Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2022, Cilt: 6 Sayı: 1, 87 - 92, 23.03.2022
https://doi.org/10.30518/jav.1062151

Öz

Kaynakça

  • Adeniran, A.O., Kanyio, O.A. and Owoeye. A.S. (2018). Forecasting methods for domestic air passenger demand in Nigeria. Journal of Applied Research on Industrial Engineering, 5(2), 146– 155.
  • Adewuyi, P.A (2013). Performance evaluation of Mamdani-type and Sugeno-type fuzzy inference system-based controllers for computer fan. International Journal of Information Technology and Computer Science, 1, 26-36.
  • Alhumade, H. & Rezk, H. (2022). An accurate model of the corrosion current density of coatings using an adaptive network-based fuzzy inference system. Metals, 12(3), 392.
  • Andreoni, A. and Postorino, M.N. (2006). A multivariate ARIMA model to forecast air transport demand. Retrieved from: https://citeseerx.ist.psu.edu/viewdoc/download?
  • Bagheri, A., Peyhani, H.M. and Akbari, M. (2014). Financial forecasting using ANFIS networks with quantum-behaved particle swarm optimization. Expert Systems with Applications, 41(14), 6235-6250.
  • Chaudhari, S. and Patil, M. (2014). Study and review of fuzzy inference systems for decision making and control. American International Journal of Research in Science, Technology, Engineering & Mathematics, 5(1), 88-92.
  • Chi, J. and Baek, J. (2013). Dynamic relationship between air transport demand and economic growth in the United States: A new look. Transport Policy, 29, 257-260.
  • Chippa, A.A., Kumar, V., Joshi, R.R., Chakrabarti, P., Jasinski, M., Burgio, A., Leonowicz, Z., Jasinska, E., Soni, R. and Jasinski, T. (2021). Adaptive neuro-fuzzy inference system-based maximum power tracking controller for variable speed WECS. Energies, 14(19), 6275.
  • Cho, H.C., Choi, S.H., Han, S.J., Lee, S.H., Kim, H.Y. and Kim, K.S. (2020). Effective compressive strengths of corner and edge concrete columns based on adaptive neuro-fuzzy inference system. Applied Sciences, 10(10), 3475.
  • Cook, G.N. & Billig, B.G. (2017). Airline operations and management: A management textbook. Abingdon: Routledge.
  • Cooper, M. (2005). Japanese tourism and the SARs epidemic of 2003. Journal of Travel & Tourism Marketing, 19(2-3), 117-131.
  • Dempsey, P.S. & O’Connor, K. (1997). Air traffic congestion and infrastructure development in the Pacific Asia region. In C. Findlay, C.L. Sien and K. Singh (Eds.), Asia Pacific air transport: Challenges and policy reforms (pp. 23-47). Singapore: Institute of Southeast Asian Studies.
  • Dileep, M.R. and Kurien, A. (2022). Air transport and tourism: Interrelationship, operations and strategies. Abingdon: Routledge.
  • Efendigil, T., Önüt, S. and Kahraman, C. (2009). A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis. Expert Systems with Applications, 36(3), 6697–6707.
  • Hamed, H.A., Sadkhan, S.B. and Hameed, A.Q. (2018). Proposed adaptive neuro fuzzy inference system (ANFIS) identifier for M-ary frequency shift keying (FSK) signals with low SNR. In I.M.M. El Emary and A. Brzozowska (Eds.), Shaping the future of ICT; Trends in information technology, communications engineering, and management (pp. 259-268). Boca Raton, FL: CRC Press.
  • Holloway, S. (2016). Straight and level: Practical airline economics (3rd ed.). Abingdon: Routledge.
  • Karlaftis. M.G. (2010). Critical review and analysis of air-travel demand: Forecasting models. In L. Weigang, A. de
  • Barros and I. Romani de Oliveria (Eds.), Computational models, software engineering, and advanced technologies in air transportation: Next generation applications (pp. 72-87). Hershey, PA: IGI Global, 2010.
  • Karlaftis, M.G., Zografos, K.G., Papastavrou, J.D. and Charnes, J.M. (1996). Methodological framework for air-travel demand forecasting. Journal of Transportation Engineering, 122(2), 96-104.
  • Khosravanian, R., Sabah, M., Wood, D.A. and Shahryari, A. (2016). Weight on drill bit prediction models: Sugeno-type and Mamdani-type fuzzy inference systems compared. Journal of Natural Gas Science and Engineering, Part A, 280-297.
  • Kim, S. and Shin, D.H. (2016). Forecasting short-term air passenger demand using big data from search engine queries. Automation in Construction, 70, 98-108.
  • Mardani, A., Streimikiene, D., Nilashi, M., Arias Aranda, D., Loganathan, N. and Jusoh, A. (2018). Energy consumption, economic growth, and CO2 emissions in G20 Countries: Application of adaptive neuro-fuzzy inference system. Energies, 11(10), 2771.
  • Morikawa, Y., Tabata, T. & Emura, T. (2007). Ground improvements for the second phase construction of Kansai International Airport. In Y. Kikuchi, M. Otani, J. Kimura and Y. Morikawa (Eds.), Advances in deep foundations (pp. 389-402). Leiden: Taylor & Francis/Balkema.
  • Narang, S.K., Kumar, S. and Verma, V. (2017). Knowledge discovery from massive data streams. In A. Singh, N.
  • Dey, A.S. Ashour and V. Santhi (Eds.), Web semantics for textual and visual information retrieval (pp. 109-143). Hershey, PA: IGI Global: Hershey.
  • Ohta, K. (1999). International airports: Financing methods in Japan. Journal of Air Transport Management, 5(4), 223-234.
  • Papageorgiou, K., Papageorgiou, E.I., Poczeta, K., Bochtis, D. and Stamoulis, G. (2020). Forecasting of day-ahead natural gas consumption demand in Greece using adaptive neuro-fuzzy inference system. Energies, 13(9), 2317.
  • Patil, S.G., Mandal, S., Hegde, A.V. and Alavandar. S. (2011). Neuro-fuzzy based approach for wave transmission prediction of horizontally interlaced multilayer moored floating pipe breakwater. Ocean Engineering, 38(1), 186-196.
  • Pearce, B. (2012). The state of air transport markets and the airline industry after the great recession. Journal of Air Transport Management, 21, 3-9.
  • Piccioni, C., Stolfa, A. and Musso, A. (2022). Exogenous shocks on the air transport business: The effects of a global emergency. In R. Macário & E. Van de Voorde (Eds.), The air transport industry: Economic conflict and competition (pp. 99-124). Amsterdam: Elsevier.
  • Pigatto, A.V. and Balbinot, A. (2018). An automatic cycling performance measurement system based on ANFIS.
  • In W. Pedrycz and S.M. Chen (Eds.), Computational intelligence for pattern recognition (pp. 227-252). Cham: Springer International Publishing.
  • Raihana, K.K., Anjum, F., Saleh Mohamed Shoiab, A., Abdullah Ibne Hossain, M., Alimuzzman, M. and Rahman, R.M. (2017). In R. Silhavy, R. Senkerik, Z.K. Oplatkova., Z. Prokopova and P. Silhavy (Eds.), Artificial intelligence trends in intelligent systems: Proceedings of the Sixth Computer Science On-Line Conference 2017 (CSOC 2017), Volume 1 (pp. 322-332). Cham: Springer International Publishing.
  • Ravi, V., Kumar, P.R., Srinivas, E.R, and Kasabov, N.K. (2008). A semi-online training algorithm for the radial basis function neural networks: Applications to bankruptcy prediction in banks. In V. Ravi (Ed.), Advances in banking technology and management: Impacts of ICT and CRM (pp. 243-260). Hershey, PA: Information Science Reference.
  • Savkovic, B., Kovac, P., Dudic, B., Rodic, D., Taric, M. and Gregus, M. (2019). Application of an adaptive “neuro-fuzzy” inference system in modelling cutting temperature during hard turning. Applied Sciences, 9(18), 3739.
  • Sohag, M.S. and Rokonuzzaman. M. (2016). Demand forecasting for a domestic airport-A case study. In Proceedings of the 3rd International Conference on Civil Engineering for Sustainable Development (ICCESD 2016), 12~14 February 2016, KUET, Khulna, Bangladesh (pp. 1255-1264).
  • Srisaeng, P. and Baxter, G. (2021). Estimation of Australia’s outbound airline passenger demand using an adaptive neuro-fuzzy inference system. International Journal for Traffic and Transport Engineering, 11(3), 475 – 487.
  • Srisaeng, P., Baxter, G.S. and Wild, G. (2015). An adaptive neuro-fuzzy inference system for forecasting Australia’s domestic low-cost carrier passenger demand. Aviation, 19(3), 150-163, 2015.
  • Takagi, T. and Sugeno, M. (1985). Fuzzy identification of systems and its application to modelling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15(1), 116 – 132.
  • Tiryaki, S. and Aydın, A. (2014). An artificial neural network model for predicting compression strength of heat-treated woods and comparison with a multiple linear regression model. Construction and Building Materials, 62, 102-108.
  • Tsui, W.H.K. and Balli, F. (2015). International arrivals forecasting for Australian airports and the impact of tourism marketing expenditure. Tourism Economics, 23(2), 403-428.
  • Übeyli, E. D., Cvetkovic, D., Holland, G. and Cosic, I. (2010). Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of alterations in sleep EEG activity during hypopnoea episodes. Digital Signal Processing, 20(3), 678-691.
  • Wadud, Z. (2014). The asymmetric effects of income and fuel price on air transport demand. Transportation Research Part A: Policy and Practice, 65, 92-102.
  • Washington, S.P., Karlaftis, M.G. and Mannering, F. (2011). Statistical and econometric methods for transportation data analysis (2nd ed.). Boca Raton, FL: Chapman & Hall/CRC Press.
  • Wong, W.H., Cheung, T., Zhang, A. and Wang, Y. (2019). Is spatial dispersal the dominant trend in air transport development? A global analysis for 2006–2015. Journal of Air Transport Management, 74, 1-12.
  • Xiao, Y., Liu, J.J., Hu, Y., Wang, Y., Lai, K.K. and Wang, S. (2014). A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting. Journal of Air Transport Management, 39,1-11.
  • Yetilmezsoy, K., Fingas, M. and Fieldhouse, B. (2011). An adaptive neuro-fuzzy approach for modeling of water-in-oil emulsion formation. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 389, 1–3: 50–62.
  • Zheng, Y., Lei, K.K. & Wang, S. (2018). Forecasting air travel demand: Looking at China. Abingdon: Routledge. Zounemat-Kermani, M. and Scholz, M. (2013). Computing air demand using the Takagi-Sugeno Model for dam outlets. Water, 5(3), 1441-1456.

Application of an ANFIS to Estimate Kansai International Airport’s International Air Passenger Demand

Yıl 2022, Cilt: 6 Sayı: 1, 87 - 92, 23.03.2022
https://doi.org/10.30518/jav.1062151

Öz

This study presents an Adaptive Network Based Inference System (ANFIS) model to forecast international passenger demand at Osaka’s Kansai International Airport. The study covered the period 1994 to 2018. The study used nine determinants of air travel demand and three dummy variables as input variables. The results reveal that the model successfully forecasts Kansai International Airport’s international passenger demand. The coefficient of determination (R2) was high, being around 0.9776%. The overall MAPE of Kansai International Airport’s international air passenger demand model was 7.40%.



Kaynakça

  • Adeniran, A.O., Kanyio, O.A. and Owoeye. A.S. (2018). Forecasting methods for domestic air passenger demand in Nigeria. Journal of Applied Research on Industrial Engineering, 5(2), 146– 155.
  • Adewuyi, P.A (2013). Performance evaluation of Mamdani-type and Sugeno-type fuzzy inference system-based controllers for computer fan. International Journal of Information Technology and Computer Science, 1, 26-36.
  • Alhumade, H. & Rezk, H. (2022). An accurate model of the corrosion current density of coatings using an adaptive network-based fuzzy inference system. Metals, 12(3), 392.
  • Andreoni, A. and Postorino, M.N. (2006). A multivariate ARIMA model to forecast air transport demand. Retrieved from: https://citeseerx.ist.psu.edu/viewdoc/download?
  • Bagheri, A., Peyhani, H.M. and Akbari, M. (2014). Financial forecasting using ANFIS networks with quantum-behaved particle swarm optimization. Expert Systems with Applications, 41(14), 6235-6250.
  • Chaudhari, S. and Patil, M. (2014). Study and review of fuzzy inference systems for decision making and control. American International Journal of Research in Science, Technology, Engineering & Mathematics, 5(1), 88-92.
  • Chi, J. and Baek, J. (2013). Dynamic relationship between air transport demand and economic growth in the United States: A new look. Transport Policy, 29, 257-260.
  • Chippa, A.A., Kumar, V., Joshi, R.R., Chakrabarti, P., Jasinski, M., Burgio, A., Leonowicz, Z., Jasinska, E., Soni, R. and Jasinski, T. (2021). Adaptive neuro-fuzzy inference system-based maximum power tracking controller for variable speed WECS. Energies, 14(19), 6275.
  • Cho, H.C., Choi, S.H., Han, S.J., Lee, S.H., Kim, H.Y. and Kim, K.S. (2020). Effective compressive strengths of corner and edge concrete columns based on adaptive neuro-fuzzy inference system. Applied Sciences, 10(10), 3475.
  • Cook, G.N. & Billig, B.G. (2017). Airline operations and management: A management textbook. Abingdon: Routledge.
  • Cooper, M. (2005). Japanese tourism and the SARs epidemic of 2003. Journal of Travel & Tourism Marketing, 19(2-3), 117-131.
  • Dempsey, P.S. & O’Connor, K. (1997). Air traffic congestion and infrastructure development in the Pacific Asia region. In C. Findlay, C.L. Sien and K. Singh (Eds.), Asia Pacific air transport: Challenges and policy reforms (pp. 23-47). Singapore: Institute of Southeast Asian Studies.
  • Dileep, M.R. and Kurien, A. (2022). Air transport and tourism: Interrelationship, operations and strategies. Abingdon: Routledge.
  • Efendigil, T., Önüt, S. and Kahraman, C. (2009). A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis. Expert Systems with Applications, 36(3), 6697–6707.
  • Hamed, H.A., Sadkhan, S.B. and Hameed, A.Q. (2018). Proposed adaptive neuro fuzzy inference system (ANFIS) identifier for M-ary frequency shift keying (FSK) signals with low SNR. In I.M.M. El Emary and A. Brzozowska (Eds.), Shaping the future of ICT; Trends in information technology, communications engineering, and management (pp. 259-268). Boca Raton, FL: CRC Press.
  • Holloway, S. (2016). Straight and level: Practical airline economics (3rd ed.). Abingdon: Routledge.
  • Karlaftis. M.G. (2010). Critical review and analysis of air-travel demand: Forecasting models. In L. Weigang, A. de
  • Barros and I. Romani de Oliveria (Eds.), Computational models, software engineering, and advanced technologies in air transportation: Next generation applications (pp. 72-87). Hershey, PA: IGI Global, 2010.
  • Karlaftis, M.G., Zografos, K.G., Papastavrou, J.D. and Charnes, J.M. (1996). Methodological framework for air-travel demand forecasting. Journal of Transportation Engineering, 122(2), 96-104.
  • Khosravanian, R., Sabah, M., Wood, D.A. and Shahryari, A. (2016). Weight on drill bit prediction models: Sugeno-type and Mamdani-type fuzzy inference systems compared. Journal of Natural Gas Science and Engineering, Part A, 280-297.
  • Kim, S. and Shin, D.H. (2016). Forecasting short-term air passenger demand using big data from search engine queries. Automation in Construction, 70, 98-108.
  • Mardani, A., Streimikiene, D., Nilashi, M., Arias Aranda, D., Loganathan, N. and Jusoh, A. (2018). Energy consumption, economic growth, and CO2 emissions in G20 Countries: Application of adaptive neuro-fuzzy inference system. Energies, 11(10), 2771.
  • Morikawa, Y., Tabata, T. & Emura, T. (2007). Ground improvements for the second phase construction of Kansai International Airport. In Y. Kikuchi, M. Otani, J. Kimura and Y. Morikawa (Eds.), Advances in deep foundations (pp. 389-402). Leiden: Taylor & Francis/Balkema.
  • Narang, S.K., Kumar, S. and Verma, V. (2017). Knowledge discovery from massive data streams. In A. Singh, N.
  • Dey, A.S. Ashour and V. Santhi (Eds.), Web semantics for textual and visual information retrieval (pp. 109-143). Hershey, PA: IGI Global: Hershey.
  • Ohta, K. (1999). International airports: Financing methods in Japan. Journal of Air Transport Management, 5(4), 223-234.
  • Papageorgiou, K., Papageorgiou, E.I., Poczeta, K., Bochtis, D. and Stamoulis, G. (2020). Forecasting of day-ahead natural gas consumption demand in Greece using adaptive neuro-fuzzy inference system. Energies, 13(9), 2317.
  • Patil, S.G., Mandal, S., Hegde, A.V. and Alavandar. S. (2011). Neuro-fuzzy based approach for wave transmission prediction of horizontally interlaced multilayer moored floating pipe breakwater. Ocean Engineering, 38(1), 186-196.
  • Pearce, B. (2012). The state of air transport markets and the airline industry after the great recession. Journal of Air Transport Management, 21, 3-9.
  • Piccioni, C., Stolfa, A. and Musso, A. (2022). Exogenous shocks on the air transport business: The effects of a global emergency. In R. Macário & E. Van de Voorde (Eds.), The air transport industry: Economic conflict and competition (pp. 99-124). Amsterdam: Elsevier.
  • Pigatto, A.V. and Balbinot, A. (2018). An automatic cycling performance measurement system based on ANFIS.
  • In W. Pedrycz and S.M. Chen (Eds.), Computational intelligence for pattern recognition (pp. 227-252). Cham: Springer International Publishing.
  • Raihana, K.K., Anjum, F., Saleh Mohamed Shoiab, A., Abdullah Ibne Hossain, M., Alimuzzman, M. and Rahman, R.M. (2017). In R. Silhavy, R. Senkerik, Z.K. Oplatkova., Z. Prokopova and P. Silhavy (Eds.), Artificial intelligence trends in intelligent systems: Proceedings of the Sixth Computer Science On-Line Conference 2017 (CSOC 2017), Volume 1 (pp. 322-332). Cham: Springer International Publishing.
  • Ravi, V., Kumar, P.R., Srinivas, E.R, and Kasabov, N.K. (2008). A semi-online training algorithm for the radial basis function neural networks: Applications to bankruptcy prediction in banks. In V. Ravi (Ed.), Advances in banking technology and management: Impacts of ICT and CRM (pp. 243-260). Hershey, PA: Information Science Reference.
  • Savkovic, B., Kovac, P., Dudic, B., Rodic, D., Taric, M. and Gregus, M. (2019). Application of an adaptive “neuro-fuzzy” inference system in modelling cutting temperature during hard turning. Applied Sciences, 9(18), 3739.
  • Sohag, M.S. and Rokonuzzaman. M. (2016). Demand forecasting for a domestic airport-A case study. In Proceedings of the 3rd International Conference on Civil Engineering for Sustainable Development (ICCESD 2016), 12~14 February 2016, KUET, Khulna, Bangladesh (pp. 1255-1264).
  • Srisaeng, P. and Baxter, G. (2021). Estimation of Australia’s outbound airline passenger demand using an adaptive neuro-fuzzy inference system. International Journal for Traffic and Transport Engineering, 11(3), 475 – 487.
  • Srisaeng, P., Baxter, G.S. and Wild, G. (2015). An adaptive neuro-fuzzy inference system for forecasting Australia’s domestic low-cost carrier passenger demand. Aviation, 19(3), 150-163, 2015.
  • Takagi, T. and Sugeno, M. (1985). Fuzzy identification of systems and its application to modelling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15(1), 116 – 132.
  • Tiryaki, S. and Aydın, A. (2014). An artificial neural network model for predicting compression strength of heat-treated woods and comparison with a multiple linear regression model. Construction and Building Materials, 62, 102-108.
  • Tsui, W.H.K. and Balli, F. (2015). International arrivals forecasting for Australian airports and the impact of tourism marketing expenditure. Tourism Economics, 23(2), 403-428.
  • Übeyli, E. D., Cvetkovic, D., Holland, G. and Cosic, I. (2010). Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of alterations in sleep EEG activity during hypopnoea episodes. Digital Signal Processing, 20(3), 678-691.
  • Wadud, Z. (2014). The asymmetric effects of income and fuel price on air transport demand. Transportation Research Part A: Policy and Practice, 65, 92-102.
  • Washington, S.P., Karlaftis, M.G. and Mannering, F. (2011). Statistical and econometric methods for transportation data analysis (2nd ed.). Boca Raton, FL: Chapman & Hall/CRC Press.
  • Wong, W.H., Cheung, T., Zhang, A. and Wang, Y. (2019). Is spatial dispersal the dominant trend in air transport development? A global analysis for 2006–2015. Journal of Air Transport Management, 74, 1-12.
  • Xiao, Y., Liu, J.J., Hu, Y., Wang, Y., Lai, K.K. and Wang, S. (2014). A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting. Journal of Air Transport Management, 39,1-11.
  • Yetilmezsoy, K., Fingas, M. and Fieldhouse, B. (2011). An adaptive neuro-fuzzy approach for modeling of water-in-oil emulsion formation. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 389, 1–3: 50–62.
  • Zheng, Y., Lei, K.K. & Wang, S. (2018). Forecasting air travel demand: Looking at China. Abingdon: Routledge. Zounemat-Kermani, M. and Scholz, M. (2013). Computing air demand using the Takagi-Sugeno Model for dam outlets. Water, 5(3), 1441-1456.
Toplam 48 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Uzay Mühendisliği
Bölüm Araştırma Makaleleri
Yazarlar

Panarat Srisaeng Bu kişi benim 0000-0002-7749-4884

Glenn Baxter 0000-0001-5910-622X

Yayımlanma Tarihi 23 Mart 2022
Gönderilme Tarihi 24 Ocak 2022
Kabul Tarihi 11 Mart 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 6 Sayı: 1

Kaynak Göster

APA Srisaeng, P., & Baxter, G. (2022). Application of an ANFIS to Estimate Kansai International Airport’s International Air Passenger Demand. Journal of Aviation, 6(1), 87-92. https://doi.org/10.30518/jav.1062151

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