SATS Transportation Systems Analysis Overview Virginia SATS Alliance TSAA Group Meeting October...

SATS Transportation Systems Analysis Overview

Virginia SATS Alliance

TSAA Group MeetingOctober 29-30, 2002

Task Objectives

• Quantify current U.S. National travel patterns as a prelude to model SATS as a feasible transportation system (the baseline for this analysis is the year 2000 )

• Develop a suitable framework and algorithms to study SATS as a feasible mode of transportation

• Relate the effect of four SATS technical capabilities and how they would contribute to make SATS a feasible mode of transportation

Participants in the Integrated Transportation Systems Analysis

Study

• Virginia Tech:

– Dr. Hojong Baik

– Mr. Howard Swingle

– Mr. Senanu Ashiabor

– Dr. Antonio Trani

• LMI (Logistics Management Institute)

– Mr. Earl Wingrove

– Dr. Dou Long

• George Mason University

– Dr. George Donohue

– Mr. Arash Yousefi

– Mr. Khurram Qureshi

TransportationTransportationSystems AnalysisSystems AnalysisReportReport

TransportationTransportationNetworkNetworkAnalysisAnalysis(Enroute Study)(Enroute Study)

Transportation Systems Framework

ScenarioDefinition

Intercity ModelScenario Analysis

Intercity NetworkAnalysis

Intercity ModalSplit Analysis

Trip DistributionAnalysis

Travel Studies(all modes)Trip Generation

Analysis

InventoryStudies

FeedbackLoop

FeedbackLoop

FeedbackLoop

InformationTechnology

National AirspaceSystem

TransportationCost Models

Intercity vehicle characteristics

TransportationVehicle

Performance Models

National andRegional

Economic Models

MetricsMetricsTravel timeTravel timeEconomic benefitsEconomic benefitsNoiseNoiseTraffic densitiesTraffic densitiesEnergy useEnergy use

Low LandingLow LandingMinimaMinima

Single Pilot Single Pilot SafetySafety High VolumeHigh Volume

OperationsOperations

Enroute Enroute OperationsOperations

Implementation Scheme (Systems Dynamics)

ScenarioDefinition






Analysis

InventoryStudies

FeedbackLoop

FeedbackLoop

FeedbackLoop






Performance Models


Economic Models

ScenarioDefinition






Analysis

InventoryStudies

FeedbackLoop

FeedbackLoop

FeedbackLoop






Performance Models


Economic Models

ScenarioDefinition






Analysis

InventoryStudies

FeedbackLoop

FeedbackLoop

FeedbackLoop






Performance Models


Economic Models

ScenarioDefinition






Analysis

InventoryStudies

FeedbackLoop

FeedbackLoop

FeedbackLoop






Performance Models


Economic Models

Time = Base YearTime = Base Year

Time = 1Time = 1

Time = 2Time = 2

Time = Horizon YearTime = Horizon Year

National Mobility National Mobility MetricsMetrics

PoliciesPolicies(Op. Capability(Op. CapabilityDeployment)Deployment)

Scenario Definition

Distribution of Airports Considered

3343 Airports3343 Airports

Runway Length Distribution

Runway Length > 3,000Runway Length > 3,000Serves 95% of AircraftServes 95% of AircraftPopulation < 12,500 lb.Population < 12,500 lb.Per FAA AC 5325-5Per FAA AC 5325-5


Baseline Itinerant Operations (TAF)


14 operations/day14 operations/day





Number of Aircraft Based at 3343 Airports

2,221 Runways vs. FAR Part 77 Design Criteria

Approach Lights at 2,221 Airports

Runway Operations Saturation Capacity Envelopes

0 10 20 30 40 50 60 70 80 90 1000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Distance to Airport (statute miles)

Percent of Population TextEnd

Where do People Live around Airports?

Towered Towered Airports (474)Airports (474) Hub Airports (135)Hub Airports (135)

Census 1990 and 2000Census 1990 and 2000Data with 61,224 tractsData with 61,224 tractsin NASin NAS


Large Hub Airports (30)Large Hub Airports (30)

0 0.5 1 1.5 2 2.5 3

x 105

0

10

20

30

40

50

60

70

80

90

100

Distribution of Income in the U.S.

Household Income ($)Household Income ($)

Per

cen

t o

f P

op

ula

tio

nP

erce

nt

of

Po

pu

lati

on

Census 2000 DataCensus 2000 Data

Travel Studies

ScenarioDefinition






Analysis

InventoryStudies

FeedbackLoop

FeedbackLoop

FeedbackLoop






Performance Models


Economic Models

0

500

1000

1500

2000

2500

3000

3500

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

x 105

0

10

20

30

40

50

60

70

80

90

100

The American Travel Survey

One-Way Trip Distance (miles)One-Way Trip Distance (miles)Household Income (10Household Income (1055 $) $)

Per

cen

t T

rave

lers

by

Air

(%

)P

erce

nt

Tra

vele

rs b

y A

ir (

%)

540,000 person trips80,000 households

Trip Rates are Influenced by Income and Trip Purpose

Data: ATS 1995Data: ATS 1995

0 500 1000 1500 2000 2500 3000 35000

10

20

30

40

50

60

70

80

90

100

High IncomeHigh Income

Medium IncomeMedium Income

Low IncomeLow Income

One-Way Trip Distance (miles)One-Way Trip Distance (miles)

Per

cen

t T

rave

lers

by

Air

(%

)P

erce

nt

Tra

vele

rs b

y A

ir (

%)

Data: ATS 1995Data: ATS 1995

Transportation Systems Modeling

Some Details of the

Methods Employed

All methodsAll methodshave beenhave beencoded incoded inMATLAB at theMATLAB at thecounty levelcounty level

ScenarioDefinition






Analysis

InventoryStudies

FeedbackLoop

FeedbackLoop

FeedbackLoop






Performance Models


Economic Models

Trip Generation

2

46

810

x 104

0

2

4

6

80

2

4

6

8

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 tablesAnnual Household

Income ($)

Years AfterHigh School

Person-tripsPer Year

(per Household)

Trip Generation Flowchart

Model Results (after Calibration)

Business Trips and MSABusiness Trips and MSA

Trip Generation Results

ScenarioDefinition






Analysis

InventoryStudies

FeedbackLoop

FeedbackLoop

FeedbackLoop






Performance Models


Economic Models

Trip Distribution

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 ModelTij

PiAjFijKij

AjFij Kijj1=

n∑

-----------------------------------=

Trip Distribution Analysis

• Calibration of Fij factors (impedence function) for business trips

Observed vs Predicted Trip Interchanges

• Good correlation is shown between observed and predicted trip interchanges (business trips)

ScenarioDefinition






Analysis

InventoryStudies

FeedbackLoop

FeedbackLoop

FeedbackLoop






Performance Models


Economic Models

Modal Split Analysis

Transportation Modal Split

Given: Trips from each origin to each destinationPredict: a) Number of person-trips

for every mode of transportation available

Use: Nested Multinomial Logit Model and Diversion Curves

AutomobileAviation

General Commercial

Traveler

Aviation Aviation

Bus

Key variables: travel cost, access time, travel time, safety

Mode Diversion Curves (derived from ATS)

Distance (nm)Distance (nm)

Ground TransportationGround Transportation

Commercial AircraftCommercial Aircraft WeibullWeibull ModelModel

Provide a picture on how people travel across modesProvide a picture on how people travel across modes(shown are diversion curves for business trips and high income)(shown are diversion curves for business trips and high income)

Mode Diversion Curves (cont.)

Distance (nm)Distance (nm)

Ground TransportationGround Transportation

General and Corporate AviationGeneral and Corporate Aviation

Weibull ModelWeibull Model

Provide a picture on how people travel across modesProvide a picture on how people travel across modes(shown are diversion curves for business trips and high income)(shown are diversion curves for business trips and high income)

Diversion Curves Represent Incomplete Information

• Lack of understanding why traveler’s selected a mode

• No information on mode attributes (i.e., cost, convenience, perceived safety, etc.) used by decision makers

• No information on mode availability (i.e. who owns an aircraft or pilot capabilities - for GA trips)

• Data set for GA and corporate travelers is small (as expected from the ATS sample size)

Recommended Next Steps (Modal Split Model)

• Formulate a model split model that captures mode-specific attributes and relates them to decision-maker’s view in selecting a mode (Volpe is designing the experiment)

• Calibrate the modal split model to include SATS as a feasible mode of transportation

• The modal split model will be an unbiased model

Mode Split Model Development

Sample MNLM Mathematical Representation

Utility FunctionUtility Function

Probability of Selecting a ModeProbability of Selecting a Mode

’’s = are model parameterss = are model parametersIVT = in-vehicle timeIVT = in-vehicle timeACC = access timeACC = access timeC/I = cost/income ratioC/I = cost/income ratioWT = intermodal waiting timeWT = intermodal waiting time

Sample Use of the MNLM

Results for a hypothetical 200 nm tripResults for a hypothetical 200 nm trip

Aircraft Cost Model

• Quantifies all the operating costs of GA vehicles including future SATS aircraft (over the life cycle of the vehicle)

• Critical sub-model in modal split analysis

• Uses Business and Commercial Aviation Week database (Operations Planning Guide)

• Use of regression analysis to derive various DOC and IOC factors

Aircraft Cost Model (Cost Categories Considered)

• Variable costs (fuel, maintenance hrs., parts, miscellaneous)

• Fixed costs (hull insurance, liability, software, miscellaneous)

• Periodic costs (engine overhaul, paint, interiors, flight deck upgrades)

• Personnel costs (captain and first officer - if applicable)

• Training costs (crew training and recurrent training, maintenance training)

• Facilities costs (hangar space, office lease, miscellaneous)

• Depreciation cost (amortization of aircraft value)

Small Aircraft Cost Model

• Uses real-data bases collected by Business and Commercial Aviation and ARG/US

• Employs regression models to derive realistic operation costs for Piston, Turboprop and Jet-engine powered aircraft

600 hours / year600 hours / year Jet AircraftJet Aircraft

Comparison of Jet-Aircraft Costs(Air Taxi Operation)

Jet > 20,000 lbJet > 20,000 lb

Jet > 10,000 lbJet > 10,000 lb

Jet < 10,000 lbJet < 10,000 lb

New Generation Ultra-light Business Jet Aircraft (Eclipse 500)

Tot

al O

pera

ting

Cos

t ($

/sea

t-m

ile)

Tot

al O

pera

ting

Cos

t ($

/sea

t-m

ile)

Fuel Cost ($2.8/gal.)Fuel Cost ($2.8/gal.)Professional PilotProfessional PilotFour seatsFour seats

Summary of Total Aircraft Costs

• Corporate turboprop aircraft

– 25-50 cents per Available Seat-Mile (ASM)

• Corporate Jet aircraft

– 45-95 cents ASM

• Regional turboprop aircraft (EMB-120, ATR-72, Saab 340)

– 9.2 to 11.5 cents per ASM

• Regional jets (Bombardier CRJ-200, Embraer 145)

– 9.5 to14.0 cents per ASM

• Transport aircraft (Boeing 737-800, Airbus A321)

– 6.1 to 8.2 cents per ASM

Airport Choice Modeling

• The goal is to distribute trip interchanges across all selected airports

• Currently we modeled 3,343 GA and hub airports

Airport Choice Model (Attractiveness Parameters)

Airport Choice Model

Airport Choice Model (cont.)

Airport Choice Model (Intermediate Airport Selection)

Intermediate airport attractiveness is a function of Intermediate airport attractiveness is a function of airport services and airport operationsairport services and airport operations

Sample Trip with an Intermediate Stop

Cessna Citation II (C550) with 60% load factorCessna Citation II (C550) with 60% load factor

Candidate AirportsCandidate Airports

Aircraft Performance Data: BADA 3.0Aircraft Performance Data: BADA 3.0

Origin Origin AirportAirport

Destination Destination AirportAirport

GA Data Used in the Model

• Utilization factors for GA aircraft are used as part of the trip assignment and airport choice modeling processes

• GAATA data has been criticized in some GA circles so we adjusted the occupancy factors based on anecdotal experience

Transportation Model Outputs

Percentage of Hours FlownPercentage of Hours Flown

Transportation Model Outputs

GA Trips Predicted by the Model

ScenarioDefinition






Analysis

InventoryStudies

FeedbackLoop

FeedbackLoop

FeedbackLoop






Performance Models


Economic Models

Transportation Network Analysis

Transportation Network Analysis

Aircraft Point Performance

Model

Aircraft Trajectory Generator

OD Person-trips by Mode

(from Modal Split Analysis)

Air Traffic Control Operational Flight Rules

Aircraft 4D Trajectories

MicroscopicSimulator

Model (TAAM)

Measures ofEffectiveness

(delays, conflicts, workload, safety)

NAS Sectorand Special Use

Airspace Database

TransportationSystemsAnalysis Modules

(Trip generation,Trip distribution,and Modal Split)

Hourly DemandFunctionDatabase

SATS Concept ofOperations

MacroscopicSimulator

Model (LMINet)

Network Scenario Analysis

• Used fast-time simulation models

– TAAM

– LMI SATS Net

– Flight explorer

• 29,815 aircraft modeled in TAAM (single day operations at 5 ARTCC Centers)

– Included all airline traffic

– Included all GA traffic (estimated from our transportation systems analysis method)

• Derived precursor metrics of workload and sector delays

Methodology Used in Traffic Flow Modeling

ARTCC ZDC Analysis (GMU Study Reported by the Alliance)

Baseline Traffic Flows (George Mason University Analysis)

Enroute Parametric Results

0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2

x 104

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

Enroute Conflict Analysis (Baik et al., 2002)

10 nm

5 nm

3 nm

2 nm

Numbers indicate minimum criteriaseparation

Number of Daily Flights (all types)

Num

ber

of D

aily

Con

flic

ts

Region of Interest = Size of ZDC ARTCC

TAAM simulationsAnalytical Results (AEM model)

LMI SATS Net Model Analysis

• Uses a broader definition of sectors in NAS

Baseline Cumulative Traffic Flows (Airline + GA)

Animation of Baseline NAS Flows

QuickTime™ and aQuickDraw decompressorare needed to see this picture.

Baseline Transportation Analysis Conclusions

• The number of trip-persons using GA as mode in the year 2000 amounted to 13 million. This equates to about 9-10 billion Transported Passenger Miles (TPM) via GA in 2000.

• Based on our study of various transportation data sets, the amount of GA travel in the U.S constitutes a small fraction (<1.2%) of the total trip-persons done in the year 2000.

• SATS has good potential, but the economic (i.e., cost) and performance variables have to be very competitive for the system to thrive in the presence of other modes of transportation

Conclusions (cont.)

• A nested multinomial Logit model has been postulated to estimate modal splits when SATS becomes a feasible model of transportation. This model obviously requires suitable calibration (one of our recommendations).

• A credible demand estimation for SATS is necessary because many of the SATS concepts of operation generated by all participating alliances depend on metrics and analyses derived from the transportation systems analysis presented here

Final Remarks

• All the analyses presented in this report have been integrated into a standard numerical computing environment called MATLAB.

• MATLAB is an off-the-shelf computer environment suitable to handle the large matrices and complex manipulations of the data presented in this report

• The algorithms for trip demand, trip distribution and mode split (including the airport choice model) can be executed in less than one hour at the country-to- county level of detail

Proposed Follow-up Diagram for SATS Transportation Studies

Volpe/VTVolpe/VT

VTVT

ERAUERAU

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