Using Predictive Analytics for Corporate Shuttle Decisions

Understanding the adage, "time is money", corporations utilize business aircraft for optimizing business travel. Business aircraft enable productivity times-savings that are most often not possible by other modes. These savings result from multi-destination day-journeys and one-day business trips that optimize travel time. Within corporate aviation are subsets of operators who use aircraft to provide scheduled air travel service for their employees along predetermined routes. Corporate shuttle decision makers face significant challenges in choosing among the myriad of options for aircraft, routes, schedules, and fleet mix that optimize operational and financial performance unique to their firm’s transportation needs. Historical data are of limited value in projecting mixes of these components, especially without prior service experience and data for analysis. Even with prior service data, the ability to ask strategic questions to evaluate new and changing options argues for modern tools. Analysis tools capable of meeting these challenges would provide companies with the means for rapidly evaluating alternative “what if” scenarios that require comprehensive assessment. Such tools would enable more effective management of corporate shuttle fleets, producing improved bottom-line financial performance as well as top-line capture of opportunity and productivity. One modern analytical approach uses agent-based consumer choice modeling simulations capable of accounting for macro- and micro-economic factors significant to corporate shuttles. Using such a model, three scenarios are analyzed, and compared to historical analysis methods for the same three scenarios: new entrants considering a shuttle operation, executive flight departments expanding into shuttle operations, and current shuttle operators seeking to optimize their program or open new routes. This paper discusses the results of analyzing the macro- and micro- factors for the three scenarios, as well as highlighting additional benefits using agent-based modeling.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee AV080 Standing Committee on Light Commercial and General Aviation.
  • Corporate Authors:

    Transportation Research Board

    500 Fifth Street, NW
    Washington, DC  United States  20001
  • Authors:
    • Grunenwald, Matthew D
    • Parker, Roger A
    • Holmes, Bruce J
  • Conference:
  • Date: 2016

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: References;
  • Pagination: 9p
  • Monograph Title: TRB 95th Annual Meeting Compendium of Papers

Subject/Index Terms

Filing Info

  • Accession Number: 01589951
  • Record Type: Publication
  • Report/Paper Numbers: 16-0144
  • Files: TRIS, TRB, ATRI
  • Created Date: Feb 8 2016 10:36AM