Analysis of Flight Based Airport Passenger Arrival Patterns

Year 2023, Volume: 1 Issue: 1, 1 - 10, 10.08.2023

Merve Gözde Sayın , Mustafa Bolat

Abstract

Maintaining effective and successful work in uncertainty is challenging in a niche sector such as aviation and all industries and situations. The situation is a little more complicated, especially in a sector such as the aviation sector, where the rules are not stretched and where one should always be on the alert. For these reasons, it is precious for businesses that carry out airline operations to analyze arriving passengers and understand whether there is a pattern among passengers. Although there are many factors affecting the arrival patterns of passengers, some of them are particularly noteworthy. For example, factors such as whether the flight is domestic or international, the season in which the flight takes place, and the flight hours take place to provide a broad perspective on the arrival patterns of the passengers at the airport. For example, it can be stated that while passengers on international flights see earlier arrival patterns at the airport, last-minute arrivals are more frequent on domestic flights. Similarly, in domestic flights, different passenger arrival patterns have been determined for the arrival airport, depending on the seasonality of the countries. On the other hand, according to the seasonality of the nations (for example, the opening of schools and national holidays), different passenger arrival patterns have been determined for the destination airport. Analyzes were carried out in Python and Excel and included case study outputs.

Keywords

Pattern Analysis, Aviation, passenger, flights

Uçuş Bazlı Havalimanı Yolcu Geliş Örüntüleri Analizi

Abstract

Belirsizlik içerisinde etkili ve başarılı herhangi bir çalışma sürdürmek sadece havacılık gibi niş bir sektörde değil bütün sektörlerde ve durumlarda oldukça zorlayıcı bir durumdur. Özellikle havacılık sektörü gibi kuralları esnetilmeyen ve her daim tetikte olunması gereken bir sektörde durum biraz daha komplikedir. Bu sebeplerden ötürü gelen yolcuların analizini geçekleştirmek, yolcular içerisinde bir örüntü olup olmadığını anlamak havayolu operasyonları geçekeştiren işletmeler için oldukça değerlidir. Yolcuların havayoluna geliş örüntülerini etkileyen oldukça fazla faktör olmasına ragmen, özellikle bazıları dikkat çekmektedir. Örneğin, uçuşun yurtiçi ya da yurtdışı uçuşu olması, uçuşun hangi mevsimde gerçekleştiği, uçuşun hangi saatlerde gerçekleştiği gibi faktörler yolcuların havalimanına geliş örüntülerine dair oldukça geniş perspektifte bilgiler vermektedir. Uluslararası uçuşlarda bulunan yolcularda havalimanına daha erken geliş örüntüleri görürken, yurtiçi uçuşlarında son dakika gelişlerin daha sıklıkla olduğu belirtilebilir. Benzer şekilde, yurtiçi uçuşlarında bayram tatilleri veya dönemselliğe bağlı (örneğin okulların açılması) gibi durumlarda uçuşların yoğunlaştığı, öbür taraftan ülkelerin sezonsallık durumlarına göre varış havalimanı özelinde farklı yolcu geliş örünrtüleri tespit edilmiştir. Analizler Python ve Excel üzerinde geçekleştirilmiş olup, case study çıktılarına yer verilmiştir.

Keywords

Uçuş, Yolcular, Analiz, yolcu modeli analizi

References

• [1] Tsai C.W., Lai C.F., Chao H.C., et al., Big data analytics: A survey, Journal of Big Data, 2(21) (2015) 1-32, https://doi.org/10.1186/s40537-015-0030-3.
• [2] Chen L., Li J., Study on passenger arrival patterns at Beijing Capital International Airport, Journal of Transportation Systems Engineering and Information Technology, 10(3) (2010) 190-195.
• [3] Wang X., Zhang Y., Analysis of passenger arrival patterns at Shanghai Pudong International Airport during holiday periods, Journal of Air Transport Management, 23 (2012) 41-45.
• [4] Wang X., Wu J., Wang J., Prediction of airport passenger arrival patterns using machine learning algorithms, Journal of Air Transport Management, 61 (2017) 87-96.
• [5] Yang Y., Li X., Wang X., Analysis of passenger arrival patterns at airports, Journal of Air Transport Management, 93 (2021) 101700.
• [6] Kim J., Kim Y., Lee Y., The impact of passenger arrival patterns on airport resource utilization, Transportation Research Part A: Policy and Practice, 144 (2021) 455-466.
• [7] Chen X., Wang X., Li, X., Analysis of passenger arrival patterns at airport security checkpoints, Journal of Air Transport Management, 98 (2022) 101743.
• [8] Government of Canada investing in research collaborations between colleges and entrepreneurs, (2019), MENA Report.
• [9] Tukey W.J., The future of data analysis, Annals of Mathematical Statistics, 33(1) (1962) 1-67.
• [10] Tukey W. J., Exploratory Data Analysis, (1977), Addison-Wesley.
• [11] Kürzl H., Exploratory data analysis: Recent advances for the interpretation of geochemical data, Journal of Geochemical Exploration, 30(1-3) (1988) 309–322, doi:10.1016/0375-6742(88)90066-0.
• [12] Kolassa S., EDA, Descriptive statistics, Visual Analytics, (2017).StackExchange.https://stats.stackexchange.com/q/309837
• [13] Gursoy D., Chi X., Lu L., & Lu Y., Passenger arrival patterns and their impact on airline scheduling, Transportation Research Part A: Policy and Practice, 38(3) (2004) 193-212.
• [14] Sweeney J., Tran V., Improving Protection Against Cybersecurity Attacks of Emergency Dispatch Centers, In International Conference on Cyber Warfare and Security, (2022) 315.
• [15] Hu Y., Wang X., Wang J., Analysis of passenger arrival patterns at Hong Kong International Airport using queuing theory, Journal of Air Transport Management, 41 (2015) 38-43.
• [16] Song H., Kim J., Lee H., The impact of airline alliances and codeshare arrangements on airport passenger arrival patterns, Transportation Research Part A: Policy and Practice, 102 (2017) 365-376.
• [17] Li Y., Zhang Y., Li J., Big data analytics of passenger arrival patterns at Beijing Capital International Airport, Journal of Air Transport Management, 89 (2020) 101935.
• [18] Corum K., Garofalo J., Analyzing 3D-printed artifacts to develop mathematical modeling strategies, Technology and Engineering Teacher, 78(2) (2018) 14.
• [19] Chen W., Li Z., Ye Y., Spatial analysis and machine learning for predicting airport passenger arrival patterns: A case study of San Francisco International Airport, Journal of Transport Geography, 91 (2021) 102947.
• [20] Zhou Z., Szymanski B., Gao, J., Modeling competitive evolution of multiple languages, PLoS One, 15(5) (2020) e0232888.
• [21] McKinney W., Data Structures for Statistical Computing in Python, pandas.DataFrame.corr - pandas 1.5.3 documentation. Retrieved February 27, 2023, from https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.corr.html
• [22] Sarmento D. (n.d.), Chapter 22: Correlation Types and When to Use Them, Retrieved February 27, 2023, from https://ademos.people.uic.edu/Chapter22.html
• [23] Chen J., Wang Y., Yu Q., Yu M., Analysis of passenger arrival patterns at Hong Kong International Airport using data mining and visualization techniques, Transportation Research Part C: Emerging Technologies, 100 (2019) 133-148.
• [24] FlyData. (n.d.). FlyData project. Retrieved from https://smartcities.berkeley.edu/projects/flydata/