Elizabeth, 2019. "Regulation of taxis and the rise of ridesharing," Transport Policy, Elsevier, vol. 76(C), pages 149-158.
    7. Donald Anderson, 2014. "“Not just a taxi”? For-profit ridesharing, driver strategies, and VMT," Transportation, Springer, vol. 41(5), pages 1099-1117, September.
    8. Itf, 2015. "Urban Mobility System Upgrade: How shared self-driving cars could change city traffic," International Transport Forum Policy Papers 6, OECD Publishing.
    9. Wright, Steve & Emele, C. David & Fukumoto, Masayuki & Velaga, Nagendra R. & Nelson, John D., 2014. "The design, management and operation of flexible transport systems: Comparison of experience between UK, Japan and India," Research in Transportation Economics, Elsevier, vol. 48(C), pages 330-338.
    10. Chen, T. Donna & Kockelman, Kara M. & Hanna, Josiah P., 2016. "Operations of a shared, autonomous, electric vehicle fleet: Implications of vehicle & charging infrastructure decisions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 243-254.
    11. Florian Dandl & Michael Hyland & Klaus Bogenberger & Hani S. Mahmassani, 2019. "Evaluating the impact of spatio-temporal demand forecast aggregation on the operational performance of shared autonomous mobility fleets," Transportation, Springer, vol. 46(6), pages 1975-1996, December.
    12. van Engelen, Matti & Cats, Oded & Post, Henk & Aardal, Karen, 2018. "Enhancing flexible transport services with demand-anticipatory insertion heuristics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 110(C), pages 110-121.
    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. Imen Haj Salah & Vasu Dev Mukku & Malte Kania & Tom Assmann & Hartmut Zadek, 2022. "Implications of the Relocation Type and Frequency for Shared Autonomous Bike Service: Comparison between the Inner and Complete City Scenarios for Magdeburg as a Case Study," Sustainability, MDPI, vol. 14(10), pages 1-18, May.
    2. Levin, Michael W., 2022. "A general maximum-stability dispatch policy for shared autonomous vehicle dispatch with an analytical characterization of the maximum throughput," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 258-280.

    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. Winter, Konstanze & Cats, Oded & Martens, Karel & van Arem, Bart, 2021. "Parking space for shared automated vehicles: How less can be more," Transportation Research Part A: Policy and Practice, Elsevier, vol. 143(C), pages 61-77.
    2. Becker, Henrik & Becker, Felix & Abe, Ryosuke & Bekhor, Shlomo & Belgiawan, Prawira F. & Compostella, Junia & Frazzoli, Emilio & Fulton, Lewis M. & Guggisberg Bicudo, Davi & Murthy Gurumurthy, Krishna, 2020. "Impact of vehicle automation and electric propulsion on production costs for mobility services worldwide," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 105-126.
    3. Al-Kanj, Lina & Nascimento, Juliana & Powell, Warren B., 2020. "Approximate dynamic programming for planning a ride-hailing system using autonomous fleets of electric vehicles," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1088-1106.
    4. Yantao Huang & Kara M. Kockelman, 2020. "What will autonomous trucking do to U.S. trade flows? Application of the random-utility-based multi-regional input–output model," Transportation, Springer, vol. 47(5), pages 2529-2556, October.
    5. Younghoon Seo & Donghyun Lim & Woongbee Son & Yeongmin Kwon & Junghwa Kim & Hyungjoo Kim, 2020. "Deriving Mobility Service Policy Issues Based on Text Mining: A Case Study of Gyeonggi Province in South Korea," Sustainability, MDPI, vol. 12(24), pages 1-20, December.
    6. Martin Adler & Stefanie Peer & Tanja Sinozic, 2019. "Autonomous, Connected, Electric Shared vehicles (ACES) and public finance: an explorative analysis," Tinbergen Institute Discussion Papers 19-005/VIII, Tinbergen Institute.
    7. Meyer, Jonas & Becker, Henrik & Bösch, Patrick M. & Axhausen, Kay W., 2017. "Autonomous vehicles: The next jump in accessibilities?," Research in Transportation Economics, Elsevier, vol. 62(C), pages 80-91.
    8. Hyland, Michael & Mahmassani, Hani S., 2020. "Operational benefits and challenges of shared-ride automated mobility-on-demand services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 134(C), pages 251-270.
    9. Noruzoliaee, Mohamadhossein & Zou, Bo, 2022. "One-to-many matching and section-based formulation of autonomous ridesharing equilibrium," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 72-100.
    10. Nadafianshahamabadi, Razieh & Tayarani, Mohammad & Rowangould, Gregory, 2021. "A closer look at urban development under the emergence of autonomous vehicles: Traffic, land use and air quality impacts," Journal of Transport Geography, Elsevier, vol. 94(C).
    11. Rodier, Caroline & Jaller, Miguel & Pourrahmani, Elham & Bischoff, Joschka & Freedman, Joel & Pahwa, Anmol, 2018. "Automated Vehicle Scenarios: Simulation of System-Level Travel Effects Using Agent-Based Demand and Supply Models in the San Francisco Bay Area," Institute of Transportation Studies, Working Paper Series qt4dk3n531, Institute of Transportation Studies, UC Davis.
    12. Tang, Zhe-Yi & Tian, Li-Jun & Wang, David Z.W., 2021. "Multi-modal morning commute with endogenous shared autonomous vehicle penetration considering parking space constraint," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 151(C).
    13. Wadud, Zia & Mattioli, Giulio, 2021. "Fully automated vehicles: A cost-based analysis of the share of ownership and mobility services, and its socio-economic determinants," Transportation Research Part A: Policy and Practice, Elsevier, vol. 151(C), pages 228-244.
    14. Peer, Stefanie & Müller, Johannes & Naqvi, Asjad & Straub, Markus, 2024. "Introducing shared, electric, autonomous vehicles (SAEVs) in sub-urban zones: Simulating the case of Vienna," Transport Policy, Elsevier, vol. 147(C), pages 232-243.
    15. Alejandro Tirachini, 2020. "Ride-hailing, travel behaviour and sustainable mobility: an international review," Transportation, Springer, vol. 47(4), pages 2011-2047, August.
    16. Gelauff, George & Ossokina, Ioulia & Teulings, Coen, 2019. "Spatial and welfare effects of automated driving: Will cities grow, decline or both?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 121(C), pages 277-294.
    17. Zhang, Wenwen & Wang, Kaidi, 2020. "Parking futures: Shared automated vehicles and parking demand reduction trajectories in Atlanta," Land Use Policy, Elsevier, vol. 91(C).
    18. Fielbaum, Andrés & Tirachini, Alejandro & Alonso-Mora, Javier, 2023. "Economies and diseconomies of scale in on-demand ridepooling systems," Economics of Transportation, Elsevier, vol. 34(C).
    19. Rich, Jeppe & Seshadri, Ravi & Jomeh, Ali Jamal & Clausen, Sofus Rasmus, 2023. "Fixed routing or demand-responsive? Agent-based modelling of autonomous first and last mile services in light-rail systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    20. Jamil Hamadneh & Domokos Esztergár-Kiss, 2021. "The Influence of Introducing Autonomous Vehicles on Conventional Transport Modes and Travel Time," Energies, MDPI, vol. 14(14), pages 1-28, July.

    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:48:y:2021:i:4:d:10.1007_s11116-020-10116-w. 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.