Air Transportation Systems Lab Virginia Tech Oshkosh, WI July 29- August 3, 2003 Transportation Systems Analysis for the SATS Program

Credits Dr. A.A. Trani (Project Lead) Dr. H. Baik and H. Swingle (Research Scientists) A. Ashiabor, S. Seshadri, K. Murthy, and N. Hinze (Graduate Research Associates)

Integrated Transportation System Decision Support Model Blue = Dynamic Model Yellow = Aerospace Technology NAS Metrics Travel time Economic benefits Noise Traffic densities Energy use

Integrated Transportation System Decision Support Model Socio-economic Data Airports and Characteristics Aircraft Performance and Cost Characteristics Baseline year Horizon Time Air Traffic Structure and CONOPS Air Transportation Network and Schedules

The Role of Aerospace Technology in the Transportation DSM Hypersonic Helicopter Tiltrotor/RIA Subsonic GA / Corporate Supersonic NAS Metrics Travel time Economic benefits Noise Traffic densities Energy use

Questions to be Answered with the Decision Support Model Reduce the travel time from door-to-destination by n percent What price for aerospace technology x is needed to achieve y(%) increase in air transportation demand? Use the integrated transportation systems model to investigate what aerospace technologies are needed to achieve the goal What is the impact of aerospace technology x in the fuel, energy consumption and the environment?

Trip Demand Generation Given: Socio-economic characteristics for each county (for all states) Predict: a) Number of trips produced per household/year for various income levels b) Trips attracted to a county Use: Trip rate tables Annual Household Income ($) Years After High School Person-trips per Year per Household

Intercity Trip are Influenced by Income and Trip Purpose Data: ATS 1995 High Income Medium Income Low Income One-Way Trip Distance (miles) Percent Travelers by Air (%)

U.S. County Population Forecast ( ) Woods and Poole Economic Model

Average U.S. County Household Income Forecast ( ) Woods and Poole Economic Model

Originating Intercity Trip Forecast ( )

Trip Distribution Analysis Given: Trips produced from and attracted to each county Predict: a) Number of person-trips from each origin to every destination (county to county) Use: Gravity Model

Transportation Modal Split Given: Trips from each origin to each destination Predict: a) Number of person-trips for every mode of transportation available Use: Nested Multinomial Logit Model and Diversion Curves Key variables: travel cost, door-to-door travel time, perceived safety

Location of Airports and Population Influences Mode Split Towered Airports (474) Hub Airports (135) Census 1990 and 2000 Data with 61,224 tracts in NAS 3348 Airports Large Hub Airports (30) Travel patterns are heavily influenced by cost economics

Airline Fares Travel costs including airline fares are needed to understand where SATS might be competitive with airline travel. We analyzed 12,000,000 million airline tickets using several statistical models.

Airline Fare Model Parameters Distance traveled Number of passengers traveling between the origin and destination Number of airlines serving the origin and destination Low cost carrier service at the origin and/or destination Origination from hub or non-hub airport Destination at hub or non hub airport

SATS vs. Other Modes: Very Light Jet Aircraft Cost Assumptions on SATS travel: jet aircraft, less than 10,000 pounds total weight, utilized 500 hours per year, no deadhead legs, 3 passengers, two pilots.

SATS vs. Other Modes: Very Light Jet Aircraft Cost Assumptions on SATS travel: jet aircraft, less than 10,000 pounds total weight, utilized 500 hours per year, no deadhead legs, 3 passengers, one pilot.

Air Transportation Cost Models 400 nm stage length 600 nm stage length 1 Professional Pilot, $2.5/gallon fuel cost, 75% load factor Very Light Jet Aircraft Model National Commercial Airline Fare Model

Intermodal Link Analysis: Ground Connection and Access Times ATS NPTS NTAD Trip to Airport SpeedAnalysis AirportProximityAnalysis TimeAnalysis Airport Processing Time Analysis Airport Slack Time Analysis Airport Access and Egress Time Analysis High Population Density Low Population Density

Transportation Network Analysis

Flight Trajectory Aircraft Model Airbus A320 Jacksonville - Miami Flight trajectory module employs the Eurocontrol’s BADA model

Coach Fare Markets (> 75,000 trips / year) Longitude (deg.) Latitude (deg.) 1059 Airport ODs

Transportation Model Outputs

Concluding Remarks An integrated transportation model has been developed The model can be used in the evaluation of future aerospace technologies The model is flexible enough to incorporate new assumptions and models developed elsewhere National-level environmental impacts require a model capable of expressing spatial demand and supply patterns (I.e., equivalent to FEM/CFD techniques used in aerospace)

Acknowledgements and Disclaimer The authors would like to acknowledge the support of NASA Langley Research Center (Stuart Cooke and Jerry Hefner) in this endeavor The opinions expressed in this presentation and the paper are those of the authors and not those of NASA or any other Federal Agency