Exploring the potential for cross-nesting structures in airport-choice analysis: A case-study of the Greater London area

The analysis of air-passengers' choices of departure airport in multi-airport regions is a crucial component of transportation planning in many large metropolitan areas, and has been the topic of an increasing number of studies over recent years. In this paper, we advance the state of the art of modelling in this area of research by making use of a Cross-Nested Logit (CNL) structure that allows for the joint representation of inter-alternative correlation along the three choice dimensions of airport, airline and access-mode. The analysis uses data collected in the Greater London area, which arguably has the highest levels of inter-airport competition of any multi-airport region; the authors of this paper are not aware of any previous effort to jointly analyse the choice of airport, airline and access-mode in this area. The results of the analysis reveal significant influences on passenger behaviour by access-time, access-cost, flight-frequency and flight-time. A structural comparison of the different models shows that the cross-nested structure offers significant improvements over simple Nested Logit (NL) models, which in turn outperform the Multinomial Logit (MNL) model used as the base model.

[1]  Peter Nijkamp,et al.  Airport Choice in a Multiple Airport Region: an Empirical Analysis of the San Franscisco Bay Area , 1998 .

[2]  H. Williams On the Formation of Travel Demand Models and Economic Evaluation Measures of User Benefit , 1977 .

[3]  Elisabetta Strazzera,et al.  Modeling Elicitation effects in contingent valuation studies: a Monte Carlo Analysis of the bivariate approach , 2005 .

[4]  A. Karlqvist,et al.  Spatial interaction theory and planning models , 1978 .

[5]  Peter Nijkamp,et al.  Access to and Competition Between Airports: A Case Study for the San Francisco Bay Area , 2003 .

[6]  Robert E. Skinner,et al.  AIRPORT CHOICE--AN EMPIRICAL STUDY , 1976 .

[7]  D. McFadden Conditional logit analysis of qualitative choice behavior , 1972 .

[8]  David Pitfield,et al.  Methodology for predicting European short-haul air transport demand from regional airports , 1994 .

[9]  J. Polak,et al.  MIXED LOGIT MODELLING OF AIRPORT CHOICE IN MULTI-AIRPORT REGIONS , 2005 .

[10]  Daniel McFadden,et al.  Modelling the Choice of Residential Location , 1977 .

[11]  Chandra R. Bhat,et al.  A parameterized consideration set model for airport choice: an application to the San Francisco Bay Area , 2004 .

[12]  John W. Polak,et al.  Capturing taste heterogeneity and correlation structure with mixed GEV models , 2005 .

[13]  John W. Polak,et al.  Accounting for Random Taste Heterogeneity in Airport Choice Modeling , 2005 .

[14]  Robert E. Caves,et al.  The Projected Market Share for a New Small Airport in the North of England , 1993 .

[15]  Peter Vovsha,et al.  Application of Cross-Nested Logit Model to Mode Choice in Tel Aviv, Israel, Metropolitan Area , 1997 .

[16]  Peter Nijkamp,et al.  Airport and Airline Choice in a Multiple Airport Region: An Empirical Analysis for the San Francisco Bay Area , 2001 .

[17]  Stephane Hess,et al.  On the Use of Discrete Choice Models for Airport Choice with Applications to the San Francisco Bay Area Airports , 2004 .

[18]  Stephane Hess,et al.  Posterior analysis of random taste coefficients in air travel behaviour modelling , 2007 .

[19]  P. Zarembka Frontiers in econometrics , 1973 .

[20]  Messaoud Benchemam,et al.  PASSENGERS' CHOICE OF AIRPORT: AN APPLICATION OF THE MULTINOMIAL LOGIT MODEL , 1988 .

[21]  Michel Bierlaire,et al.  BIOGEME: a free package for the estimation of discrete choice models , 2003 .

[22]  Stephane Hess,et al.  A MODEL FOR THE JOINT ANALYSIS OF AIRPORT, AIRLINE, AND ACCESS-MODE CHOICE FOR PASSENGERS DEPARTING FROM THE SAN FRANCISCO BAY AREA , 2004 .

[23]  Peter Nijkamp,et al.  Access to airports: A case study for the San Francisco Bay Area , 1998 .