Building the Timetable from
Bottom-Up Demand: A Micro-Econometric Approach
Dipasis Bhadra, The MITRE Corporation
Jennifer Gentry, The MITRE Corporation
Brendan Hogan, The MITRE Corporation
Michael Wells, The MITRE Corporation
The aviation community has a rich collection of tools that simulate
the operational flows of the National Airspace System (NAS). In nearly
all cases, modeled operational flows of aircraft in the NAS begin with
a schedule generated outside of the model. In the past, the schedule
has been derived by translating the Federal Aviation Administration's
(FAA's) Terminal Area Forecast (TAF) into flights. The downside
to this, however, is that NAS operations are made up of specific airport-to-airport
flows, which may be different from terminal area growth attributable
to those airports. The challenge is to move from a generic traffic count
at a specific terminal to a schedule of flights that includes a "when" and a "where" dimension.
Modeled NAS operational performance is highly dependent on the characteristics
of the forecasted operations; hence it is critical that the traffic
schedule be created correctly. The top-down approach based on TAF projections
achieves its goal of replicating the intended volume of flights at each
airport, but it does not necessarily achieve the desired operational-level
integrity. In other words, the existing method is not capable of forecasting
route-specific growth in operational flows.
At the MITRE Corporation's Center for Advanced Aviation System
Development (CAASD), we are building a framework which attempts to fill
in the gaps mentioned above using a bottom-up, demand-driven micro-econometric
approach. Our ultimate goal is to produce a schedule of flights that
is linked with origin and destination (O&D) operations via passenger
route choice. It should thus be in sync with the Official Airline Guide
(OAG), but not driven by it. Our method is comprised of six basic steps,
beginning with estimation and forecasts of traveler demand between O&D
city pairs, and culminating with the creation of a forecasted schedule
that incorporates all major aspects of passenger demand.
