Havalimanı Yolcularının Uluslararası Terminal Bazında Kontuarlara Geliş Paternlerin Tahminleme Çalışması

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

Abstract

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.

Keywords

• Passenger Arrival
• Pattern
• Regression
• Prediction

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