An Optimization Approach towards Air Traffic Forecasting: A Case Study of Air Traffic in Changi Airport
AbstractSouth-East Asia is considered one of the fastest air traffic growing regions in the world. Congested air traffic and weather conditions have thus become major factors in air traffic management. In this paper the model for air traffic forecasting was able to forecast the country, city-pair and airport-pair air traffic. Results show that the passenger forecasting for Singapore has dependence not only on that country but on neighbouring countries as well. The paper predicts that the passenger movements in Changi airport will increase up to 81.65 million people by year 2023, which is 31.23 % more than that in 2017. Also, the number of passenger aircraft between Singapore and Jakarta city-pair will increase up to 34702 by year 2023, which is 26.6 % more than that in 2017. In addition, the number of passenger aircraft between Changi airport and KLIA airport-pair will be between 31698 and 40311 by year 2023.
Manualonairtrafficforecasting(mediumandlong-termforecasting). Doc.-InternationalCivilAviationOrganization:8991-AT/722.[Montreal]: International Civil Aviation Organization, 1972.
Zeliha Aka. Comparative analysis with a new hub connectivity measure considering revenue and passenger demand. Journal of Air Transport Management, 67:34 – 45, 2018.
W. L. Ashby. Future demand for air traffic services. Proceedings of the IEEE, 58(3):292–299, March 1970.
Richard T. Carson, Tolga Cenesizoglu, and Roger Parker. Forecasting (aggregate) demand for us commercial air travel. International Journal of Forecasting, 27(3):923 – 941, 2011. Special Section 1: Forecasting with Artificial Neural Networks and Computational Intelligence Special Section 2: Tourism Forecasting.
J. Chen, L. Chen, and D. Sun. Air traffic flow management under uncertainty using chance-constrained optimization. Transportation
Research Part B: Methodological, 102:124 – 141, 2017.
Valerie Chew. Severe acute respiratory syndrome (sars) outbreak, 2003, 2004. Last accessed 21 June 2018.
Giulio Di Gravio, Maurizio Mancini, Riccardo Patriarca, and Francesco Costantino. Overall safety performance of air traffic management system: Forecasting and monitoring. Safety Science, 72:351 – 362, 2015.
Trading Economics. Trading economics, 2016. Last accessed 30 May 2018.
XIAOWEN FU, TAE HOON OUM, and ANMING ZHANG. Air transport liberalization and its impacts on airline competition and air passenger traffic. Transportation Journal, 49(4):24–41, 2010.
International Monetary Fund. Gdp. Last accessed 4 June 2016.
T. Hazledine. An augmented gravity model for forecasting passenger air traffic on city-pair routes. Journal of Transport Economics and Policy, 51(3):208–224, 2017.
Hashem Salarzadeh Jenatabadi and Noor Azina Ismail. Application of structural equation modelling for estimating airline performance. Journal of Air Transport Management, 40:25 – 33, 2014.
The Smart Local. The escape of mas selamat (27 feburary 2008). Last accessed 28 July 2018.
Ana M. Lopez, Mario A. Flores, and Juan I. Sanchez. Modelos de series temporales aplicados a la prediccion del trafico aeroportuario
espanol de pasajeros: Un enfoque agregado y desagregado (forecasting of spanish passenger air traffic based on time series models with aggregated and disaggregated approaches. with english summary.). Estudios de Economia Aplicada, 35(2):395 – 418, 2017.
Larry Meyn. Probabilistic methods for air traffic demand forecasting. AIAA Guidance, Navigation, and Control Conference and
Exhibit, 5:188 – 199, 2002.
V.A. Profillidis. Econometric and fuzzy models for the forecast of demand in the airport of rhodes. Journal of Air Transport Management, 6(2):95–100, 2000.
Athina Sismanidou and Joan Tarradellas. Traffic demand forecasting and flexible planning in airport capacity expansions: Lessons from the madrid-barajas new terminal area master plan. Case Studies on Transport Policy, 5:188 – 199, 2017.
Z.W. Zhong Tolebi Sailauov. Air traffic forecasting using optimization for econometric models. International Conference on
Engineering & Technology, Computer, basic & Applied Sciences,2016.
Z.W. Zhong Tolebi Sailauov. Air traffic forecasting using optimization for econometric models. International Journal of Technology and Engineering Studies, 3 (5):197–203, 2017.
Suryan Viktor. Econometric forecasting models for air traffic passenger of indonesia. Journal of the Civil Engineering Forum, Vol 3,Iss 1, Pp 33-44 (2017), (1):33, 2017.
Bogdana B. Vuji, Vukmirovi Srdan M., Goran V. Vuji, Jovii Neboja M., Jovii Gordana R., and Milun J. Babi. Experimental and artificial neural network approach for forecasting of traffic air pollution in urban areas: The case of subotica. Thermal Science,14:S79, 2010.
Zia Wadud. Simultaneous modeling of passenger and cargo demand at an airport. Transportation Research Record, 2336(1):63–74,2013.
S. Wenzel, Latjens K. Klker, K., and P. Bielich. Approach to forecast air-traffic movements at capacity-constrained airports. Journal of Aircraft, 52(5):1710–1714, 2015.
Wikipedia. Singapore changi airport, 2015. Last accessed 25 August 2016.
Bao Yukun, Xiong Tao, and Hu Zhongyi. Forecasting air passenger traffic by support vector machines with ensemble empirical mode decomposition and slope-based method. Discrete Dynamics in Nature and Society, Vol 2012 (2012), 2012.
Hong-hai Zhang, Cheng-peng Jiang, and Lei Yang. Forecasting traffic congestion status in terminal areas based on support vector machine. Advances in Mechanical Engineering, 8(9), 09 2016. Copyright - The Author(s) 2016; Last updated - 2017-01-07.
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).