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Shared Automated Mobility with Demand-Side Cooperation: A Proof-of-Concept Microsimulation Study

Author

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  • Lei Zhu

    (Department of System Engineering and Engineering Management, The University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223, USA)

  • Zhouqiao Zhao

    (Center for Environmental Research & Technology, University of California at Riverside, Riverside, CA 92507, USA)

  • Guoyuan Wu

    (Center for Environmental Research & Technology, University of California at Riverside, Riverside, CA 92507, USA)

Abstract

Most existing shared automated mobility (SAM) services assume the door-to-door manner, i.e., the pickup and drop-off (PUDO) locations are the places requested by the customers (or demand-side). While some mobility services offer more affordable riding costs in exchange for a little walking effort from customers, their rationales and induced impacts (in terms of mobility and sustainability) from the system perspective are not clear. This study proposes a demand-side cooperative shared automated mobility (DC-SAM) service framework, aiming to fill this knowledge gap and to assess the mobility and sustainability impacts. The optimal ride matching problem is formulated and solved in an online manner through a micro-simulation model, Simulation of Urban Mobility (SUMO). The objective is to maximize the profit (considering both the revenue and cost) of the proposed SAM service, considering the constraints in seat capacities of shared automated vehicles (SAVs) and comfortable walking distance from the perspective of customers. A case study on a portion of a New York City (NYC) network with a pre-defined fleet size demonstrated the efficacy and promise of the proposed system. The results show that the proposed DC-SAM service can not only significantly reduce the SAV’s operating costs in terms of vehicle-miles traveled (VMT), vehicle-hours traveled (VHT), and vehicle energy consumption (VEC) by up to 53, 46 and 51%, respectively, but can also considerably improve the customer service by 30 and 56%, with regard to customer waiting time (CWT) and trip detour factor (TDF), compared to a heuristic service model. In addition, the demand-side cooperation strategy can bring about additional system-wide mobility and sustainability benefits in the range of 4–10%.

Suggested Citation

  • Lei Zhu & Zhouqiao Zhao & Guoyuan Wu, 2021. "Shared Automated Mobility with Demand-Side Cooperation: A Proof-of-Concept Microsimulation Study," Sustainability, MDPI, vol. 13(5), pages 1-17, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:5:p:2483-:d:505720
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    References listed on IDEAS

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    1. Shaheen, Susan PhD & Cohen, Adam, 2020. "Mobility on Demand in the United States," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt14f893rv, Institute of Transportation Studies, UC Berkeley.
    2. Dumas, Yvan & Desrosiers, Jacques & Soumis, Francois, 1991. "The pickup and delivery problem with time windows," European Journal of Operational Research, Elsevier, vol. 54(1), pages 7-22, September.
    3. Shaheen, Susan PhD & Cohen, Adam, 2018. "Impacts of Shared Mobility," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt9vx1m1t9, Institute of Transportation Studies, UC Berkeley.
    4. Stefan Ropke & David Pisinger, 2006. "An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows," Transportation Science, INFORMS, vol. 40(4), pages 455-472, November.
    5. Shyue Koong Chang & Schonfeld, Paul M., 1991. "Multiple period optimization of bus transit systems," Transportation Research Part B: Methodological, Elsevier, vol. 25(6), pages 453-478, December.
    6. Stiglic, M. & Agatz, N.A.H. & Savelsbergh, M.W.P. & Gradisar, M., 2015. "The Benefits of Meeting Points in Ride-sharing Systems," ERIM Report Series Research in Management ERS-2015-003-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.
    7. Daniel J. Fagnant & Kara M. Kockelman, 2018. "Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas," Transportation, Springer, vol. 45(1), pages 143-158, January.
    8. Badia, Hugo & Estrada, Miquel & Robusté, Francesc, 2014. "Competitive transit network design in cities with radial street patterns," Transportation Research Part B: Methodological, Elsevier, vol. 59(C), pages 161-181.
    9. Iacobucci, Riccardo & McLellan, Benjamin & Tezuka, Tetsuo, 2018. "Modeling shared autonomous electric vehicles: Potential for transport and power grid integration," Energy, Elsevier, vol. 158(C), pages 148-163.
    10. Stiglic, Mitja & Agatz, Niels & Savelsbergh, Martin & Gradisar, Mirko, 2015. "The benefits of meeting points in ride-sharing systems," Transportation Research Part B: Methodological, Elsevier, vol. 82(C), pages 36-53.
    11. Alejandro Tirachini, 2020. "Ride-hailing, travel behaviour and sustainable mobility: an international review," Transportation, Springer, vol. 47(4), pages 2011-2047, August.
    12. Claudio Gambella & Joe Naoum-Sawaya & Bissan Ghaddar, 2018. "The Vehicle Routing Problem with Floating Targets: Formulation and Solution Approaches," INFORMS Journal on Computing, INFORMS, vol. 30(3), pages 554-569, August.
    13. Furuhata, Masabumi & Dessouky, Maged & Ordóñez, Fernando & Brunet, Marc-Etienne & Wang, Xiaoqing & Koenig, Sven, 2013. "Ridesharing: The state-of-the-art and future directions," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 28-46.
    14. Alejandro Henao & Wesley E. Marshall, 2019. "The impact of ride-hailing on vehicle miles traveled," Transportation, Springer, vol. 46(6), pages 2173-2194, December.
    15. Xin Li & Sangen Hu & Wenbo Fan & Kai Deng, 2018. "Modeling an enhanced ridesharing system with meet points and time windows," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-19, May.
    16. Susan Shaheen & Adam Cohen, 2019. "Shared ride services in North America: definitions, impacts, and the future of pooling," Transport Reviews, Taylor & Francis Journals, vol. 39(4), pages 427-442, July.
    17. Shaheen, Susan & Cohen, Adam & Broader, Jacquelyn & Davis, Richard & Brown, Les & Neelakantan, Radha & Gopalakrishna, Deepak, 2020. "Mobility on Demand Planning and Implementation: Current Practices, Innovations, and Emerging Mobility Futures," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt3hc6m2vj, Institute of Transportation Studies, UC Berkeley.
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