Managing reliever gateway airports with high-speed rail network

Abstract On the basis of model analysis, this study proposes an effective method for managing reliever gateway airports when the main gateway airport is completely dysfunctional owing to a catastrophe. In particular, we deal with the case where the main and reliever airports are connected by high-speed rail (HSR), and we discuss a desirable and effective support policy for regaining passenger flow from/to the affected area. First, we analyze the market behavior under the usual condition as the base case by adopting the bi-level model, which is a supply-demand interaction model. Second, under the supposition of a catastrophe, we set up some scenarios of management policies, i.e. (i) no special policy and (ii) providing support to HSR passengers to induce them to the reliever gateways. Through such scenario analyses, we show that (i) HSR fare restriction is required to regain sufficient passenger flow and (ii) providing fare support to HSR passengers is an effective way to regain passenger flow using reliever gateways, which can contribute toward building a robust air transport network.

[1]  Elise Miller-Hooks,et al.  Assessing the role of network topology in transportation network resilience , 2015 .

[2]  Frédéric Dobruszkes,et al.  High-speed rail and air transport competition in Western Europe: A supply-oriented perspective , 2011 .

[3]  Augusto Voltes-Dorta,et al.  Vulnerability of the European air transport network to major airport closures from the perspective of passenger delays: Ranking the most critical airports , 2017 .

[4]  P. Harker Generalized Nash games and quasi-variational inequalities , 1991 .

[5]  Mikio Takebayashi Network competition and the difference in operating cost: Model analysis , 2013 .

[6]  Mikio Takebayashi,et al.  How could the collaboration between airport and high speed rail affect the market , 2016 .

[7]  Yung-Hsiang Cheng High-speed rail in Taiwan: New experience and issues for future development , 2010 .

[8]  Anming Zhang,et al.  Air and high-speed rail transport integration on profits and welfare: Effects of air-rail connecting time , 2017 .

[9]  William H. K. Lam,et al.  THE GENERALIZED NASH EQUILIBRIUM MODEL FOR OLIGOPOLISTIC TRANSIT MARKET WITH ELASTIC DEMAND , 2005 .

[10]  Changmin Jiang,et al.  Effects of high-speed rail and airline cooperation under hub airport capacity constraint , 2014 .

[11]  Anming Zhang,et al.  High-speed rail and air transport competition and cooperation: A vertical differentiation approach , 2016 .

[12]  David Banister,et al.  Airline and railway integration , 2006 .

[13]  Peter Nijkamp,et al.  Airport and Airline Competition for Passengers Departing from a Large Metropolitan Area , 2000 .

[14]  Jia Yan,et al.  An Analysis of Travel Demand in Japan's Intercity Market Empirical Estimation and Policy Simulation , 2014 .

[15]  Mikio Takebayashi,et al.  Low-cost carriers versus full service carriers in ASEAN: The impact of liberalization policy on competition , 2014 .

[16]  Jonathan F. Bard,et al.  Reallocating arrival slots during a ground delay program , 2008 .

[17]  M. Pilar Socorro,et al.  The effects of airline and high speed train integration , 2013 .

[18]  Christian Hofer,et al.  Price premiums and low cost carrier competition , 2008 .

[19]  Mikio Takebayashi,et al.  Multiple hub network and high-speed railway: Connectivity, gateway, and airport leakage , 2015 .

[20]  H. Murakami,et al.  Dynamic effect of inter-firm rivalry on airfares: Case of Japan's full-service and new air carriers , 2015 .

[21]  Anming Zhang,et al.  Will China's airline industry survive the entry of high-speed rail? , 2012 .

[22]  Milan Janic,et al.  Modelling the resilience, friability and costs of an air transport network affected by a large-scale disruptive event , 2015 .

[24]  Managing airport charges under the multiple hub network with high speed rail: Considering capacity and gateway function , 2018, Transportation Research Part A: Policy and Practice.

[25]  L. R. Christensen,et al.  Economies of Density versus Economies of Scale: Why Trunk and Local Service Airline Costs Differ , 1984 .

[26]  Martin Dresner,et al.  COMPETITIVE RESPONSES TO LOW COST CARRIER ENTRY , 1999 .

[27]  Cynthia Barnhart,et al.  Applications of Operations Research in the Air Transport Industry , 2003, Transp. Sci..

[28]  Jonathan F. Bard,et al.  Models and Methods for Managing Airline Irregular Operations , 1998 .

[29]  Henry Y. K. Lau,et al.  A math-heuristic algorithm for the integrated air service recovery , 2016 .

[30]  Achim I. Czerny,et al.  Airports and airlines economics and policy: An interpretive review of recent research , 2012 .

[31]  B. Mantin,et al.  Transportation Infrastructure Management: One- and Two-sided Market Approaches , 2013 .