Measuring the potential for self-connectivity in global air transport markets: implications for airports and airlines

One of the strategies that air travellers employ to save money is self-connectivity, i.e. travelling with a combination of tickets where the airline/s involved do not handle the transfer themselves. Both airports and airlines, particularly low-cost carriers, have recently started catering to the needs of this type of passengers with the introduction of transfer fees or the development of self-connection platforms. The evidence provided by the existing literature, however, suggests that the degree of implementation of these strategies falls short of its true potential. In order to investigate how much self-connectivity could be observed in global air transport markets, this paper develops a forecasting model based on a zero-inflated Poisson regression on MIDT data. We identify the airports that have the highest potential to facilitate self-connections, as well as the factors that hinder or facilitate the necessary airline agreements at major locations. The results from this paper have many implications in regards to the widespread implementation of self-connection services and the future of the air travel industry.

[1]  Renato Redondi,et al.  The competitive landscape of air transport in Europe , 2016 .

[2]  Nigel Halpern,et al.  Airport route development: a survey of current practice. , 2015 .

[3]  Guillaume Burghouwt,et al.  Airline Network Development in Europe and its Implications for Airport Planning , 2007 .

[4]  David Gillen,et al.  The Dilemma of Slot Concentration at Network Hubs , 2006 .

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

[6]  Richard Klophaus,et al.  Low cost carriers going hybrid: Evidence from Europe , 2012 .

[7]  John F. O'Connell,et al.  The economic viability of long-haul low cost operations: Evidence from the transatlantic market , 2015 .

[8]  Q. Vuong Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses , 1989 .

[9]  Elsevier Sdol Transportation Research Part D: Transport and Environment , 2009 .

[10]  J. G. de Wit,et al.  The growth limits of the low cost carrier model , 2012 .

[11]  Pere Suau-Sanchez,et al.  A model to analyse the profitability of long-haul network development involving non-hub airports: The case of the Barcelona–Asian market , 2016 .

[12]  Jan Veldhuis,et al.  The competitive position of airline networks , 1997 .

[13]  B. Biggerstaff,et al.  On the Treatment of Airline Travelers in Mathematical Models , 2011, PloS one.

[14]  W. Greene,et al.  Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models , 1994 .

[15]  Tomohiko Kawamori,et al.  Airline Alliances with Low Cost Carriers , 2011 .

[16]  P. Malighetti,et al.  Hub competition and travel times in the world-wide airport network , 2011 .

[17]  Franz Rothlauf,et al.  Gravity models for airline passenger volume estimation , 2007 .

[18]  Peter Morrell,et al.  Airlines within airlines: An analysis of US network airline responses to Low Cost Carriers , 2005 .

[19]  J. Claro,et al.  The airport business in a competitive environment , 2013, European Journal of Transport and Infrastructure Research.

[20]  Stephane Hess,et al.  Understanding air travellers' trade-offs between connecting flights and surface access characteristics , 2014 .

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

[22]  Andrew J. Tatem,et al.  Modeling monthly flows of global air travel passengers: An open-access data resource , 2015, Journal of Transport Geography.

[23]  Franz Rothlauf,et al.  An airline connection builder using maximum connection lag with greedy parameter selection , 2014 .

[24]  Anthony Owen Lee,et al.  Airline reservations forecasting--probabilistic and statistical models of the booking process , 1990 .

[25]  Frank S. Koppelman,et al.  Modeling the competition among air-travel itinerary shares: GEV model development , 2005 .

[26]  Augusto Voltes-Dorta,et al.  Regulatory Airport Classification in the US: The Role of International Markets , 2015 .

[27]  Frank S. Koppelman,et al.  Modeling aggregate air-travel itinerary shares: logit model development at a major US airline , 2003 .

[28]  Augusto Voltes-Dorta,et al.  The role of London airports in providing connectivity for the UK: regional dependence on foreign hubs , 2016 .

[29]  Robert J. Windle,et al.  AIRPORT CHOICE IN MULTIPLE-AIRPORT REGIONS , 1995 .

[30]  Guillaume Burghouwt,et al.  Connectivity levels and the competitive position of Spanish airports and Iberia's network rationalization strategy, 2001-2007 , 2012 .

[31]  Frank Fichert,et al.  Self-connecting, codesharing and hubbing among European LCCs: From point-to-point to connections? , 2016 .

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

[33]  Tobias Grosche,et al.  Hubs at risk: Exposure of Europe's largest hubs to competition on transfer city Pairs , 2015 .

[34]  P. Malighetti,et al.  Codesharing agreements by low-cost carriers: An explorative analysis , 2015 .

[35]  Mark Hansen,et al.  An aggregate demand model for air passenger traffic in the hub-and-spoke network , 2006 .

[36]  Sven Maertens,et al.  The scope for low-cost connecting services in Europe — Is self-hubbing only the beginning? , 2016 .

[37]  Guillaume Burghouwt,et al.  Temporal Configurations of European Airline Networks , 2005 .

[38]  Xavier Fageda,et al.  The evolving low-cost business model: Network implications of fare bundling and connecting flights in Europe , 2015 .

[39]  Robin L. Dillon,et al.  Airline Safety Improvement Through Experience with Near‐Misses: A Cautionary Tale , 2016, Risk analysis : an official publication of the Society for Risk Analysis.

[40]  Renato Redondi,et al.  Connectivity of the European airport network: “Self-help hubbing” and business implications , 2008 .

[41]  F. Dobruszkes The geography of European low-cost airline networks: a contemporary analysis , 2013 .

[42]  Guoqiang Peter Zhang,et al.  Neural network forecasting for seasonal and trend time series , 2005, Eur. J. Oper. Res..