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Yıl 2023, Cilt: 7 Sayı: 3, 415 - 424, 15.11.2023

Bahri Baran Koçak

https://doi.org/10.30518/jav.1351229

Öz

Kaynakça

  • Akaike, H. (1973), Maximum likelihood identification of Gaussian autoregressive moving average models, Biometrika, 60, 255–265.
  • Boonekamp, T., Zuidberg, J., & Burghouwt, G. (2018). Determinants of air travel demand: The role of low-cost carriers, ethnic links and aviation-dependent employment. Transportation Research Part A: Policy and Practice, 112, 18-28.
  • Breiman, L. (2001). Random Forests, Machine Learning, 45(1), 5-32.
  • Candel, A., & LeDell, E. (2020). Deep Learning with H2O. (6th ed.), H2O.ai, Inc, Mountain View, CA, pp.1-55.
  • Carmona-Benítez, R. B., Nieto, M. R., & Miranda, D. (2017). An Econometric Dynamic Model to estimate passenger demand for air transport industry. Transportation Research Procedia, 25, 17-29.
  • Chou, J. S., & Tran, D. S. (2018). Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders. Energy, 165, 709-726.
  • CTP. (2022). Aviation sector guide. https://www.ctp.org.uk/assets/x/55122 (Accessed: December 8, 2022).
  • Dantas, T. M., Oliveira, F. L. C., & Repolho, H. M. V. (2017). Air transportation demand forecast through Bagging Holt Winters methods. Journal of Air Transport Management, 59, 116-123.
  • Das, A. K., Bardhan, A. K., & Fageda, X. (2022). What is driving the passenger demand on new regional air routes in India: A study using the gravity model. Case Studies on Transport Policy, 10(1), 637-646.
  • Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431.
  • Dreher, P. C., Tong, C., Ghiraldi, E., & Friedlander, J. I. (2018). Use of Google Trends to Track Online Behavior and Interest in Kidney Stone Surgery. Urology, 121, 74-78.
  • Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica: journal of the Econometric Society, 251-276.
  • Frank, E., Hall, M., & Witten, I. H. (2016). The WEKA Workbench. Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, Fourth Edition, 2016.
  • Glorot, X., & Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks, in: Proceedings of International Conference on Artificial Intelligence and Statistics, pp. 249–256.
  • Google. (2022). https://support.google.com/trends/answer/ 4365533? hl=tr&ref_topic=6248052 (Accessed: December 8, 2022).
  • Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: journal of the Econometric Society, 37, 424-438.
  • Gultekin, N., & Acik Kemaloglu, S. (2023). Evaluation of the impact of Covid-19 on air traffic volume in Turkish airspace using artificial neural networks and time series. Scientific reports, 13(1), 6551.
  • Hadavandi, E., Shavandi, H., Ghanbari, A., & Abbasian-Naghneh, S. (2012). Developing a hybrid artificial intelligence model for outpatient visits forecasting in hospitals. Applied Soft Computing, 12(2), 700–711.
  • Hsiao, C. Y., & Hansen, M. (2011). A passenger demand model for air transportation in a hub-and-spoke network. Transportation Research Part E: Logistics and Transportation Review, 47(6), 1112-1125.
  • Hu, S., Liu, M., Fong, S., Song, W., Dey, N., & Wong, R. (2018). Forecasting China future MNP by deep learning. In Behavior engineering and applications (pp. 169-210). Springer, Cham.
  • IATA. (2022). Global outlook for air transport: Times of turbulence. https://www.iata.org/en/iata-repository/ publications/economic-reports/airline-industry-economic-performance---june-2022---report/ (Accessed: December 8, 2022).
  • Jin, F., Li, Y., Sun, S., & Li, H. (2020). Forecasting air passenger demand with a new hybrid ensemble approach. Journal of Air Transport Management, 83, 101744.
  • Kanavos, A., Kounelis, F., Iliadis, L., & Makris, C. (2021). Deep learning models for forecasting aviation demand time series. Neural Computing and Applications, 33(23), 16329-16343.
  • Ke-wu, Y. (2009). Study on the forecast of air passenger flow based on SVM regression algorithm. In 2009 First International Workshop on Database Technology and Applications (pp. 325-328). IEEE.
  • Kim, S. (2016). Forecasting short-term air passenger demand using big data from search engine queries. Automation in Construction, 70, 98-108.
  • Kim, S., & Shin, D. H. (2016). Forecasting short-term air passenger demand using big data from search engine queries. Automation in Construction, 70, 98-108.
  • Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
  • Koçak, B. B. (2020). Exploring and identification of passengers’web search goals using ticket related queries in the airline market: a google trends study. Pazarlama ve Pazarlama Araştırmaları Dergisi, 3(2020), 443-460.
  • Koçak, B. B. (2023). Deep Learning Techniques for Short-Term Air Passenger Demand Forecasting with Destination Insight: A Case Study in The New Zealand Air Market. International Research in Social, Human and Administrative Sciences XII, 61.
  • Kumar, M., & Thenmozhi, M. (2006). Forecasting stock index movement: A comparison of support vector machines and random forest. In Indian institute of capital markets 9th capital markets conference paper.
  • Lai, K., Lee, Y. X., Chen, H., & Yu, R. (2017). Research on web search behavior: How online query data inform social psychology. Cyberpsychology, Behavior, and Social Networking, 20(10), 596-602.
  • Laik, M. N., Choy, M., & Sen, P. (2014). Predicting airline passenger load: A case study. In 2014 IEEE 16th Conference on Business Informatics (Vol. 1, pp. 33-38). IEEE.
  • Liang, X., Zhang, Q., Hong, C., Niu, W., & Yang, M. (2022). Do Internet Search Data Help Forecast Air Passenger Demand? Evidence from China’s Airports. Frontiers in Psychology, 13.
  • Little, R., Williams, C., & Yost, J. (2011). Airline travel: A history of information-seeking behavior by leisure and business passengers. W. Aspray, B.M. Hayes (Eds.), Everyday information: The evolution of seeking in America, 121-156.
  • Liu, L., & Chen, R. C. (2017). A novel passenger flow prediction model using deep learning methods. Transportation Research Part C: Emerging Technologies, 84, 74-91.
  • Long, C. L., Guleria, Y., & Alam, S. (2021). Air passenger forecasting using Neural Granger causal Google trend queries. Journal of Air Transport Management, 95, 102083.
  • Lu, Y., Park, Y., Chen, L., Wang, Y., De Sa, C., & Foster, D. (2021). Variance Reduced Training with Stratified Sampling for Forecasting Models. In International Conference on Machine Learning (pp. 7145-7155). PMLR.
  • MacKinnon, J. G. (1996). Numerical distribution functions for unit root and cointegration tests. Journal of applied econometrics,11(6), 601-618.
  • Madas, M. A., & Zografos, K. G. (2010). Airport slot allocation: a time for change? Transport Policy, 17(4), 274-285.
  • Massaro, A., Maritati, V., & Galiano, A. (2018). Data Mining model performance of sales predictive algorithms based on RapidMiner workflows. International Journal of Computer Science & Information Technology (IJCSIT), 10(3), 39-56.
  • Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., and Euler, T. (2006) YALE: Rapid prototyping for complex data mining tasks. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
  • Mumayiz, S.A., & Pulling, R.W. (1992). Forecasting air passenger demand in multi-airport regions. in: Proceedings of the Transportation Research Forum. TRF, Arlington.
  • Nwulu, N. I. (2017). A decision trees approach to oil price prediction. In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP) (pp. 1-5). IEEE.
  • Önder, I., & Gunter, U. (2016). Forecasting tourism demand with Google trends for a major European city destination. Tourism Analysis, 21(2-3), 203-220.
  • Park, S., Lee, J., & Song, W. (2017). Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data. Journal of Travel & Tourism Marketing, 34(3), 357-368.
  • Parvat, A., Chavan, J., Kadam, S., Dev, S., & Pathak, V. (2017). A survey of deep-learning frameworks. In 2017 International Conference on Inventive Systems and Control (ICISC) (pp. 1-7). IEEE.
  • Peterson, R. A., & Merino, M. C. (2003). Consumer information search behavior and the Internet. Psychology & Marketing, 20(2), 99-121.
  • Platt, J. (1999) ‘Sequential minimal optimization: a fast algorithm for training support vector machines’, in Scholkopf, B. et al. (Eds.): Advances in Kernel Methods: Support Vector Learning, pp.185–208, MIT Press, Cambridge, MA, USA.
  • Putra, A. M., & Kusumastuti, R. D. (2019). Forecasting airline passenger demand for the long-haul route: The case of Garuda Indonesia. In Proceedings of the 2nd International Conference on Inclusive Business in the Changing World. doi (Vol. 10, No. 0008433305300537).
  • Quinlan, J. R. (2014). C4. 5: programs for machine learning. Elsevier.
  • Rashad, A. S. (2022). The Power of Travel Search Data in Forecasting the Tourism Demand in Dubai. Forecasting, 4(3), 674-684.
  • Rolim, P. S., Bettini, H. F., & Oliveira, A. V. (2016). Estimating the impact of airport privatization on airline demand: A regression-based event study. Journal of Air Transport Management, 54, 31-41.
  • Shevade, S. K., Keerthi, S. S., Bhattacharyya, C., & Murthy, K. R. K. (2000). Improvements to the SMO algorithm for SVM regression. IEEE transactions on neural networks, 11(5), 1188-1193.
  • Shin, E., Yang, D. H., Sohn, S. C., Huh, M., & Baek, S. (2017). Search Trend's Effects On Forecasting the Number of Outbound Passengers of the Incheon Airport. Journal of IKEEE, 21(1), 13-23.
  • Sismanidou, A., & Tarradellas, J. (2017). 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(2), 188-199.
  • Smola, A. J., & Schölkopf, B. (1998). A tutorial on support vector regression. Statistics and Computing, 14(3), 199– 222.
  • Srisaeng, P., & Baxter, G. (2017). Modelling Australia’s outbound passenger air travel demand using an artificial neural network approach. International Journal for Traffic and Transport Engineering, 7(4), 406-423.
  • Srisaeng, P., Baxter, G., & Wild, G. (2015). Using an artificial neural network approach to forecast Australia's domestic passenger air travel demand. World Review of Intermodal Transportation Research, 5(3), 281-313.
  • Suh, D. Y., & Ryerson, M. S. (2019). Forecast to grow: Aviation demand forecasting in an era of demand uncertainty and optimism bias. Transportation Research Part E: Logistics and Transportation Review, 128, 400- 416.
  • Sun, S., Wei, Y., Tsui, K. L., & Wang, S. (2019). Forecasting tourist arrivals with machine learning and internet search index. Tourism Management, 70, 1-10.
  • Treeratanaporn, T., Rochananak, P., & Srichaikij, C. (2021). Data analytics for electricity revenue forecasting by using linear regression and classification method. In 2021 9th International Electrical Engineering Congress (iEECON) (pp. 468-471). IEEE.
  • Tsui, W.H.K., Ozer Balli, H., Gilbey, A., Gow, H. (2014). Forecasting of Hong Kong airport’s passenger throughput. Tour. Manag. 42, 62–76.
  • Tung, T. M., & Yaseen, Z. M. (2021). Deep learning for prediction of water quality index classification: tropical catchment environmental assessment. Natural Resources Research, 30(6), 4235-4254.
  • UK. (2022). Statistics relating to passenger arrivals in the United Kingdom since the COVID-19 outbreak, May 2022.https://www.gov.uk/government/statistics/statistics-relating-to-passenger-arrivals-since-the-covid-19- outbreak-may-2022/statistics-relating-to-passenger-arrivals-in-the-united-kingdom-since-the-covid-19- outbreak-may-2022 (Accessed: December 8, 2022).
  • Wang, S., & Gao, Y. (2021). A literature review and citation analyses of air travel demand studies published between 2010 and 2020. Journal of Air Transport Management, 97, 102135.
  • Widiasari, I. R., & Nugroho, L. E. (2017). Deep learning multilayer perceptron (MLP) for flood prediction model using wireless sensor network based hydrology time series data mining. In 2017 International Conference on Innovative and Creative Information Technology (ICITech) (pp. 1-5). IEEE.
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  • Yu, L., Zhao, Y., Tang, L., & Yang, Z. (2019). Online big data-driven oil consumption forecasting with Google trends. International Journal of Forecasting, 35(1), 213-223.
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Comparison of Artificial Intelligence Techniques for The UK Air Passenger Short-Term Demand Forecasting: A Destination Insight Study

Yıl 2023, Cilt: 7 Sayı: 3, 415 - 424, 15.11.2023

Bahri Baran Koçak

https://doi.org/10.30518/jav.1351229

Öz

Web search queries become essential drivers to forecast air passenger demand for operational benefits. Scholars and marketing experts. Forecasting passenger demand is one of the most important marketing problems that experts frequently encounter, but there are very few studies in the literature using search queries. The main novelty of this study is to show that Destination Insight (DI) can be useful as an air passenger demand proxy in the UK. To prove this primary objective, this work uses several machine and deep learning multi-layer perceptron (MLP) methods based on a big-data framework. The findings indicate that DI is a crucial predictor of the UK air passenger demand. Besides, popular error metrics (RMSE, MAPE, MAD and AIC) were compared to find the best model in this study. Specifically, results indicate that MLP following feed forward neural networks works better for the UK air passenger market.

Anahtar Kelimeler

Destination insight air passenger demand forecasting artificial intelligence consumer search behavior big data analytics

Kaynakça

  • Akaike, H. (1973), Maximum likelihood identification of Gaussian autoregressive moving average models, Biometrika, 60, 255–265.
  • Boonekamp, T., Zuidberg, J., & Burghouwt, G. (2018). Determinants of air travel demand: The role of low-cost carriers, ethnic links and aviation-dependent employment. Transportation Research Part A: Policy and Practice, 112, 18-28.
  • Breiman, L. (2001). Random Forests, Machine Learning, 45(1), 5-32.
  • Candel, A., & LeDell, E. (2020). Deep Learning with H2O. (6th ed.), H2O.ai, Inc, Mountain View, CA, pp.1-55.
  • Carmona-Benítez, R. B., Nieto, M. R., & Miranda, D. (2017). An Econometric Dynamic Model to estimate passenger demand for air transport industry. Transportation Research Procedia, 25, 17-29.
  • Chou, J. S., & Tran, D. S. (2018). Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders. Energy, 165, 709-726.
  • CTP. (2022). Aviation sector guide. https://www.ctp.org.uk/assets/x/55122 (Accessed: December 8, 2022).
  • Dantas, T. M., Oliveira, F. L. C., & Repolho, H. M. V. (2017). Air transportation demand forecast through Bagging Holt Winters methods. Journal of Air Transport Management, 59, 116-123.
  • Das, A. K., Bardhan, A. K., & Fageda, X. (2022). What is driving the passenger demand on new regional air routes in India: A study using the gravity model. Case Studies on Transport Policy, 10(1), 637-646.
  • Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431.
  • Dreher, P. C., Tong, C., Ghiraldi, E., & Friedlander, J. I. (2018). Use of Google Trends to Track Online Behavior and Interest in Kidney Stone Surgery. Urology, 121, 74-78.
  • Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica: journal of the Econometric Society, 251-276.
  • Frank, E., Hall, M., & Witten, I. H. (2016). The WEKA Workbench. Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, Fourth Edition, 2016.
  • Glorot, X., & Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks, in: Proceedings of International Conference on Artificial Intelligence and Statistics, pp. 249–256.
  • Google. (2022). https://support.google.com/trends/answer/ 4365533? hl=tr&ref_topic=6248052 (Accessed: December 8, 2022).
  • Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: journal of the Econometric Society, 37, 424-438.
  • Gultekin, N., & Acik Kemaloglu, S. (2023). Evaluation of the impact of Covid-19 on air traffic volume in Turkish airspace using artificial neural networks and time series. Scientific reports, 13(1), 6551.
  • Hadavandi, E., Shavandi, H., Ghanbari, A., & Abbasian-Naghneh, S. (2012). Developing a hybrid artificial intelligence model for outpatient visits forecasting in hospitals. Applied Soft Computing, 12(2), 700–711.
  • Hsiao, C. Y., & Hansen, M. (2011). A passenger demand model for air transportation in a hub-and-spoke network. Transportation Research Part E: Logistics and Transportation Review, 47(6), 1112-1125.
  • Hu, S., Liu, M., Fong, S., Song, W., Dey, N., & Wong, R. (2018). Forecasting China future MNP by deep learning. In Behavior engineering and applications (pp. 169-210). Springer, Cham.
  • IATA. (2022). Global outlook for air transport: Times of turbulence. https://www.iata.org/en/iata-repository/ publications/economic-reports/airline-industry-economic-performance---june-2022---report/ (Accessed: December 8, 2022).
  • Jin, F., Li, Y., Sun, S., & Li, H. (2020). Forecasting air passenger demand with a new hybrid ensemble approach. Journal of Air Transport Management, 83, 101744.
  • Kanavos, A., Kounelis, F., Iliadis, L., & Makris, C. (2021). Deep learning models for forecasting aviation demand time series. Neural Computing and Applications, 33(23), 16329-16343.
  • Ke-wu, Y. (2009). Study on the forecast of air passenger flow based on SVM regression algorithm. In 2009 First International Workshop on Database Technology and Applications (pp. 325-328). IEEE.
  • Kim, S. (2016). Forecasting short-term air passenger demand using big data from search engine queries. Automation in Construction, 70, 98-108.
  • Kim, S., & Shin, D. H. (2016). Forecasting short-term air passenger demand using big data from search engine queries. Automation in Construction, 70, 98-108.
  • Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
  • Koçak, B. B. (2020). Exploring and identification of passengers’web search goals using ticket related queries in the airline market: a google trends study. Pazarlama ve Pazarlama Araştırmaları Dergisi, 3(2020), 443-460.
  • Koçak, B. B. (2023). Deep Learning Techniques for Short-Term Air Passenger Demand Forecasting with Destination Insight: A Case Study in The New Zealand Air Market. International Research in Social, Human and Administrative Sciences XII, 61.
  • Kumar, M., & Thenmozhi, M. (2006). Forecasting stock index movement: A comparison of support vector machines and random forest. In Indian institute of capital markets 9th capital markets conference paper.
  • Lai, K., Lee, Y. X., Chen, H., & Yu, R. (2017). Research on web search behavior: How online query data inform social psychology. Cyberpsychology, Behavior, and Social Networking, 20(10), 596-602.
  • Laik, M. N., Choy, M., & Sen, P. (2014). Predicting airline passenger load: A case study. In 2014 IEEE 16th Conference on Business Informatics (Vol. 1, pp. 33-38). IEEE.
  • Liang, X., Zhang, Q., Hong, C., Niu, W., & Yang, M. (2022). Do Internet Search Data Help Forecast Air Passenger Demand? Evidence from China’s Airports. Frontiers in Psychology, 13.
  • Little, R., Williams, C., & Yost, J. (2011). Airline travel: A history of information-seeking behavior by leisure and business passengers. W. Aspray, B.M. Hayes (Eds.), Everyday information: The evolution of seeking in America, 121-156.
  • Liu, L., & Chen, R. C. (2017). A novel passenger flow prediction model using deep learning methods. Transportation Research Part C: Emerging Technologies, 84, 74-91.
  • Long, C. L., Guleria, Y., & Alam, S. (2021). Air passenger forecasting using Neural Granger causal Google trend queries. Journal of Air Transport Management, 95, 102083.
  • Lu, Y., Park, Y., Chen, L., Wang, Y., De Sa, C., & Foster, D. (2021). Variance Reduced Training with Stratified Sampling for Forecasting Models. In International Conference on Machine Learning (pp. 7145-7155). PMLR.
  • MacKinnon, J. G. (1996). Numerical distribution functions for unit root and cointegration tests. Journal of applied econometrics,11(6), 601-618.
  • Madas, M. A., & Zografos, K. G. (2010). Airport slot allocation: a time for change? Transport Policy, 17(4), 274-285.
  • Massaro, A., Maritati, V., & Galiano, A. (2018). Data Mining model performance of sales predictive algorithms based on RapidMiner workflows. International Journal of Computer Science & Information Technology (IJCSIT), 10(3), 39-56.
  • Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., and Euler, T. (2006) YALE: Rapid prototyping for complex data mining tasks. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
  • Mumayiz, S.A., & Pulling, R.W. (1992). Forecasting air passenger demand in multi-airport regions. in: Proceedings of the Transportation Research Forum. TRF, Arlington.
  • Nwulu, N. I. (2017). A decision trees approach to oil price prediction. In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP) (pp. 1-5). IEEE.
  • Önder, I., & Gunter, U. (2016). Forecasting tourism demand with Google trends for a major European city destination. Tourism Analysis, 21(2-3), 203-220.
  • Park, S., Lee, J., & Song, W. (2017). Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data. Journal of Travel & Tourism Marketing, 34(3), 357-368.
  • Parvat, A., Chavan, J., Kadam, S., Dev, S., & Pathak, V. (2017). A survey of deep-learning frameworks. In 2017 International Conference on Inventive Systems and Control (ICISC) (pp. 1-7). IEEE.
  • Peterson, R. A., & Merino, M. C. (2003). Consumer information search behavior and the Internet. Psychology & Marketing, 20(2), 99-121.
  • Platt, J. (1999) ‘Sequential minimal optimization: a fast algorithm for training support vector machines’, in Scholkopf, B. et al. (Eds.): Advances in Kernel Methods: Support Vector Learning, pp.185–208, MIT Press, Cambridge, MA, USA.
  • Putra, A. M., & Kusumastuti, R. D. (2019). Forecasting airline passenger demand for the long-haul route: The case of Garuda Indonesia. In Proceedings of the 2nd International Conference on Inclusive Business in the Changing World. doi (Vol. 10, No. 0008433305300537).
  • Quinlan, J. R. (2014). C4. 5: programs for machine learning. Elsevier.
  • Rashad, A. S. (2022). The Power of Travel Search Data in Forecasting the Tourism Demand in Dubai. Forecasting, 4(3), 674-684.
  • Rolim, P. S., Bettini, H. F., & Oliveira, A. V. (2016). Estimating the impact of airport privatization on airline demand: A regression-based event study. Journal of Air Transport Management, 54, 31-41.
  • Shevade, S. K., Keerthi, S. S., Bhattacharyya, C., & Murthy, K. R. K. (2000). Improvements to the SMO algorithm for SVM regression. IEEE transactions on neural networks, 11(5), 1188-1193.
  • Shin, E., Yang, D. H., Sohn, S. C., Huh, M., & Baek, S. (2017). Search Trend's Effects On Forecasting the Number of Outbound Passengers of the Incheon Airport. Journal of IKEEE, 21(1), 13-23.
  • Sismanidou, A., & Tarradellas, J. (2017). 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(2), 188-199.
  • Smola, A. J., & Schölkopf, B. (1998). A tutorial on support vector regression. Statistics and Computing, 14(3), 199– 222.
  • Srisaeng, P., & Baxter, G. (2017). Modelling Australia’s outbound passenger air travel demand using an artificial neural network approach. International Journal for Traffic and Transport Engineering, 7(4), 406-423.
  • Srisaeng, P., Baxter, G., & Wild, G. (2015). Using an artificial neural network approach to forecast Australia's domestic passenger air travel demand. World Review of Intermodal Transportation Research, 5(3), 281-313.
  • Suh, D. Y., & Ryerson, M. S. (2019). Forecast to grow: Aviation demand forecasting in an era of demand uncertainty and optimism bias. Transportation Research Part E: Logistics and Transportation Review, 128, 400- 416.
  • Sun, S., Wei, Y., Tsui, K. L., & Wang, S. (2019). Forecasting tourist arrivals with machine learning and internet search index. Tourism Management, 70, 1-10.
  • Treeratanaporn, T., Rochananak, P., & Srichaikij, C. (2021). Data analytics for electricity revenue forecasting by using linear regression and classification method. In 2021 9th International Electrical Engineering Congress (iEECON) (pp. 468-471). IEEE.
  • Tsui, W.H.K., Ozer Balli, H., Gilbey, A., Gow, H. (2014). Forecasting of Hong Kong airport’s passenger throughput. Tour. Manag. 42, 62–76.
  • Tung, T. M., & Yaseen, Z. M. (2021). Deep learning for prediction of water quality index classification: tropical catchment environmental assessment. Natural Resources Research, 30(6), 4235-4254.
  • UK. (2022). Statistics relating to passenger arrivals in the United Kingdom since the COVID-19 outbreak, May 2022.https://www.gov.uk/government/statistics/statistics-relating-to-passenger-arrivals-since-the-covid-19- outbreak-may-2022/statistics-relating-to-passenger-arrivals-in-the-united-kingdom-since-the-covid-19- outbreak-may-2022 (Accessed: December 8, 2022).
  • Wang, S., & Gao, Y. (2021). A literature review and citation analyses of air travel demand studies published between 2010 and 2020. Journal of Air Transport Management, 97, 102135.
  • Widiasari, I. R., & Nugroho, L. E. (2017). Deep learning multilayer perceptron (MLP) for flood prediction model using wireless sensor network based hydrology time series data mining. In 2017 International Conference on Innovative and Creative Information Technology (ICITech) (pp. 1-5). IEEE.
  • Wu, X., Xiang, Y., Mao, G., Du, M., Yang, X., & Zhou, X. (2021). Forecasting air passenger traffic flow based on the two-phase learning model. The Journal of Supercomputing, 77(5), 4221-4243.
  • Xiao, Y., Liu, J. J., Hu, Y., Wang, Y., Lai, K. K., & 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.
  • Xie, G., Wang, S., & Lai, K. K. (2014). Short-term forecasting of air passenger by using hybrid seasonal decomposition and least squares support vector regression approaches. Journal of Air Transport Management, 37, 20-26.
  • Yu, L., Zhao, Y., Tang, L., & Yang, Z. (2019). Online big data-driven oil consumption forecasting with Google trends. International Journal of Forecasting, 35(1), 213-223.
  • Yang, J. F., Zhai, Y. J., Xu, D. P., & Han, P. (2007). SMO algorithm applied in time series model building and forecast. In 2007 International Conference on Machine Learning and Cybernetics (Vol. 4, pp. 2395-2400). IEEE.
  • Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of the art. International journal of forecasting, 14(1), 35-62.
  • Zhang, F., & Graham, D. J. (2020). Air transport and economic growth: a review of the impact mechanism and causal relationships. Transport Reviews, 40(4), 506-528.
  • Zhang, B., Li, N., Shi, F., & Law, R. (2020). A deep learning approach for daily tourist flow forecasting with consumer search data. Asia Pacific Journal of Tourism Research, 25(3), 323-339.
  • Zhang, Anming, Yahua Zhang, and Zhibin Huang. (2021). Airline Economics. In International Encyclopedia of Transportation, edited by Roger Vickerman, 392–396. Oxford: Elsevier.
Toplam 75 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İşletme , Ulaşım, Lojistik ve Tedarik Zincirleri (Diğer)
Bölüm Araştırma Makaleleri
Yazarlar

Bahri Baran Koçak 0000-0001-5658-7371

Yayımlanma Tarihi 15 Kasım 2023
Gönderilme Tarihi 29 Ağustos 2023
Kabul Tarihi 2 Ekim 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 7 Sayı: 3

Kaynak Göster

APA Koçak, B. B. (2023). Comparison of Artificial Intelligence Techniques for The UK Air Passenger Short-Term Demand Forecasting: A Destination Insight Study. Journal of Aviation, 7(3), 415-424. https://doi.org/10.30518/jav.1351229
 Kapak Resmi İndir

Journal of Aviation - JAV 

www.javsci.com - editor@javsci.com


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