CHAPTER 7 Conclusions
7.2 Directions for future research
According to the proposed models and important findings of this dissertation, we discuss several directions for future research. First, although we have found that the performance of the three-airlines alliance is better than that of the two-airlines alliance, the effectiveness of increasing alliance routes may not justify the cost involved.
Therefore, we suggest that future studies may further evaluate the trade-off between the effectiveness brought by alliances and the cost involved so as to optimize alliances. Next, for realism we have weighted each link of air transportation network by the flying time and the number of passengers on the flight. However, for predicting the transmission of influenza more realistically, we suggest that future studies should combine the air transportation network with other transportation systems.
In the study of WOM influence on the adoption of products, some social heterogeneity has been incorporated into our model. Future studies could further apply other preference heterogeneity existing in the population to their model formulation, in
order to fit the real population. In addition, we suggest that future studies could investigate the influences that the alliances between different industries have on the spread of information across different social groups and industries, and on the designs of effective marketing strategies. Finally, it is necessary to apply real data that are available in the aviation industry to the calibration of parameters of the models, in order to make the results more realistic to the current environment.
References
Albers, S. et al., 2005. Strategic alliances between airlines and airports – theoretical assessment and practical evidence. Journal of Air Transport Management 11, pp.
49-58.
Allen, L. J. S., Burgin, A. M., 2000. Comparison of deterministic and stochastic SIS and SIR models in discrete time. Mathematical Biosciences 163, pp. 1-33.
Alsnih, R., Hensher, D. A., 2003. The mobility and accessibility expectations of seniors in an aging population. Transportation Research Part A 37 (10), pp. 903-916.
Barabasi, A. L., Albert, R., 1999. Emergence of scaling in random networks. Science 286, pp. 509-512.
Bass, F. M., 1969. A new product growth model for consumer durables. Management Science 15, pp. 215-227.
Bell, D. M. et al., 2004. Public health interventions and SARS spread, 2003. Emerging Infectious Diseases 10 (11), pp. 1900-1906.
Bertsimas, D., Stock Patterson, S., 1998. The air traffic flow management problem with enroute capacities. Operations Research 46 (3), pp. 406-422.
Bissessur, A., Alamdari, F., 1998. Factors affecting the operational success of strategic airline alliances. Transportation 25 (4), pp. 331-355.
Brahmbhatt, M., 2005. Avian and human pandemic influenza- economic and social
impacts. http://www.who.int/mediacentre/events/2005/World_Bank_Milan_
Brahmbhattv2.pdf
Brown, J. J., Reingen, P. H., 1987. Social ties and word-of-mouth referral behavior.
Journal of Consumer Research 14 (3), pp. 350-362.
Brueckner, J. K., 2003. The benefits of codesharing and antitrust immunity for international passengers, with an application to the Star alliance. Journal of Air Transportation Management 9 (2), pp. 83-89.
Bureau of Transportation Statistics, 2007. Airline data and statistics.
http://www.bts.gov/oai.
Cameron, I. et al., 2003. Understanding and predicting private motorised urban mobility.
Transportation Research Part D 8 (4), pp. 267-283.
Capua, I., Alexander, D. J., 2002. Avian influenza and human health. Acta Tropica 83, pp. 1-6.
Centers for Disease Control and Prevention, 2003. Use of quarantine to prevent transmission of severe acute respiratory syndrome- Taiwan, 2003. Morbidity and Mortality Weekly Report 52 (29), pp. 680-683.
Chen, F. C. Y., Chen, C., 2003. The effects of strategic alliances and risk pooling on the load factors of international airline operations. Transportation Research Part E 39 (1), pp. 19-34.
Clarke, M., 2004. Survey highlights growth of no-frills. Travel Weekly 1732, pp. 22.
Colizza, V. et al., 2006. The role of the airline transportation network in the prediction and predictability of global epidemics. PNAS 103 (7), pp. 2015-2020.
Colizza, V. et al., 2007. Modeling the worldwide spread of pandemic influenza: baseline case and containment interventions. PLoS Medicine 4 (1), pp. 95-110.
Company Barclaycard, 2005. Travel in business survey. Company Barclaycard, London.
Daley, D. J., Gani, J., 1999. Epidemic modelling: an introduction, Cambridge University Press, New York.
Dennis, N., 2000. Scheduling issues and network strategies for international airline alliances. Journal of Air Transport Management 6 (2), pp. 75-85.
Dijkstra, E. W., 1959. A note on two problems in connection with graphs. Numerische Mathematik 1, pp. 269-271.
Dong, X. et al., 2006. Moving from trip-based to activity-based measures of accessibility.
Transportation Research Part A 40 (2), pp. 163-180.
Donnelly, C. A. et al., 2003. Epidemiological determinants of spread of causal agent of severe acute respiratory syndrome in Hong Kong. The Lancet 361, pp. 1761-1766.
Espino, R. et al., in press. Analyzing the effect of preference heterogeneity on willingness to pay for improving service quality in an airline choice context.
Transportation Research Part E.
Federal Aviation Administration (FAA), 2004. Airport capacity benchmark report.
http://www.faa.gov/about/office_org/headquarters_offices/ato/publications/bench.
Ferguson, N. M. et al., 2005. Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature 437, pp. 209-214.
Field, D., 2005. Collective gains. Airline business 21 (9), pp. 44-46.
Fourie, C., Lubbe, B., 2006. Determinants of selection of full-service airlines and low-cost carriers-a note on business travelers in South Africa. Journal of Air Transport Management 12, pp. 98-102.
Frauenthal, J. C., 1980. Mathematical modeling in epidemiology, Springer-Verlag, New York.
Geertman, S. C. M., Ritsema van Eck, J. R., 1995. GIS and models of accessibility potential: an application in planning. International Journal of Geographical Information Systems 9 (1), 67-80.
Geurs, K. T., van Wee, B., 2004. Accessibility evaluation of land-use and transport strategies: review and research directions. Journal of Transport Geography 12 (2), pp. 127-140.
Goh, K., Uncles, M., 2003. The benefits of airline global alliances: an empirical assessment of the perceptions of business travelers. Transportation Research Part A 37 (6), pp. 479-497.
Guimerà, R., Amaral, L. A. N., 2004. Modeling the world-wide airport network. The European Physical Journal B 38 (2), pp.381-385.
Gutiérrez, J., Gómez, G., 1999. The impact of orbital motorways on intra-metropolitan accessibility: the case of Madrid’s M-40. Journal of Transport Geography 7 (1), pp.
1-15.
Hall, W. D., 1999. Efficient capacity allocation in a collaborative air transportation system. Ph.D. thesis, Massachusetts Institute of Technology, Cambridge, MA.
Hughes, M., 2005. Buzzmarketing: get people to talk about your stuff. Penguin Group Inc., New York 10014, USA.
Huse, C., Evangelho, F., 2007. Investigating business traveler heterogeneity: low-cost vs full-service airline users? Transportation Research Part E 43, pp. 259-268.
Hsu, C. I., Shih, H. H., 2008. Small-world network theory in the study of network
connectivity and efficiency of complementary international airline alliances. Journal of Air Transport Management 14, pp. 123-129.
Iatrou, K., Alamdari, F., 2005. The empirical analysis of the impact of alliances on airline operations. Journal of Air Transportation Management 11, pp. 127-134.
Janić, M., 2005. Modeling the large scale disruptions of an airline network. Journal of Transportation Engineering 131 (4), pp. 249-260.
Jiang, B., Claramunt, C., 2004. Topological analysis of urban street networks.
Environment and Planning B 31 (1), pp.151-162.
Jun, T. et al., 2006. Consumer referral in a small world network. Social Networks 28, pp.232-246.
Katz, E., Lazarsfeld, P. F., 1955. Personal influence. Glencoe, IL: The Free Press.
Kemp, R. et al., 2005. Airline alliance survey 2005. Airline business 21 (9), pp. 49-91.
King, C. C. et al., 2004. Influenza pandemic plan: integrated wild bird/domestic avian/swine/human flu surveillance systems in Taiwan. International Congress Series 1263, pp. 407-412.
Kuperman, M., Abramson, G., 2001. Small world effect in an epidemiological model.
Physical Review Letters 86 (13), pp. 2909-2912.
Latora, V., Marchiori, M., 2001. Efficient behavior of small-world networks. Physical Review Letters 87 (19), pp.198701-1-198701-4.
Latora, V., Marchiori, M., 2002. Is the Boston subway a small-world network? Physica A 314, pp.109-113.
Levine, J., Garb, Y., 2002. Congestion pricing’s conditional prosmise: promotion of accessibility or mobility? Transport Policy 9 (3), pp. 179-188.
Ligon, B. L., 2005. Avian influenza virus H5N1: a review of its history and information regarding its potential to cause the next pandemic. Seminars in Pediatric Infectious Diseases 16 (4), pp. 326-335.
Lin, M. H., 2004. Strategic airline alliances and endogenous Stackelberg equilibria.
Transportation Research Part E 40 (5), pp. 357-384.
Mahajan, V. et al., 1990. New product diffusion models in marketing: a review and
directions for research. Journal of Marketing 54 (1), pp. 1-26.
Mason, K. J., 2000. The propensity of business travelers to use low cost airlines. Journal of Transport Geography 8, pp. 107-119.
Mason, K. J., 2001. Marketing low-cost airline services to business travelers. Journal of Air Transport Management 7, pp. 103-109.
Massad, E. et al., 2005. Forecasting versus projection models in epidemiology: the case of the SARS epidemics. Medical Hypotheses 65 (1), pp. 17-22.
Meijer, A. et al., 2004. Highly pathogenic avian influenza virus A (H7N7) infection of humans and human-to-human transmission during avian influenza outbreak in the Netherlands. International Congress Series 1263, pp. 65-68.
Méndez, V., Fort, J., 2000. Dynamical evolution of discrete epidemic models. Physica A 284, pp. 309-317.
Meyer, M., 1995. Alternative performance measures for transportation planning:
evolution toward multimodal planning. Report No. FTA-GA-26-7000, Washington, D.C.: Federal Transit Administration.
Milgram, S., 1967. The small world problem. Psychology Today 2, pp. 60-67.
Money, R. B. et al., 1998. Explorations of national culture and word-of-mouth referral behavior in the purchase of industrial services in the United States and Japan.
Journal of Marketing 62, pp. 76-87.
Mukherjee, A., Hansen, M., 2007. A dynamic stochastic model for the single airport ground holding problem. Transportation Science 41 (4), pp. 444-456.
O’Connell, J. F., Williams, G., 2005. Passengers’ perceptions of low cost airlines and full service carriers: a case study involving Ryanair, Aer Lingus, Air Asia and Malaysia Airlines. Journal of Air Transport Management 11, pp. 259-272.
Olsen, S. J. et al., 2003. Transmission of the severe acute respiratory syndrome on aircraft. The New England Journal of Medicine 349 (25), pp. 2416-2422.
Oum, T. H. et al., 2004. The effect of horizontal alliances on firm productivity and profitability: evidence from the global airline industry. Journal of Business Research 57 (8), pp. 844-853.
Park, J. H., 1997. The effects of airline alliances on markets and economic welfare.
Transportation Research Part E 33 (3), pp. 181-195.
Park, J. H. et al., 2001. Analytical models of international alliances in the airline industry.
Transportation Research Part B 35 (9), pp. 865-886.
Park, J. H., Zhang, A., 1998. Airline alliances and partner firms’ outputs. Transportation Research Part E 34, pp. 245-255.
Pearmain, D. et al., 1991. Stated preference techniques: a guide to practice, second ed.
Hague Consulting Group.
Richetta, O., Odoni, A. R., 1994. Dynamic solution to the ground-holding problem in air traffic control. Transportation Research Part A 28 (3), pp. 167-185.
Rose, J. M. et al., 2005. Recovering costs through price and service differentiation:
accounting for exogenous information on attribute processing strategies in airline choice. Journal of Air Transport Management 11, pp. 400-407.
Rosen, E., 2000. The anatomy of buzz: how to create word-of-mouth marketing.
Doubleday, New York 10036, USA.
Rossi, F., Smriglio, S., 2001. A set packing model for the ground holding problem in congested networks. European Journal of Operational Research 131, pp. 400-416.
Saramäki, J., Kaski, K., 2005. Modelling development of epidemics with dynamic small-world networks. Journal of Theoretical Biology 234, pp. 413-421.
Schafer, A., Victor, D. G., 2000. The future mobility of the world population.
Transportation Research Part A 34 (3), pp. 171-205.
Sen, P. et al., 2003. Small-world properties of the Indian railway network. Physical Review E 67 (3), pp.036106-1-036106-5.
Siren, A., Hakamies-Blomqvist, L., 2004. Private car as the grand equaliser?
Demographic factors and mobility in Finnish men and women aged 65+.
Transportation Research Part F 7 (2), pp. 107-118.
Small, M., Tse, C. K., 2005. Clustering model for transmission of the SARS virus:
application to epidemic control and risk assessment. Physica A 351, pp. 499-511.
Vossen, T. W. M., Ball, M. O., 2006. Slot trading opportunities in collaborative ground delay programs. Transportation Science 40 (1), pp. 29-43.
Vranas, P. B. et al., 1994. The multi-airport ground-holding problem in air traffic control.
Operations Research 42 (2), pp. 249-261.
Watts, D. J., Strogatz, S. H., 1998. Collective dynamics of ‘small-world’ networks.
Nature 393, pp. 440-442.
Wen, Y. H., Hsu, C. I., 2006. Interactive multiobjective programming in airline network design for international airline code-share alliance. European Journal of Operational Research 174 (1), pp. 404-426.
World Bank, 2005. Interview with Milan Brahmbhatt on avian flu.
http://discuss.worldbank.org/content/interview/detail/2739.
Zanette, D. H., 2001. Critical behavior of propagation on small-world networks. Physical Review E 64, pp. 050901-1-050901-4.
Zanette, D. H., Kuperman, M., 2002. Effects of immunization in small-world epidemics.
Physica A 309, pp. 445-452.
Zhu, X., Liu, S., 2004. Analysis of the impact of the MRT system on accessibility in Singapore using integrated GIS tool. Journal of Transport Geography 12 (2), pp.
89-101.
GLOSSARY OF SYMBOLS
Part I: Small-world network theory in the study of network connectivity and efficiency of complementary international airline alliances
) , ( x x
x N A
G The network of airline ‘x’ before it enters an international alliance, where N and x Ax represent, respectively, the set of nodes and the set of links in graphG x
Nx The total number of nodes in graphG x A x The total number of links in graphG x
J x The set of all origin-destination (OD) pairs served by airline ‘x’
Jx The total number of OD pairs in graphG x S c The set of the allied airlines with airline ‘x’
xy
N c The set of additional nodes for airline ‘x’ after it has signed a complementary alliance agreement with airline ‘y’
xy
A c The set of additional links for airline ‘x’ after it has signed a complementary alliance agreement with airline ‘y’
xy
J c The set of additional OD pairs for airline ‘x’ after it has signed a complementary alliance agreement with airline ‘y’
Nxyc The set of nodes of the airline ‘x’ network in the post-alliance situation Axyc The set of links of the airline ‘x’ network in the post-alliance situation Jxyc The set of OD pairs of the airline ‘x’ network in the post-alliance
situation )
, (N A
Gxy The alliance network of airlines ‘x’ and ‘y’, where N and A are the set of nodes and the set of links, respectively
t ij The travel time between OD pair i-j (i≠ j∈Gxy)
m
tij The shortest travel time between OD pair i-j )
Gx′ The pre-alliance network of airlines ‘x’ and ‘y’
R OD pairs with changed shortest paths after the alliance
k i The number of neighbor-nodes of node i
Gi The subgraph of node i, i∉Gi
m
tpq The shortest travel time between OD pair p-q, where both nodes p and q are the neighbor-nodes of node i
)
P j The attraction of destination node j
α The decay parameter of the shortest travel time
Part II: Transmission and control of an emerging influenza pandemic in a small-world
Ij The number of infected individuals on flight j at time t
t j The time on which flight j departs from its origin airport
o
Ij The initial number of infected individuals on flight j at time t j
j′ One of the upstream flights connectable to flight j at the origin airport of flight j
U j The set of upstream flights of flight j
f
Ij′ The number of infected individuals on flight j′ when the flight arrives at its destination airport
j
αj ′ The proportion of infected individuals on flight j′ who transfer to flight j
) ( j
j t
Z The number of infected individuals who board the airplane from the origin airport of flight j at time t j
N j The total number of passengers of flight j j o The origin airport of flight j
) (t
Yjo The cumulative number of infected individuals in the restricted areas of airport j at time t o
νj The proportion of Yjo(t) who board flight j )
(t
Sj The number of susceptible individuals on flight j at time t
β The infection parameter on flights
F j The total elapsed flying time of flight j
t j The time flight j arrives at its destination airport
f
Ij The final total number of infected individuals in the cabin of flight j at time t j
j d The destination airport of flight j
jd
ρ The average number of contacts that lead to infection per infected individual per unit of time within the restricted areas of the terminals of airport j d
) (t
Xjd The number of infected individuals within the restricted areas of destination airport j at time t d
jˆ One of the downstream flights connectable to flight j
L j The set of the downstream flights of flight j
j
λjˆ The proportion of Ijf who stay and wait for transferring to flight jˆ
Wˆj The waiting time needed for transferring to flight jˆ from flight j
γjˆ An indicator variable, γjˆ =1 if tj ≤t<tj +Wjˆ; γjˆ =0 if t≥tj+Wˆj E m The set of those flights whose destination airport, j , is airport m d
)
m(0
Y The initial number of infected individuals in the restricted areas of airport m
) (t
I The cumulative number of infected individuals in the whole airline network up to time t
N o The initial number of infected individuals in the network at time t=0 φj An indicator variable, φj =1 if tj <t<tj; φj =0 if t≥ tj
) (t
H The cumulative percentage of airports with infected cases occurring at time t
V The total number of airports in the network
δm An indicator variable, δm =1 if Ym(t)>0; otherwise δm =0
Part III: Word-of-mouth marketing in a small-world network: the case of low-cost carriers
p The probability of rewiring links, called network randomness
P x The probability that a potential passenger adopts LCC based on the information acquired from mass media communication
VF The observed utility of FSC VL The observed utility of LCC
s q The strength of the social relationship of neighbor q to a given potential passenger i
) (t
aq An indicator, 1aq(t)= if neighbor q has adopted LCC at time t;
otherwise, aq(t)=0
r q The probability that neighbor q gives WOM information to potential passenger i
) (t
Wi The intensity of WOM information received by potential passenger i at time t
) (t
PiL The probability that potential passenger i adopts LCC at time t according to mass media and WOM communications
)
i(t
θ The adjustment factor of potential passenger i used to alter the probability of adoption at time t
Ω A threshold of the intensity of WOM
Part IV: Application of small-world theory to airport delay problem )
M i The planned arrival capacity of airport i k i The number of links connected to node i Θ The set of capacity profile scenarios
{ }
qP The probability of occurrence of a scenario q∈Θ )
~ ( t
Miq The practical arrival capacity of a specific scenario q of airport i at time t
)
ij(t
ς The state of a link connecting origin-destination (OD) pair i-j at time t )
i(t
ς The state of node i at time t
D j The expected duration that severe weather at node j lasts τij The scheduled departure time of a link connecting OD pair i-j
F ij The elapsed flying time from node i to node j for a link connecting OD
pair i-j
D ij The delay duration incurred by the link connecting OD pair i-j )
(Dij
p The probability that the link connecting OD pair i-j incurs a delay with the duration of D time periods ij
A The matching level between the characteristics of link ij and the implemented allocation strategy at node j
) (t
nj The number of links that are scheduled to arrive at node j at time t
G ij The maximum number of time periods that link ij may be held on the ground
βij The capacity cut caused by the ground hold of link ij Z The total cost of a given allocation strategy
E d The set of delayed links E c The set of cancelled links
d
cij The delay cost of link ij per time period
c
cij The cancellation cost of link ij
NR The total number of links that have been delayed or cancelled at any past time period
VITA
Name: Hsien-Hung Shih
施憲宏
Birthplace: Pingtung City, TAIWAN
台灣屏東市 Date of birth: June 1, 1980
民國69 年 6 月 1 日
Education: Ph.D. in Transportation Technology and Management, National Chiao Tung University (2003.09 - 2008.06)
國立交通大學運輸科技與管理學系 博士
B.S., Department of Transportation Technology and Management, National Chiao Tung University (1998.09 - 2002.06)
國立交通大學運輸科技與管理學系 學士
Address: No. 66, Ansin 4th Side Lane, Rueiguang Village, Pingtung City, Pingtung County 900, TAIWAN
屏東縣屏東市瑞光里安心四橫巷66 號
Email: u9132523.tem91g@nctu.edu.tw