IDEAS home Printed from https://ideas.repec.org/p/cdl/itsdav/qt6r6139g8.html
   My bibliography  Save this paper

Dynamic Ridesharing: Exploration of Potential for Reduction in Vehicle Miles Traveled

Author

Listed:
  • Rodier, Caroline
  • Alemi, Farzad
  • Smith, Dylan

Abstract

It is widely recognized that new vehicle and fuel technologies are necessary but not sufficient to meet deep greenhouse gas reduction goals in the United States. Demand management strategies, such as land use, transit, and auto pricing policies, are also needed. These measures, however, have historically faced political challenges and have been difficult to implement. Emerging ridesharing systems now suggest the possibility of a new demand management strategy that may be more politically palatable and reduce the number of vehicle miles traveled (VMT). To date, however, little research has evaluated their potential travel effects, especially on a regional scale. This study used the San Francisco, California, Bay Area activity-based travel demand model to simulate business-as-usual, transit-oriented development, and auto pricing scenarios with and without high, medium, and low ridesharing participation levels. The analysis suggests that relatively large VMT reductions are possible from moderate and high participation levels, but at low participation levels, VMT reductions are negligible. Moderate dynamic ridesharing alone compares favorably, with a 9% reduction in VMT, to transit-oriented development and auto pricing scenarios. The analysis also suggests a potentially promising policy combination: a moderately used regional dynamic ridesharing system with a 10- to 30-cent increase in the per mile cost of auto travel, which together may reduce VMT on the order of 11% to 19%.

Suggested Citation

  • Rodier, Caroline & Alemi, Farzad & Smith, Dylan, 2016. "Dynamic Ridesharing: Exploration of Potential for Reduction in Vehicle Miles Traveled," Institute of Transportation Studies, Working Paper Series qt6r6139g8, Institute of Transportation Studies, UC Davis.
  • Handle: RePEc:cdl:itsdav:qt6r6139g8
    as

    Download full text from publisher

    File URL: https://www.escholarship.org/uc/item/6r6139g8.pdf;origin=repeccitec
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Agatz, Niels A.H. & Erera, Alan L. & Savelsbergh, Martin W.P. & Wang, Xing, 2011. "Dynamic ride-sharing: A simulation study in metro Atlanta," Transportation Research Part B: Methodological, Elsevier, vol. 45(9), pages 1450-1464.
    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. 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.
    2. Fox-Penner, Peter & Gorman, Will & Hatch, Jennifer, 2018. "Long-term U.S transportation electricity use considering the effect of autonomous-vehicles: Estimates & policy observations," Energy Policy, Elsevier, vol. 122(C), pages 203-213.
    3. Liu, Zhiyong & Li, Ruimin & Dai, Jingchen, 2022. "Effects and feasibility of shared mobility with shared autonomous vehicles: An investigation based on data-driven modeling approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 156(C), pages 206-226.
    4. Soria, Jason & Stathopoulos, Amanda, 2021. "Investigating socio-spatial differences between solo ridehailing and pooled rides in diverse communities," Journal of Transport Geography, Elsevier, vol. 95(C).
    5. Circella, Giovanni & Tiedeman, Kate & Handy, Susan & Alemi, Farzad & Mokhtarian, Patricia, 2016. "What Affects U.S. Passenger Travel? Current Trends and Future Perspectives," Institute of Transportation Studies, Working Paper Series qt2w16b8bf, Institute of Transportation Studies, UC Davis.
    6. Mingyang Du & Lin Cheng & Xuefeng Li & Jingzong Yang, 2019. "Investigating the Influential Factors of Shared Travel Behavior: Comparison between App-Based Third Taxi Service and Free-Floating Bike Sharing in Nanjing, China," Sustainability, MDPI, vol. 11(16), pages 1-18, August.
    7. Choi, Yunkyung & Guhathakurta, Subhrajit & Pande, Anurag, 2022. "An empirical Bayes approach to quantifying the impact of transportation network companies (TNCs) operations on travel demand," Transportation Research Part A: Policy and Practice, Elsevier, vol. 161(C), pages 269-283.
    8. María J. Alonso-González & Oded Cats & Niels van Oort & Sascha Hoogendoorn-Lanser & Serge Hoogendoorn, 2021. "What are the determinants of the willingness to share rides in pooled on-demand services?," Transportation, Springer, vol. 48(4), pages 1733-1765, August.
    9. María J. Alonso-González & Oded Cats & Niels van Oort & Sascha Hoogendoorn-Lanser & Serge Hoogendoorn, 0. "What are the determinants of the willingness to share rides in pooled on-demand services?," Transportation, Springer, vol. 0, pages 1-33.
    10. Wei Zhai & Shuqi Gao & Mengyang Liu & Di Wei, 2023. "Examining the effects of climate change perception and commuting experience on the willingness to pay for micro-transit service in Tampa, FL," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-10, December.

    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. Meng Li & Guowei Hua & Haijun Huang, 2018. "A Multi-Modal Route Choice Model with Ridesharing and Public Transit," Sustainability, MDPI, vol. 10(11), pages 1-14, November.
    2. Tafreshian, Amirmahdi & Masoud, Neda, 2022. "A truthful subsidy scheme for a peer-to-peer ridesharing market with incomplete information," Transportation Research Part B: Methodological, Elsevier, vol. 162(C), pages 130-161.
    3. Xingyuan Li & Jing Bai, 2021. "A Ridesharing Choice Behavioral Equilibrium Model with Users of Heterogeneous Values of Time," IJERPH, MDPI, vol. 18(3), pages 1-22, January.
    4. Gurumurthy, Krishna Murthy & Kockelman, Kara M., 2021. "Impacts of shared automated vehicles on airport access and operations, with opportunities for revenue recovery: Case Study of Austin, Texas," Research in Transportation Economics, Elsevier, vol. 90(C).
    5. Dessouky, Maged M & Hu, Shichun, 2021. "Dynamic Routing for Ride-Sharing," Institute of Transportation Studies, Working Paper Series qt6qq8r7hz, Institute of Transportation Studies, UC Davis.
    6. Yue Guo & Fu Xin & Xiaotong Li, 2020. "The market impacts of sharing economy entrants: evidence from USA and China," Electronic Commerce Research, Springer, vol. 20(3), pages 629-649, September.
    7. Zhang, Haoran & Chen, Jinyu & Li, Wenjing & Song, Xuan & Shibasaki, Ryosuke, 2020. "Mobile phone GPS data in urban ride-sharing: An assessment method for emission reduction potential," Applied Energy, Elsevier, vol. 269(C).
    8. Daganzo, Carlos F. & Ouyang, Yanfeng & Yang, Haolin, 2020. "Analysis of ride-sharing with service time and detour guarantees," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 130-150.
    9. Hosni, Hadi & Naoum-Sawaya, Joe & Artail, Hassan, 2014. "The shared-taxi problem: Formulation and solution methods," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 303-318.
    10. Agatz, Niels & Erera, Alan & Savelsbergh, Martin & Wang, Xing, 2012. "Optimization for dynamic ride-sharing: A review," European Journal of Operational Research, Elsevier, vol. 223(2), pages 295-303.
    11. Bhoopalam, Anirudh Kishore & Agatz, Niels & Zuidwijk, Rob, 2018. "Planning of truck platoons: A literature review and directions for future research," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 212-228.
    12. Stiglic, M. & Agatz, N.A.H. & Savelsbergh, M.W.P. & Gradisar, M., 2016. "Enhancing Urban Mobility: Integrating Ride-sharing and Public Transit," ERIM Report Series Research in Management ERS-2016-006-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    13. 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.
    14. Zhan, Xingbin & Szeto, W.Y. & Shui, C.S. & Chen, Xiqun (Michael), 2021. "A modified artificial bee colony algorithm for the dynamic ride-hailing sharing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    15. Tao Yang & Weixin Wang, 2022. "Logistics Network Distribution Optimization Based on Vehicle Sharing," Sustainability, MDPI, vol. 14(4), pages 1-12, February.
    16. Xing Wang & Niels Agatz & Alan Erera, 2018. "Stable Matching for Dynamic Ride-Sharing Systems," Transportation Science, INFORMS, vol. 52(4), pages 850-867, August.
    17. Wang, Jing-Peng & Ban, Xuegang (Jeff) & Huang, Hai-Jun, 2019. "Dynamic ridesharing with variable-ratio charging-compensation scheme for morning commute," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 390-415.
    18. Masoud, Neda & Jayakrishnan, R., 2017. "A decomposition algorithm to solve the multi-hop Peer-to-Peer ride-matching problem," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 1-29.
    19. Yu, Qing & Li, Weifeng & Zhang, Haoran & Chen, Jinyu, 2022. "GPS data in taxi-sharing system: Analysis of potential demand and assessment of fuel consumption based on routing probability model," Applied Energy, Elsevier, vol. 314(C).
    20. Mohammad Peyman & Pedro J. Copado & Rafael D. Tordecilla & Leandro do C. Martins & Fatos Xhafa & Angel A. Juan, 2021. "Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems," Energies, MDPI, vol. 14(19), pages 1-26, October.

    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:cdl:itsdav:qt6r6139g8. 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: Lisa Schiff (email available below). General contact details of provider: https://edirc.repec.org/data/itucdus.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.