Post on 12-Jan-2016
PASSENGERS’ CHOICE BETWEEN COMPETING AIRPORTS
Radosav Jovanovic
Faculty of Transport and Traffic Engineering, University of Belgrade
Introduction
Liberalization of airline regulation – changes to management and planning of airports
Increased competition Low cost carriers EU expansion Necessity of explicit analysis of airport
choice determinants
On the paper
A model to predict the distribution of business passengers in a MAS
Case study: E-75 HCAS Data used: 2001, 2002, 2003 FTTE air
passengers surveys at Belgrade a/p, 2001 FTTE survey of Serbia originating passengers departing from Budapest a/p
Background
US MASs, London airport system Different functional forms and explanatory
variables Usually: MNL, nested logit model Relevant variables: air fare, flight
frequency, airport accessibility (ground access characteristics)
Proposed Airport Demand Allocation Model
Exponential formula to calculate the effects of choice attributes (FF, ATD, AF) on airport attractiveness
Stage 1 – Indifference equation to relate FRk to ATD variable
Stage 2 – To establish a pattern of airport attractiveness alteration in the region observed
Case Study:
E – 75 HCAS
Stage 1 Specification
100 % flight frequency (FF) ~ 15 % of fare 1 h difference in travel time (ATD) ~ 20-40 % of fare Linear AF to ATD relationship
The compensating frequency ratio
FRk = a*eb*ATD
Equal-attractiveness point (EAP): ATD = p*lnFR – q [hours]
Stage 1 Application Example
Trip to Munich Belgrade versus Budapest airport 2 vs 7 daily-direct flights (FR=7/2) 80 kmph average highway speed 30 minutes border stopping
=> ATD = 94 min, EAP in Backa Topola (157 km north of Belgrade)
Stage 2 Specification
Input variables: Daily-direct FFsATD“S”-curve α parameter-how airport’s
frequency share affects its market share
Five-sequences procedure to calculate the market share attracted
1. FRk = 1.1025*e0.7392*ATD
2. FFD(k) = FRk * FFC
3. LRFD = FFD / FFD(k)
4. RFD = LRFD / (LRFD + LRFC)
5. PSD = (RFD)α / [(RFD)α + (1 - RFD)α]
Stage 2 Application ExampleAirport Choice of Business Travelers, Munich Trip
0
20
40
60
80
100E
qu
alT
ime
po
int
Su
bo
tica
Bac
ka
To
po
la
Ku
la
No
vi
Sad
Ind
jija
PS
[%]
via BUD
via BEG
Different Scenarios Considered
Nine destinations (MUN, FRA, LON, PAR, AMS, MIL, ZUR, VIE, MOS)
Base case (BC) – current levels of airline services
SC1 – BEG FF+1 SC2 – BEG FF+1, BUD FF+1 SC3 – NIS vs BEG distribution (ZUR trip)
Base Case Belgrade Airport Market Shares
0
20
40
60
80
100
-2 -1 0 1 2 3 4
ATD [h]
PS [%]
MUN
FRA
ZUR
AMS
MIL
VIE
MOS
Belgrade Market Growth, SC1
0
5
10
15
20
25
-2 -1 0 1 2 3 4
ATD [h]
PS [%]
MUN
FRA
ZUR
AMS
MIL
VIE
MOS
Belgrade Market Growth, SC2
0
5
10
15
20
-2 -1 0 1 2 3 4
ATD [h]
PS [%]
MUN
FRA
ZUR
AMS
MIL
VIE
MOS
SC3 Nis Airport Market Share
0
20
40
60
80
100
-3 -2 -1 0 1 2 3
ATD [h]
PS [%]
Limitations – Possible Improvements
Absence of authentic preference structure of a Serbian air traveler
Credible calibration of the "S"-curve α parameter (origin and/or destination zone specific)
Getting quantitative perceptive scales from qualitative survey data
Conclusions
Sensitivity analysis (predicting FF and ATD changes effects-redistribution)
“What to offer” at or “where to locate” a new airport
To match the aircraft capacity to demand attracted