Highlights Expected to launch in coming years, air taxis are new aviation ridesharing services. Identified potential sites for building stations based on estimated air taxi demand. Proposed iterative constrained clustering algorithm with novel warm start technique. Tested the approach using millions of real-life New York City taxi records.
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.
Insights on strategic air taxi network infrastructure locations using an iterative constrained clustering approach
2019-06-05
36 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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