This preview shows page 1. Sign up to view the full content.
Unformatted text preview: Demand in Air Transportation
Systems
Drs. A. Trani and D. Teodorovic
Department of Civil and Environmental Engineering June 912, 2003 Virginia Tech 1 of 81 Basics Facts
• Air transportation demand is related to the socioeconomic
characteristics of the region in question • Demand for air transportation services is greater in more
developed regions of the world • The noted dependencies between the demand for air
transportation services and the socioeconomic characteristics of
the region are used in the air transportation planning process • This process entails the planning of airports, needed
transportation facilities, route networks, and planning the
network of airways Virginia Tech 2 of 81 Basic Air Transportation Demand Models
• As noted by Kanafani (1983), models of air transportation
demand can be divided into:  Macroscopic models, and
 Microscopic models
• Macroscopic models are used to estimate the development of air
transportation in a certain country or region. Typical indicators
are ﬂights, passengers, cargo, etc. • Microscopic models estimate air transportation demand
between two cities. Typical indicators are the passenger trafﬁc in
a speciﬁc OriginDestination (OD) route and the number of
passengers in each class when there are various tariffs on a
route. Virginia Tech 3 of 81 Basic Air Transportation Demand Models
• Another classiﬁcation of demand models used in air
transportation relates to the number of modes modeled:  Multimode models, and
 Unimode models
• Multimode models attempt to estimate the mode split between
air transportation submodes and other competing models of
transportation. Suitable when more than one mode of
transportation is present in the market to be studied • Unimode models consider a single model in the analysis of air
transport demand. • Various classiﬁcations can be combined (i.e., a multimode,
macroscopic model can be developed) Virginia Tech 4 of 81 Basic Air Transportation Demand Models
• A ﬁnal way to understand of demand models used in air
transportation relates to the number of steps needed in the
evaluation of the demand function:  Single step models (or direct demand models), and
 Multistep models
• Single step models obtain the demand function using one or
several explanatory variable in a single step procedure • Multistep models develop air transportation demand function
in multiple steps starting with a basic intercity demand function
and then applying a suitable mode split analysis to derive air
transport demand Virginia Tech 5 of 81 Where do People Live Around Airports?
1 3346 Airports 0.9
0.8 Towered
Airports (474) Percent of Population 0.7 Hub Airports (135) 0.6
0.5 Large Hub Airports (30) 0.4
TextEnd 0.3 Census 1990 and 2000
Data with 61,224 tracts
in NAS 0.2
0.1
0
0 10 20 30 40
50
60
Distance to Airport (statute miles) Virginia Tech 70 80 90 100 6 of 81 Basics on the American Travel Survey
Find information about ATS at:http://transtats.bts.gov
• Excellent source of information to understand travel patterns in
the U.S.  370,000 household trips
 540,000 person trips
 80,000 households interviewed
• Each survey point has 300+ ﬁelds of information • ATS data is imperfect but some trends and calibration factors can
be obtained from the data Virginia Tech 7 of 81 How do People Travel (American Travel Survey)? Percent Travelers by Air (%) 540,000 person trips
80,000 households 100
90
80
70
60
50
40
30
20
10
0
1.8
1.6
1.4 3500
3000 1.2
x 10 5 2500 1
2000 0.8 1500
0.6 5 $)
Household Income (10 $) 1000
0.4 500
0.2 0 Virginia Tech OneWay Trip Distance (miles) 8 of 81 Observations from ATS 1995
Data: ATS 1995
100 Percent Travelers by Air (%) 90
80
70
60
50 High Income 40
30 Medium Income 20 Low Income 10
0
0 500 1000 1500 2000 2500 3000 3500 OneWay
OneWay Trip Distance (miles) Virginia Tech 9 of 81 Resistance to Travel is a Function of Distance
90 Percent Travelers by Air (%) 80 1000 mile trip 70 60 750 mile trip 50 40 500 mile trip
30 20 10
0.2 0.4 0.6 0.8 1 1.2 1.4 Household Income level (105 $)
$) Virginia Tech 1.6 1.8
5
x 10 10 of 81 Observations of Household Income in the U.S.
Census 2000 Data
Percent of Population 100
90
80
70
60
50
40
30
20
10
0 0 0.5 1 1.5 2 2.5 3
x 10 5 Household Income ($)
Virginia Tech 11 of 81 Sample Results for Intercity Trips in the U.S. Virginia Tech 12 of 81 What we are Trying to Answer
The main goal of the analysis is development of reliable models
that can provide various information to decision makers related to
some of the following questions:
• How many passengers will use air transportation for business
and/or leisure trips at the airport? • What is the expected number of operations (takeoffs and
landings) at the airport? • What is appropriate ﬂeet size? • What is appropriate aircraft mix in the ﬂeet? • What types of airport investments (new runways, air trafﬁc
control modernization, new aircraft types) will improve the
regional, national system? Virginia Tech 13 of 81 Latitude (degrees) Sample OD Pairs (> 400,000 passengers/year) Longitude (degrees) Virginia Tech 14 of 81 Latitude (degrees) Sample OD Pairs (> 200,000 passengers/year) Longitude (degrees) Virginia Tech 15 of 81 Latitude (degrees) Sample OD Pairs (>100,000 passengers/year) Longitude (degrees) Virginia Tech 16 of 81 Demand Estimation Procedures
There are several ways to estimate demand for air transportation
services:
• Expert opinion • Trend extrapolation techniques • Trip rate tables • Market share and market deﬁnition models • Econometric models Virginia Tech 17 of 81 Expert Opinion
• Useful when no other information is available to make
predictions • Relies on focus group analysis (a panel of experts) • Intuition is the basis for estimating demand • Relies on past trends but applies subjective evaluations Virginia Tech 18 of 81 Trend Extrapolation Techniques
• Use of regression models (linear and nonlinear) to assess the
demand in the future • Longterm trend behaviors are most frequently modeled using
linear, quadratic or exponential functions We denote time by t, and the number of air passengers that changes
over time by D(t). Trend models of the air transportation demand
D(t) in a period t are described mathematically by:
D(t ) = a+b⋅t D(t) = a ⋅ b
D(t) = k ⋅ a t b Linear model Exponential Model t
Gompertz Model
Virginia Tech 19 of 81 Other Trend Extrapolation Models
k
D ( t ) =  Logistic Model
– at
1+b⋅e
• These model can be calibrated using historical data about the
airport facility or region. In most cases a transformation using
logarithms is needed to simplify the analysis. • Once a logarithmic transformation has been done we can use
standard regression techniques to the ﬁnd coefﬁcients of the
model Virginia Tech 20 of 81 Example Transformation
Suppose that we have data of demands and time and would like to
use the exponential model: D(t) = a ⋅ b t Apply a logarithmic transformation to get, log D ( t ) = log ( a ) + t ⋅ log ( b )
The new equation is a linear model of the form, y = A + Bt
This new model can be studied easily using standard linear
regression techniques Virginia Tech 21 of 81 Example with Data
Data on passenger trafﬁc at the Belgrade Airport, Serbia, from
19621978
Year Number of
Passengers Year Number of
Passengers 1962 220,726 1971 1,036,311 1963 282,873 1972 1,155,166 1964 329,619 1973 1,434,454 1965 405,191 1974 1,688,247 1966 335,999 1975 2,020,291 1967 399,066 1976 2,047,016 1968 462,919 1977 2,280,972 1969 602,257 1978 2,660,670 1970 838,156 Virginia Tech 22 of 81 Graphical Analysis of Trend Models
Demand (t) Exponential k
Logistic
Linear Time (yrs) Virginia Tech 23 of 81 Demand Function Example
Given data representing demand at an airport (D(t)) we
would like to derive the best nonlinear model to ﬁt the data
to a model of the form:
D(t) = k ⋅ a b t
Gompertz Model k
D ( t ) =  Logistic Model
– at
1+b⋅e Virginia Tech 24 of 81 Data
Given: data pairs for time and Demand (D(t))
Find: the best nonlinear regression equation that correlates
with the data pairs (t, D(t))
Data File: airport2.xls Virginia Tech 25 of 81 Data Set Plot
8000000
7000000
6000000
5000000
Series1 4000000
3000000
2000000
1000000
0
1970 1975 1980 1985 1990 Virginia Tech 1995 2000 2005 26 of 81 Setup of Solver Procedure
The idea is to minimize the Sum of Square Errors of the
data and an assumed regressions equation
• Create a column with values of the assumed regression
equation • Leave parameters of the model as cells in the
spreadsheet (Excel will iterate among any number of
parameters) • Minimize the Sum of the Square Errors (SSE) of the
data • You are done! Virginia Tech 27 of 81 Setup of Solver Virginia Tech 28 of 81 Setup of Solver Cells to Iterate Cell to Minimize
Virginia Tech 29 of 81 Solution Set and Original Data
8000000
7000000
6000000
5000000
Series1 4000000 Series2 3000000
2000000
1000000
0
1970 1975 1980 1985 1990 Virginia Tech 1995 2000 2005 30 of 81 General Remarks
• The logistic curve is perhaps the best representation of the
capacity constraints that an airport gets to experience over time • The exponential model is good to approximate shortterm
behavior of the demand function • The linear model is the easiest to use, but the results in the long
term are usually unacceptable Virginia Tech 31 of 81 Trip Rate Approach
• The idea is to predict trips or demand using socioeconomic
variables of the region  Trips by air = f (income, population)
• Socioeconomic variables such as income, car ownership,
education level, number of industries and other can represent the
levels of activity for air transportation • This approach is difﬁcult to implement over long periods of time
unless an economic model exists for the region of interest • Watch out for the assumptions in the economic model Virginia Tech 32 of 81 Trip Rate Table Approach 8 Persontrips
Per Year
(per
Household) 6 Given : Socioeconomic characteristics
for each county (for all states) 4 2 Predict : a) Number of trips produced
per household/year
for various income levels
b) Trips attracted to a county 0
8
6 10
8 4 Years After
High School Use: Trip rate tables Virginia Tech 6 2 4
0 2 x 10 4 Annual Household
Income ($) 33 of 81 Example of Trip Rate Tables to Predict GA
Demand Virginia Tech 34 of 81 Market Share Models
• Start with a nationallevel picture of the share of an airport • Assume that over time, share of passengers can change or remain
the same as before • For example:  Atlanta handles 5% of the enplanements of the US per
year (695 million in 2000)
 if the number of enplanements in the US is estimated,
then ATL would continue capturing 5% of the total
• These have to use stated preference surveys when studying
multiairport systems Virginia Tech 35 of 81 Econometric Models
• Use of economic variables to predict demand • SE – set of socioeconomic variables (population (current and
forecasted), income, employment, volume of trade, average level
of education,...) • LOS – set of levelofservice variables (service frequencies, total
travel times, departure and arrival schedule, routing, waiting
times, fares, travel costs, schedule reliability, perceived level of
comfort, perceived level of safety, carrier reputation,...) Virginia Tech 36 of 81 General Model
• A general model where demand is a function of socioeconomic
characteristics and the characteristics of the transportation
system can be written in the following general form:
m • D(t) = a ∏ b
S it i i=1 m • D(t) = a ⋅ ∏
i=1 n
bi S it ⋅ ∏ T jt cj j=1 • where: • m  the total number of socioeconomic characteristics, • n the total number of transportation system characteristics, • D(t) – the number of air passenger in year t
Virginia Tech 37 of 81 • Sit  the value of the ith socioeconomic characteristics
in year t • Tjt  the value of the jth transportation system characteristics in
year t • a, bi, cj,  parameters to be estimated Virginia Tech 38 of 81 Choice of Variables in the Model (Sit and Tjt)
Socioeconomic vector Sit (variables)
• Income (most important) • Age and role in household • Car ownership • Household size • Residential location • Profession Virginia Tech 39 of 81 Choice of Variables in the Model (Sit and Tjt)
Transportationrelated variables (for vector Tjt)
• Invehicle travel time • Access, waiting and transfer times • Travel cost • Qualitative variables (comfort, reliability, safety) Virginia Tech 40 of 81 Case Study
Integrated Transportation Systems Model for
NASA Langley Research Center Virginia Tech 41 of 81 Application of the Model to Study SATS IMpacts
Airspace and
Airport ATM
Procedures Environmental (noise
and pollution)
CNS
Infrastructure Intercity
Travel
(Mobility
Benefits) Airport
Infrastructure NAS System
Capacity Safety
Energy Use
Virginia Tech Picture source: NASA LaRC 42 of 81 Integrated Approach to Study Air Transportation
Systems (Virginia Tech Model)
Scenaro
i
Definition National and
Re
gional
Economic Models National Airspace
System Feedback
Loop Trip Generation
Analysis Trip Distribution
Analysis Inventory
Studies
Travel Studies
(all modes) Feedback
Loop Transport ation
Vehicle
Intercity vehicle characteristics
Performance Models Information
Technology
Intercity Modal
Split Analysis Intercity Net work
Analysis Intercity Model
Scenario Analysis Virginia Tech Transport ation
Cost Models
Feedback
Loop Metrics
Travel time
Economic benefits
Noise
Traffic densities
Energy use 43 of 81 Model Implementation
Policies
(Op. Capability
Deployment) Time = Base Year
Inventory
Scena
rio
Studies
Definition
Time = 1
Scenario
Studies Inventory
Trip Generation 2
National and
Time = DefinitionTravelmodes) Studies
(all
Analysis
Regio
nal
E
conomic Models Inventory
Scenario
Transportation
Studies
Definition Travel Studies
Intercity vehicle characteristics
Trip G neration
e
National and
(all Vehicle
modes)
Feedback
Performance Models
Re
gional
Trip Distr ibution Analysis
Feedback
Loop
Analysis
Travel Studies
E
conomic Models
Loop
Transportation
Invent ory
Scenario
National Airspace
Trip Generation
National and
(all modes)Definition
Intercity vVehicle
ehicle characteristics
St udies
Information
System
Analysis
Regi nal
o
Feedback
Performance Models
Trip Distribution
Technology
Feedback
Economic Models
Loop
Transportation
Loop Intercity Modal Analysis
National Airspace
Trav el Studies
Intercity vehicle characteristics
Vehicle
Trip Generation
Split Analysis
National and Feedbac
System
(all modes)
Performance Models
Trip Distribution Information k
Transportation
Feedback
Analysis
Loop
Regi
Cost Models Technology
Analysis onal
Loop
Economic Models
National Airspace
Transportation
Intercity Modal
Information
System
Intercity Network Analysis
Vehicle
Intercity vehicle characteristics
Split Feedback
Transportation
Technology
Analysis
Feedback
Performance Models
Loop
Trip Distribution
Feedback
Loop
Intercity Modal Cost Models
Analysis
Loop
National A irspace
Split Analysis
Intercity Network
Transportat ion
Information
Feedback
Sy
stem
Analysis
Cost Models
Loop
Technology
Intercity Model
Scenario Analysis
Intercity Modal
Intercity Netw ork
Feedback
Split Analysis
An
alysis
Transportation
Loop
Inter city Model
Cost Models
Scenario Analysis
Intercity Ne twork
Feedback
Intercity Model
An
alysis
Loop
Scenario Analysis Time = Horizon Year Intercity Model
Scenario Analysis Virginia Tech National Mobility
Metrics 44 of 81 Scenario Deﬁnition 3346 Airports Virginia Tech 45 of 81 Inventory Studies
3346 Airports Runway Length > 3,000
Serves 95% of Aircraft
Population < 12,500 lb.
Per FAA AC 53255 Virginia Tech 46 of 81 Data Analysis of Intermodal Transportation
Systems Source: NPTS
Business trips
(11.86% of total) Virginia Tech 47 of 81 Core Transportation Modules Some Details
of the
Methods
Employed All methods
have been
coded in
MATLAB at the
county level Virginia Tech 48 of 81 Trip Generation Analysis 8 Persontrips
Per Year
(per
Household) 6 Given: Socioeconomic characteristics
for each county (for all states) 4 2 Predict: a) Number of trips produced
per household/year
for various income levels
b) Trips attracted to a county 0
8
6 10
8 4 Years After
High School Use: Trip rate tables Virginia Tech 6 2 x 10 4 4
0 2 Annual Household
Income ($) 49 of 81 Trip Generation Analysis Virginia Tech 50 of 81 Trip Distribution Analysis Given: Trips produced from and
attracted to each county
Predict: a) Number of persontrips
from each origin to every
destination (county to
county) Use: Gravity Model PAF K
j  T =  iij    ijij
n
∑ A jF ijK ij
j1= Virginia Tech 51 of 81 Trip Distribution Analysis
Use of a gravity model to distribute trips across NAS Tij: trips from zone i to zone j
Pi: trip productions at zone i, Ai: trip attraction to zone j,
Fij: friction factor between zone i and j, and
Kij: trip production between zone i and j. Virginia Tech 52 of 81 Mode Split Analysis Given : Trips from each origin to
each destination
Predict : a) Number of persontrips
for every mode of
transportation available
Use: Nested Multinomial
Logit Model and Diversion Curves
Traveler Aviation General
Aviation Bus Commercial
Aviation Automobile Key variables: travel cost,
access time, travel time, safety Virginia Tech 53 of 81 Mode Split Analysis Temporal Decisions
8:00
am Virginia Tech 54 of 81 Mode Split Analysis
Which Mode? Virginia Tech 55 of 81 Mode Split Analysis BWI
DCA
HEF
JYO
BCB
Destination airport choice modeling
Virginia Tech 56 of 81 Details of the Mode Split Model Virginia Tech 57 of 81 Presentation of a General Logit Model V ijk P ijk = e ∑e V ijr r Pijk  the proportion of trafﬁc between origin I and destination j that
uses kth mode
Vijk  the travel disutility (or utility) traveling from i to j via the kth
mode (a function of travel cost, travel time, waiting time, and
access time) Virginia Tech 58 of 81 Disutility Function (Vijk) V ijk = a 1 x 1 + a 2 x 2 + … + a n x n
where: V ijk is the disutlity to travel from city i to j via mode k
a 1 , a 2 , ..., a n are model coefﬁcients (to be determined from a
stated preference survey) x 1 , x 2 , ..., and x n are attributes associated with mode of
transportation k
Potential attributes of the model are travel cost, travel time,
comfort, frequency of service, etc. Any variable that helps
discriminate two or more modes of transportation
Virginia Tech 59 of 81 Mode Choices are Complex
Travel patterns are heavily influenced
by cost economics Virginia Tech 60 of 81 Calibration of a Mode Split Model
556,000 records ATS record
ATS record
ATS record
ATS record Nested Multinomial Logit
Model Specification 18,000 records ATS random
ATS random
record
record Auto Cost
DB1B
UCB and VT SAS MDC
Method
Synthetic Trip Simulator
Outputs: travel cost
and travel time Airline Cost Sabre Virginia Tech Calibrated Nested
MNLM GA/SATS Cost
Prediction of Mode
Choice (Pijkm) 61 of 81 Types of Travelers Modeled
Consider two trip purposes:  business
 nonbusiness
Consider two types of air transportation travelers:  Limited choice auto/commercial airline travelers
 Choice travelers (can select auto, commercial air and
GA/corporate)
The end result of the analysis yields 4 distinct models to address
mode choice Virginia Tech 62 of 81 Types of Trips Makers and Their Trip Purpose Trip Maker Trip Purpose
Business NonBusiness Limited
Captive
Choice Z Z Choice Z Z Virginia Tech 63 of 81 Modeling Technique
• Use Random Utility Maximization (RUM) technique • We used a multinomial logit formulation with  Travel time
 Relative travel cost (= trip cost / income)
 *Level of GA use (for choice riders)
as the explanatory variables in the disutility expressions Virginia Tech 64 of 81 Virginia Tech 65 of 81 Idea Behind a Logit Model
• • Models how people make decisions while selecting a mode of
transportation among several alternatives V ijk = a 1 x 1 + a 2 x 2 + … + a n x n • The disutility function represents a combined “worth” of the trip
from i to j via mode k. • The model is probabilistic (this is the output of the model) • The model reﬂects individual choice behavior • The model can be calibrated with responses from a stated
preference survey and using Maximum Likelihood techniques Virginia Tech 66 of 81 Variables Used in all Four Models
Variables Used
Captive
Riders
Business
Travelers
NonBusiness
Travelers f(TT,TC)
f(TT,TC) Choice
Riders
f(TT,TC,
Level of Use)
f(TT,TC,
Level of Use) TT = travel time (hrs)
TC = scaled relative travel cost (dim)
Level of Use = percent of trips made via GA Virginia Tech 67 of 81 Logit Model Layout
ATS: American Travel Survey
Auto:Automobile
CA: Commercial Airline
GA: General Aviation ATS
DATABASE CA
CA/Auto
CAPTIVE
Travelers
TRAVELERS CA
GA Accessible
CHOICE
Travelers
TRAVELERS NON BUSINESS TRIP NON BUSINESS TRIP
TOP TOP
TOP
AUTO BUSINESS TRIP BUSINESS TRIP TOP
AUTO CA GA CA AUTO CA AUTO Virginia Tech CA GA 68 of 81 Extracting Data From ATS Virginia Tech 69 of 81 Travel Time Assumptions in the Logit Model
Automobile
Speed
Urban areas: 32.5 (mph)
(or Travel
Highways : 60 (mph)
time)
Access/egress
Distance
Processing
Time
Detouring
(3)
ratio Airline GA Table function(1) Table function(2) (4) 30 (miles) 15 (miles) Orig. airport = 60 (min)
Dest. airport = 30 (min) Orig. airport = 30 (min)
Dest. airport = 20 (min) (3) 1.2  1) Travel distance vs. flight time, Airline (min)
Miles 300 600
3000 3300 3600
Time 50.0 89.0
397 443 485 Data: Boeing 737800 BC/A
Data: 2) Travel distance vs. flight time, GA (min)
Miles 300
600
2700 3000
Time 100.0 178.0
702 974
3)
4) Data: BC/A for Various GA
aircraft Accounts for incremental trip distance due to the connecting flights.
Data extracted from NPTS data. Virginia Tech 70 of 81 Travel Costs in the Logit Model
Automobile
Cost per mile
(Cents/mile)
Access/egress
Distance
Lodging cost3)
Average Occupancy
1) Airline1) GA 31 (cents/mile) Equations2) Combined Cost 4)  30 (miles) 15 (miles) 150 ($/day)
Business trip: 1.2
Nonbusiness trip: 3.0
(travelers/car)  150 ($/day)  All trips: 1.2
(travelers /flight) It is assumed that 9% of air travelers purchase first and business class tickets 2) Airline fares:
fare = distance / ( 0.26 + 0.027 * distance0.727) , For distance > 100 statute miles
fare = distance / (0.67 + 0.241 * distance0.3508), For distance > 100 statute miles
3) If driving time (or flight time for GA) is longer than maximum hours a day,
the lodging cost is added to the total trip cost. We assume the maximum
driving/flight hours/day to be 8 hours. 4) GA cost is obtained by combining costs of three GA aircraft types (SE, ME, JE)
weighted by distance traveled. Virginia Tech 71 of 81 Calibrated Logit Model
Synthetic trips are generated for every passenger in the ATS
database. Travel costs and travel times are computed based on
supply costs functions.
The following model parameters are obtained:
Parameter
Nonbusiness
Trip
Business Trip TTIME
relativeCost
TTIME
relativeCost Estimate tValue 0.0073
4.0405
0.0104
3.0835 4.34
41.54
2.95
19.92 approx.
Pr > t
<.0001
<.0001
0.0032
<.0001 TTIME = travel time (hrs)
TTIME
relativeCost = scaled relative travel cost (dim)
Virginia Tech 72 of 81 Computational Example
• For a traveler with annual household income of $35,000 and
synthesized travel cost and time by mode for a 328mile trip are
as follows:
Travel Time
(hr) Travel Cost
($) Relative Travel Cost
(Cost/HHIncome) Automobile 5.47 32.00 0.09371 Airline 4.54 282.80 0.80800 GA 1.92 492.00 1.40571 Note: HHIncome is scaled to $/100
Virginia Tech 73 of 81 Sample Problem Output (Business)
The following probabilities apply to limited choice intercity
travelers (remember a limited choice rider is one that can only go
via auto or commercial airline)
•
P auto P airline For a business traveler:
( – 0.0104 ) ( 5.47 ) + ( – 3.0835 ) ( 0.09371 ) =  ( – 0.0104 ) ( 5.47 ) + ( – 3.0835 ) ( 0.09371 ) + ( – 0.0104 ) ( 4.54 ) + ( – 3.0835 ) ( 0.808 ) ( – 0.0104 ) ( 4.54 ) + ( – 3.0835 ) ( 0.808 ) =  ( – 0.0104 ) ( 5.47 ) + ( – 3.0835 ) ( 0.09371 ) + ( – 0.0104 ) ( 4.54 ) + ( – 3.0835 ) ( 0.808 ) P auto = 0.90
P airline = 0.10 Virginia Tech 74 of 81 Sample Problem Output (NonBusiness)
The following probabilities apply to limited choice intercity
travelers (remember a limited choice rider is one that can only go
via auto or commercial airline)
•
P auto P airline For a nonbusiness traveler:
( – 0.0073 ) ( 5.47 ) + ( – 4.0405 ) ( 0.09371 ) =  ( – 0.0104 ) ( 5.47 ) + ( – 3.0835 ) ( 0.09371 ) + ( – 0.0104 ) ( 4.54 ) + ( – 3.0835 ) ( 0.808 )
( – 0.0073 ) ( 4.54 ) + ( – 4.0405 ) ( 0.808 ) =  ( – 0.0073 ) ( 5.47 ) + ( – 4.0405 ) ( 0.09371 ) + ( – 0.0073 ) ( 4.54 ) + ( – 4.0405 ) ( 0.808 ) P auto = 0.95
P airline = 0.05 Virginia Tech 75 of 81 0.4 Business Trips
0.35 Percent Travel by Commercial Air) Percent Travelers by Commercial Air Sensitivity Analysis (Income Changes) 0.3 0.25 0.2 0.15
TextEnd
0.1 0.05 0 3.5 4.5 5.5 6.5 7.5
8.5
Income ($) Income ($) Virginia Tech 9.5 10.5 11.5 12.5
x 104 76 of 81 Percent Travelers by Commercial Air
Percent Travel by Commercial Air) Sensitivity Analysis (Income)
0.35 NonBusiness Trips
0.3 0.25 0.2 0.15 TextEnd 0.1 0.05 0 3.5 4.5 5.5 6.5 7.5
8.5
Income ($) Income ($) Virginia Tech 9.5 10.5 11.5 12.5
x 104 77 of 81 Transportation Network Analysis
Airbus A320
Jacksonville  Miami Virginia Tech 78 of 81 Remarks About Logit Models
• Require careful evaluation and individual choice behavior survey
methods • Require calibration using Maximum Likelihood techniques • Relatively simple to use once they have been calibrated • Have been used in the analysis of air transportation in other
context:  Airline consolidation and market share analysis
 Route choice
 Airport choice
 Economics Virginia Tech 79 of 81 Sample Model Used in NAS Simulator
LMI Economic
Impacts Module FAA Policy
Module Exogenous Database
NAS Level of
Service Module
(UMD) Woods and Poole
Database Trip Demand (Tij) NAS Capacity
Modules (UMD/VT) Trip Distribution
Analysis (Tijk)
(Tijk)
Airline Fleet Module
(UCB) Mode Split
Model (Tijkm)
No Sustainable? Yes Airline Operations
Module (MIT) Virginia Tech 80 of 81 Sample Airport Choice Model Virginia Tech 81 of 81 ...
View
Full
Document
This note was uploaded on 12/31/2011 for the course CEE 5614 taught by Professor Staff during the Fall '10 term at Virginia Tech.
 Fall '10
 Staff

Click to edit the document details