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