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Insights on strategic air taxi network infrastructure locations using an iterative constrained clustering approach

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  • Rajendran, Suchithra
  • Zack, Joshua

Abstract

Expected to launch globally in the forthcoming years, air taxis are brand-new aviation ridesharing services that will be provided by international logistic pioneers. This study is one of the first to estimate the demand for such a service and provide recommendations on the potential location of facilities to support this network operations. We adopt a two-phase approach: phase-1 estimates the demand for air taxi services by taking a subset of the regular taxi customers who are likely to avail this service, and phase-2 proposes a constrained clustering approach, with multimodal transportation-based warm start technique, to identify potential sites for locating infrastructures based on the estimated demand. We test the feasibility of the proposed approach using millions of real-life New York City taxi records. Results indicate that large facilities with a capacity of nearly 150 landings/hour have to be established in JFK International Airport and South Central Park, while smaller stops are required in World Trade Center, Washington Square and Allerton Ballfields. In addition, we evaluate the impact of the commuter’s “willingness to fly” rate, demand fulfillment rate and time-cost tradeoffs. Our analysis shows that the percentage of time savings and “willingness to fly” rate did not significantly impact location decisions and the number of sites, while it is necessary to conduct an intense market study to determine on-road travel limits. Insights provided in this study can act as a decision support tool for any logistics company that is interested in venturing into the air taxi market.

Suggested Citation

  • Rajendran, Suchithra & Zack, Joshua, 2019. "Insights on strategic air taxi network infrastructure locations using an iterative constrained clustering approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 470-505.
    • Handle: RePEc:eee:transe:v:128:y:2019:i:c:p:470-505
    DOI: 10.1016/j.tre.2019.06.003
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    5. Long, Qi & Ma, Jun & Jiang, Feifeng & Webster, Christopher John, 2023. "Demand analysis in urban air mobility: A literature review," Journal of Air Transport Management, Elsevier, vol. 112(C).
    6. Rajendran, Suchithra & Srinivas, Sharan & Grimshaw, Trenton, 2021. "Predicting demand for air taxi urban aviation services using machine learning algorithms," Journal of Air Transport Management, Elsevier, vol. 92(C).
    7. Escribano Macias, Jose & Khalife, Carl & Slim, Joseph & Angeloudis, Panagiotis, 2023. "An integrated vertiport placement model considering vehicle sizing and queuing: A case study in London," Journal of Air Transport Management, Elsevier, vol. 113(C).
    8. Chen, Linlin & Han, Shuihua & Ye, Zhen & Xia, Senmao, 2023. "The optimisation of the location of front distribution centre: A spatio-temporal joint perspective," International Journal of Production Economics, Elsevier, vol. 263(C).
    9. Wei, Qinshuang & Gao, Zhenyu & Clarke, John-Paul & Topcu, Ufuk, 2024. "Risk-aware urban air mobility network design with overflow redundancy," Transportation Research Part B: Methodological, Elsevier, vol. 185(C).
    10. Rajendran, Suchithra & Srinivas, Sharan, 2020. "Air taxi service for urban mobility: A critical review of recent developments, future challenges, and opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    11. Rajendran, Suchithra & Shulman, Jake, 2020. "Study of emerging air taxi network operation using discrete-event systems simulation approach," Journal of Air Transport Management, Elsevier, vol. 87(C).
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