Enhancing Intelligent Aviation Operations Using Process Mining Techniques

Document Type : Original Article

Authors

1 M.Sc., Grad., Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran.

2 Professor, Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran.

3 Assistant Professor, Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran.

4 B.Sc., Grad., Front-End Developer, Iran Internet Group (IIG), Snapp food, Tehran, Iran.

10.22034/tri.2021.119310

Abstract

Today, the aviation industry plays a significant role in relations between different countries of the world, demonstrating the economic and military power and extending essentials of a country. Improving the performance of the aviation industry by analyzing big data available using intelligent methods, increases the efficiency of this system. According to the previous studies, despite the importance of this industry and large amount of data, there has not been adequate attention using data-based methods in past studies in order to discover knowledge in this industry. The process mining is one of these intelligent methods that allows automatically obtaining information from event log data and analyzing system processes. This research focuses on the implementation of the process discovery method with control-flow and time pesrpective using Prom software to investigate intelligent prediction of occurrence time of key events in the field of aviation system. In this research, after extracting the initial flight data from Zagros Airlines and converting them to the event log, the flights event log is given to the Prom software and are extracted the transition system model and time prediction of key flight events. Some innovations of this research include using time prediction method of the process mining approach on the actual data, time prediction and analyse the time behavior of the key events of future flights dynamically based on the past behavior of each flight. By using the results of this study, waiting time which prevents from passengers’ congestion is reduced and because of high satisfaction of passengers with up-to-date flights information, demand for aviation system is increased. It also helps to create intelligent scheduling in the aviation system and improves the scheduling of airport facilities.

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Main Subjects

References

-فریبرزی عراقی، ف. شهپر افراشته، الف. و سالاری جوینی، الف.، (1381)، "مقدمه­ای بر سیستم‌های حمل‌ونقل هوشمند"، گزارش کمیته فناوری اطلاعات (IT) شورای اصلاحات وزارت راه و ترابری، شماره 4.
 
-ﻋﻄﺎﺋﯽﻓﺮ، الف. و اسماعیلی، میم.، (1394)، "پیاده‌سازی اتوماسیون پیش‌بینی وضعیت ترافیک جاده­ای با استفاده از تکنیک­های داده‌کاوی"، مقاله منتشر شده در پانزدهمین کنفرانس بین­المللی مهندسی حمل‌ونقل و ترافیک، تهران، معاونت و سازمان حمل و نقل ترافیک، 11-12 اسفند.
 
-تقی­زاده نوعی، میم.، (1390)، "توسعه چارچوبی برای بهبود فرآیند مراقبت از بیماران بیمارستان بر پایه فرآیندکاوی"،
پایان­نامه کارشناسی ارشد، استاد راهنما: محمد مهدی سپهری، تهران: دانشکده فنی مهندسی، گروه فناوری اطلاعات، دانشگاه تربیت مدرس.
 
- احمدی، سین.، (1395)، "بررسی تحلیل فرآیندهای
کسب­و­کار بر پایه رویکرد فرآیندکاوی (فرآیند اجرای فوندانسیون در نیروگاه سیکل ترکیبی بهبهان)"، پایان­نامه کارشناسی ارشد، استاد راهنما: سید یعقوب حسینی، بوشهر: دانشکده ادبیات و علوم انسانی، گروه مدیریت بازرگانی، دانشگاه خلیج فارس.
- کاظمی، ز.، (1394)، "به کارگیری فرآیندکاوی در جهت بهبود فرآیندهای مدیریت دانش در مراکز تماس (مطالعه موردی مرکز تماس 122 سازمان آب و فاضلاب استان تهران)"، پایان­نامه کارشناسی ارشد، استاد راهنما: محمد اقدسی، تهران: دانشکده فنی و مهندسی، گروه مهندسی
صنایع- گرایش مدیریت سیستم و بهره وری، دانشگاه تربیت مدرس.
 
- ون در آلاست، و. (2011)، "فرآیندکاوی: کشف، تطبیق و بهبود فرآیندهای کسب و کار (ترجمه سید حسین سیادت و راضیه همتی گشتاسب)"، 1394، تهران، دانشگاه شهید بهشتی، مرکز چاپ و انتشارات.
 
- سلیمی­فرد، خ. حسینی، سین ی. و مرادی، میم صاد.، (1393)، "بهبود ﻓﺮاﯾﻨﺪﻫﺎی ﺑﺨﺶ اورژاﻧﺲ ﺑﯿﻤﺎرﺳﺘﺎن ﺑﺎ اﺳﺘﻔﺎده از ﺷﺒﯿﻪﺳﺎزی راﯾﺎﻧﻪای"، فصلنامه مدیریت سلامت، جلد 17، شماره 55، ص. 62-72.
 
-An, S. H., Lee, B. H., & Shin, D. R., (2011), "A survey of intelligent transportation systems", In 2011 Third International Conference on Computational Intelligence, Communication Systems and Networks, IEEE Computer Society Washington, July 26 - 28, pp. 332-337.
-Yongjun, Z., Xueli, Z., & Shuxian, Z., (2012), "Intelligent transportation system based on Internet of Things", In World Automation Congress 2012, 24-28 June, Puerto Vallarta, Mexico, pp. 1-3.
-Lin, H. E., Zito, R., & Taylor, M., (2005), "A review of travel-time prediction in transport and logistics", In Proceedings of the Eastern Asia Society for transportation studies, Vol. 5, pp. 1433-144.
-Zhang, Y., & Haghani, A., (2015), "A gradient boosting method to improve travel time prediction", Transportation Research Part C: Emerging Technologies, Vol. 58,
pp. 308-324.
-Esmaeili, L., (2015), "Rural Intelligent Public Transportation System Design: Applying the Design for Re-Engineering of Transportation eCommerce System in Iran", International Journal of Information Technologies and Systems Approach (IJITSA), Vol. 8, No. 1,
pp. 1-27.
-Chen, C. H., (2018), "An arrival time prediction method for bus system", IEEE Internet of Things Journal, Vol. 5, No. 5,
pp. 4231-4232.
-Skorupski, J., & Florowski, A., (2016), "Method for evaluating the landing aircraft sequence under disturbed conditions with the use of Petri nets", The Aeronautical Journal, Vol.  120, No. 1227, pp. 819-844.
-Čelan, M., & Lep, M., (2018), "Bus-arrival time prediction using bus network data model and time periods", Future Generation Computer Systems.
-Olutayo, V. A., & Eludire, A. A., (2014), "Traffic accident analysis using decision trees and neural networks", International Journal of Information Technology and Computer Science, pp. 22-28.
-Anand, S., Padmanabham, P., Govardhan, A., & Kulkarni, R. H., (2018), "An extensive review on data mining methods and clustering models for intelligent transportation system", Journal of Intelligent Systems, Vol. 27, No. 2, pp. 263-273.
-Ma, X., & Chen, X., (2019), "Public Transportation Big Data Mining and Analysis", In Data-Driven Solutions to Transportation Problems, Elsevier,
pp. 175-200.
-Mathur, A., (2002), "Data mining of aviation data for advancing health managemet", In Component and Systems Diagnostics, Prognostics, and Health Management II, International Society for Optics and Photonics, Vol. 4733, pp. 61-71.
-Tiwari, A., Turner, C. J., & Majeed, B., (2008), "A review of business process mining: state-of-the-art and future trends", Business Process Management Journal, Vol. 14, No. 1, pp. 5-22.
-Bogarín, A., Cerezo, R., & Romero, C., (2018), "A survey on educational process mining", Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery,
Vol. 8, No. 1, e1230.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
-Tax, N., Lu, X., Sidorova, N., Fahland, D., & van der Aalst, W. M., (2018), "The imprecisions of precision measures in process mining", Information Processing Letters,
Vol. 135, pp. 1-8.
-Partington, A., Wynn, M., Suriadi, S., Ouyang, C., & Karnon, J., (2015), "Process mining for clinical processes: a comparative analysis of four Australian hospitals", ACM Transactions on Management Information Systems (TMIS), Vol. 5, No. 19.
-Van der Aalst, W. M., Schonenberg, M. H., & Song, M., (2011), "Time prediction based on process mining", Information systems, Vol. 36, No. 2, pp. 450-475.  

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