Havalimanı Yolcularının Uluslararası Terminal Bazında Kontuarlara Geliş Paternlerin Tahminleme Çalışması

Yıl 2023, Sayı: 51, 63 - 74, 31.08.2023

Merve Gözde Sayın Doruk Yarkın Aktaş Mustafa Bolat Murat Kerem Çelenli Boran Dursun Gökhan Koç Kami Serdar Üçkardeş

https://doi.org/10.31590/ejosat.1214786

Öz

Havalimanı ekosisteminde iç içe geçmiş birçok operasyonel sürecin verimli yönetimini sağlayabilmek için yolcu işlem noktalarındaki yolcu varış miktarlarını yapay zeka tabanlı sistemlerle tahmin edebilmek çok önemlidir. Örneğin, havalimanının farklı bölümlerinde ve farklı operasyon türleri için ilerleyen saatlerde, günlerde ihtiyaç duyulacak yer hizmet personeli sayısını analiz edebilmek için havalimanına kaç yolcunun geleceğini tahmin edebilmek gerekmektedir. Ayrıca, yoğunluğa dayalı akıllı enerji yönetimi ve farklı hizmetlerde dinamik fiyat teklifi seçenekleri ancak doğru yolcu varış tahminleri ile oluşturulabilir. Günümüzde, ilgi tahminlemenin temelini oluşturan veri havuzu bilgisayarlı görü, IoT, lidar, radar gibi çeşitli teknolojilerle beslenebilmektedir ama bu çalışmada İzmir Adnan Menderes Havalimanı Dış Hatlar Terminali'nde kullanılan CUPPS çözümü ile yazıcılara gönderilen yolcu biniş kartı basma mesajları veri kaynağı olarak kullanılmıştır. Ayrıca Lineer regresyon, FEDOT, LSTM ve hibrit yöntemler konfigüre edilerek dış hat terminali bazında kaç adet yolcunun belirli zaman aralığında kontuarlara varacağını tahmin eden modeller geliştirilmiş ve birbirleri ile karşılaştırılmıştır.

Anahtar Kelimeler

Yolcu Varış, Patern, LSTM, FEDOT, Regresyon, Tahminleme., Passenger Arrival, Pattern, Regression, Prediction

A Study Of Predicting Arrival Patterns Of Airport Passengers To The Counters On The Basis Of International Terminal

Öz

AI-based passenger arrival predictions to the processing points are essential to ensure efficient management of many intertwined operational processes in the airport ecosystem. For example, to be able to analyze the number of ground service personnel that will be required in the following hours, days in different parts of the airport and for different types of operations, it is essential to predict how many passengers will come to the airport in the following time zones. Moreover, density-driven intelligent energy management and dynamic price offering options in different services could only be generated with accurate passenger arrival predictions. Passenger arrivals can be detected with various technologies such as computer vision, IoT, lidar, and radar. However, passenger boarding pass printing event messages from the CUPPS solution, which is implemented in İzmir Adnan Menderes Airport International Terminal, is used as the data source in this study. Also, Linear regression, FEDOT, LSTM, and hybrid methods are configured and compared to predict passenger arrival counts to the counters of the international terminal in the specified time slots.

Anahtar Kelimeler

Passenger Arrival, Pattern, Regression, Prediction

Kaynakça

• De Neufville, R. (2016). Airport systems planning and design. In Air Transport Management (pp. 89-106). Routledge.
• Park, Y., & Ahn, S. B. (2003). Optimal assignment for check-in counters based on passenger arrival behaviour at an airport. Transportation Planning and Technology, 26(5), 397-416.
• Fayez, M. S., Kaylani, A., Cope, D., Rychlik, N., & Mollaghasemi, M. (2008). Managing airport operations using simulation. Journal of Simulation, 2(1), 41-52.
• Martín-Cejas, R. R. (2006). Tourism service quality begins at the airport. Tourism Management, 27(5), 874-877.
• Kirschenbaum, A. A. (2013). The cost of airport security: The passenger dilemma. Journal of Air Transport Management, 30, 39-45.
• Bevilacqua, M., & Ciarapica, F. E. (2010, December). Analysis of check-in procedure using simulation: a case study. In 2010 IEEE International Conference on Industrial Engineering and Engineering Management (pp. 1621-1625). IEEE.
• Kalakou, S., Psaraki-Kalouptsidi, V., & Moura, F. (2015). Future airport terminals: New technologies promise capacity gains. Journal of Air Transport Management, 42, 203-212.
• van Boekhold, J., Faghri, A., & Li, M. (2014). Evaluating security screening checkpoints for domestic flights using a general microscopic simulation model. Journal of Transportation Security, 7(1), 45-67.
• Ashford, N. J., Stanton, H. M., Moore, C. A., AAE, P. C., & Beasley, J. R. (2013). Airport operations. McGraw-Hill Education.
• Postorino, M. N., Mantecchini, L., Malandri, C., & Paganelli, F. (2019). Airport passenger arrival process: Estimation of earliness arrival functions. Transportation Research Procedia, 37, 338-345.
• Manataki, I. E., & Zografos, K. G. (2009). A generic system dynamics based tool for airport terminal performance analysis. Transportation Research Part C: Emerging Technologies, 17(4), 428-443.
• Ashford, N. J., Mumayiz, S., & Wright, P. H. (2011). Airport engineering: planning, design, and development of 21st century airports. John Wiley & Sons.
• Cheng, L. (2014). Modelling airport passenger group dynamics using an agent-based method (Doctoral dissertation, Queensland University of Technology).
• Alodhaibi, S., Burdett, R. L., & Yarlagadda, P. K. (2019). Impact of passenger-arrival patterns in outbound processes of airports. Procedia Manufacturing, 30, 323-330.
• Jin, Y., Yan, D., Chong, A., Dong, B., & An, J. (2021). Building occupancy forecasting: A systematical and critical review. Energy and Buildings, 251, 111345.
• Schreiber, D., & Rauter, M. (2012, June). A CPU-GPU hybrid people counting system for real-world airport scenarios using arbitrary oblique view cameras. In 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (pp. 83-88). IEEE.
• Mizutani, M., Uchiyama, A., Murakami, T., Abeysekera, H., & Higashino, T. (2020, March). Towards people counting using Wi-Fi CSI of mobile devices. In 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (pp. 1-6). IEEE.
• Kouyoumdjieva, S. T., Danielis, P., & Karlsson, G. (2019). Survey of non-image-based approaches for counting people. IEEE Communications Surveys & Tutorials, 22(2), 1305-1336.
• Monmousseau, P., Jarry, G., Bertosio, F., Delahaye, D., & Houalla, M. (2020, February). Predicting passenger flow at Charles de Gaulle airport security checkpoints. In 2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) (pp. 1-9). IEEE.
• Guo, J., Xie, Z., Qin, Y., Jia, L., & Wang, Y. (2019). Short-term abnormal passenger flow prediction based on the fusion of SVR and LSTM. IEEE Access, 7, 42946-42955.
• Li, Z., Bi, J., & Li, Z. (2017, December). Passenger flow forecasting research for airport terminal based on SARIMA time series model. In IOP conference series: earth and environmental science (Vol. 100, No. 1, p. 012146). IOP Publishing.
• Takakuwa, S., Oyama, T., & Chick, S. (2003, December). Simulation analysis of international-departure passenger flows in an airport terminal. In Winter Simulation Conference (Vol. 2, pp. 1627-1634).
• Guizzi, G., Murino, T., & Romano, E. (2009). A discrete event simulation to model passenger flow in the airport terminal. Proc. 11th WSEAS Int. Conf. Math. Methods Comput. Tech. Electr. Eng., pp. 427–434.
• Munasingha, K., & Adikariwattage, V. (2020). Discrete Event Simulation Method to Model Passenger Processing at an International Airport. In 2020 Moratuwa Engineering Research Conference (MERCon) (pp. 401-406). doi: 10.1109/MERCon50084.2020.9185370.
• Airport Cooperative Research Program (ACRP). (2010). Airport Passenger Terminal Planning and Design, Volume 1: Guidebook. Airport Cooperative Research Program. National Academies of Sciences, Engineering, and Medicine.
• Postorino, M. N., Mantecchini, L., Malandri, C., & Paganelli, F. (2019). Airport Passenger Arrival Process: Estimation of Earliness Arrival Functions. Transportation Research Procedia, Volume 37.
• Olaru, D. (2008). Simulation and GA-optimisation for modeling the operation of airport passenger terminals.
• Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, USA.
• Davis, L. D. (1991). Handbook of Genetic Algorithms. Van Nostrand, New York.