Abstract
The aim of this study is to define an appropriate approach to forecast the appearance and disappearance of air passenger demand between cities worldwide. For the air passenger demand link forecasting, a weighted similarity-based algorithm is used, with an analysis of nine indices. The weighted resource allocation index demonstrates the best metrics. The accuracy of this method has been determined through a comparison of modeled and known data from three separate years. The known data was used to establish boundaries when applying the similarity-based algorithm. As a result, it is found that a weighted resource allocation index, with defined boundaries, should be utilized for link prediction in the air passenger demand network. Furthermore, it is shown that grouping cities within the air passenger demand network, based on socio-economic indicators, increases the accuracy of the forecast.
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References
Terekhov, I., Ghosh, R., Gollnick, V.: A concept of forecasting origin-destination air passenger demand between global city pairs using future socio-economic development scenarios. In: 53rd AIAA Aerospace Sciences Meeting, Kissimmee, Florida, USA (2015)
Ghosh, R., Terekhov, I.: Future Passenger Air Traffic Modelling: Trend Analysis of the Global Passenger Air Travel Demand Network. In: 53rd AIAA Aerospace Science Meeting, Kissimmee, Florida (2015)
Boeing, Current Market Outlook 2013–2032, USA (2013). http://www.boeing.com/assets/pdf/commercial/cmo/pdf/Boeing_Current_Market_Outlook_2013.pdf [cited 19.11.2014]
Dray, L., Evans, A.D., Reynolds, T., Schäfer, A.: Mitigation of aviation emissions of carbon dioxide: analysis for Europe. Transp. Res. Record 2177, 17–26 (2010)
Lü, L., Zhou, T.: Link prediction in complex networks: a survey. Physica A 390, 1150–1170 (2011)
Murata, T., Moriyasu, S.: Link prediction of social networks based on weighted proximity measures. In: IEEE/WIC/ACM International conference on Web Intelligence, Fremont, California (2007)
Lü, L., Zhou, T.: Link prediction in weighted networks: the role of weak ties. EPL Lett. J. Explor. Front. Phys. 89(2012), 18001 (2010)
Zheleva, E., Golbeck, J., Kuter, U.: Using Friendship Ties and Family Circles for Link Prediction. Advances in Social Network Mining and Analysis. Lecture Notes in Computer Science, vol. 5498, pp. 97–113 (2012)
Hanely, J.A., McNeil, B.J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29–39 (1982)
Herlocker, J.L., Konstann, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)
Sabre Airline Solutions, Aviation Data Intelligence (ADI). http://www.sabreairlinesolutions.com/home/software_solutions/airports/ [cited 19.11.2014]
UN, National Accounts Main Aggregates Database. http://unstats.un.org/unsd/snaama/dnllist.asp [cited 19.11.2014]
The World Bank, World Bank Open Data. http://data.worldbank.org/indicator/NY.GDP.MKTP.CD. [cited 19.11.2014]
UN, World population Prospects: The 2012 Revision. http://esa.un.org/unpd/wpp/Excel-Data/population.htm [cited 19.11.2014]
MaxMind, Free World Cities Database. https://www.maxmind.com/en/worldcities. [cited 19.11.2014]
Our Airports. http://ourairports.com/data/. [cited 19.11.2014]
OpenFlights, Airport database. http://openflights.org/data.html. [cited 19.11.2014]
Terekhov, I., Gollnick, V.: Clustering of airport cities and cluster dynamic for the air passenger demand forecasting model based on a socio-economic scenario. In: CEAS conference, Delft, Netherlands (2015)
Grosche, T., Rothlauf, F., Heinzl, A.: Gravity models for airline passenger volume estimation. J. Air Transp. Manag. 13, 175–183 (2007)
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Terekhov, I., Evans, A., Gollnick, V. (2016). Forecasting a Global Air Passenger Demand Network Using Weighted Similarity-Based Algorithms. In: Cherifi, H., Gonçalves, B., Menezes, R., Sinatra, R. (eds) Complex Networks VII. Studies in Computational Intelligence, vol 644. Springer, Cham. https://doi.org/10.1007/978-3-319-30569-1_26
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DOI: https://doi.org/10.1007/978-3-319-30569-1_26
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