Analyzing the New Global Reporting Format from the Pilot Perspective

Abstract

Global Reporting Format (GRF) for runway surface conditions is an important step in improving aviation safety by providing standardized and consistent information. The aim of the study was to evaluate the effectiveness and implementation of the GRF among pilots. Qualitative and quantitative methods were used to comprehensively address all aspects of the study. The sample consisted of 266 pilots. Findings showed that the majority of pilots are aware of the GRF and value its benefits, such as consistency, reliability, and standardized terminology, despite it being a new method. Pilots highlighted the role of the GRF in improving communication and decision-making for take-off and landing. However, the study also identified challenges, including occasional inaccuracies in reporting, the need for real-time updates, the length of ATIS reports, and inconsistencies in application across airports in different regions. These issues highlight the human factor and the need to develop the GRF. The study makes a unique contribution by highlighting both the practical benefits and the challenges of the GRF from the perspective of the pilots. It is recommended that future research include a more diverse sample of pilots from all regions and that technical studies be undertaken to compare runway surface conditions with aircraft performance under the GRF. This will provide a more complete understanding of the effectiveness of the GRF and identify areas for further improvement.

Keywords

• Aviation safety
• Global reporting format
• Pilot
• Runway excursions
• Runway safety

References

1. Airbus, 2024. Statistical Analysis of Commercial Aviation Accidents 1958 – 2023. Available online at: https://accidentstats.airbus.com/wp-content/uploads/2024/02/20230873_A-Statistical-analysis-of-commercial-aviation-accidents-2024-version.pdf (Accessed on 11 October 2024).
2. Blondel, B., Zein, A., Ghosn, N., Du Mazaubrun, C., & Bréart, G., 2006. Collecting population‐based perinatal data efficiently: the example of the Lebanese National Perinatal Survey. Paediatric and perinatal epidemiology, 20(5), 416-424. https://doi.org/10.1111/j.1365-3016.2006.00732.x
3. Brassard, J. D., Beaulieu, A., Tremblay, M. M., & Momen, G., 2022. Assessment of Runway Surface Conditions by British Pendulum Testing under the Global Reporting Format Winter Conditions. Applied Sciences, 12(19), 9646. https://doi.org/10.3390/app12199646
4. Brassard, J. D., Laforte, C., Tremblay, M. M., & Volat, C., 2019. Runway deicing product anti/deicing performance assessment: review and future directions. SAE Technical Paper 2019-01-1974. https://doi.org/10.4271/2019-01-1974
5. Büyüköztürk, Ş., 2020. Data Analysis Handbook for Social Sciences: Statistics, Research Design, SPSS Applications, and Interpretation (27th Edition). Ankara: Pegem Academy
6. Bylica, A., & Pashkevich, A., 2022. Introduction of Global Reporting Format: Summary of the First Winter Season in Poland. Sustainability, 15(1), 167. https://doi.org/10.3390/su15010167
7. Chang, Y. H., Yang, H. H., & Hsiao, Y. J., 2016. Human risk factors associated with pilots in runway excursions. Accident Analysis & Prevention, 94, 227-237. https://doi.org/10.1016/J.AAP.2016.06.007
8. Chen, X., Zhang, Q., Cheng, C., Zhou, X., & Yu, X., 2022. Accuracy Assessment of SRTM DEM, ASTER GDEM, AW3D30 DSM, and TanDEM-X 90 m DEM Based on Runway Elevation Data. In 2022 2nd International Conference on Big Data, Artificial Intelligence and Risk Management (ICBAR) (pp. 30-34). IEEE. https://doi.org/10.1109/icbar58199.2022.00013

9. Distefano, N., & Leonardi, S., 2019. Aircraft runway excursion features: a multiple correspondence analysis. Aircraft Engineering and Aerospace Technology, 91(1), 197-203. https://doi.org/10.1108/AEAT-11-2017-0244 European Union Aviation Safety Agency [EASA], 2024. Annual Safety Review (ASR) 2024. Available online at: https://www.easa.europa.eu/en/document-library/general-publications/annual-safety-review-2024 (Accessed on 10 July 2024).
10. Garcia, J. S., Jaedicke, C., Leng Lim, G., & Truong, D., 2023. Predicting the Severity of Runway Excursions from Aviation Safety Reports. Journal of Aerospace Information Systems, 1-10. https://doi.org/10.2514/1.I011145 George, D, & Mallery, P., 2003. SPSS for Windows step by step: A simple guide and reference. 11.0 update (4th ed.). Boston: Allyn & Bacon.
11. Hair, J.F., Black, W.C., Babin, B. J., & Anderson, R.E., 2019. Multivariate Data Analysis (8th Edition). Hampshire: Cengage Learning.
12. Hu, J., Zhao, K., Zheng, P., Mi, C., Liu, W., & Gong, H., 2022. Nondestructive testing of the airfield pavement structural condition based on the GPR and HWD. In Second International Conference on Testing Technology and Automation Engineering (TTAE 2022) (Vol. 12457, pp. 139-145). SPIE. https://doi.org/10.1117/12.2660552
13. International Air Transport Association [IATA], 2023. Annual Safety Report. Available online at: https://www.iata.org/en/publications/safety-report/interactive-safety-report/ (Accessed on 10 March 2024).
14. International Civil Aviation Organization [ICAO], 2018. Doc 10066, Procedures for Air Navigation Services - Aeronautical Information Management. ISBN 978-92-9258-597-6
15. International Civil Aviation Organization [ICAO], 2019. Cir 355, Assessment, Measurement and Reporting of Runway Surface Conditions, ISBN 978-92-9258-719-2
16. International Civil Aviation Organization [ICAO], 2020. Doc 10064, Aeroplane Performance Manual, ISBN 978-92-9265-279-1
17. International Civil Aviation Organization [ICAO], 2021. Implementation of Global Reporting Format for Runway Surface Conditions (GRF), Guidance based on management of change (MOC), Version 1.0
18. Karyawan, I., 2021. Analysis of the causes and prevention of runway excursions. Analysis of the causes and prevention of runway excursions. In Proceeding International Conference on Science (ICST) (Vol. 2, pp. 156-166).
19. Kelley, K., Clark, B., Brown, V., & Sitzia, J., 2003. Good practice in the conduct and reporting of survey research. International Journal for Quality in health care, 15(3), 261-266. https://doi.org/10.1093/intqhc/mzg031
20. Klein-Paste, A., 2018. Airplane braking friction on dry snow, wet snow or slush contaminated runways. Cold regions science and technology, 150, 70-74. https://doi.org/10.1016/j.coldregions.2017.02.004
21. Kornstaedt, L., & Lignee, R., 2010. Operational Landing Distances, A new standard for in-flight landing distance assessment. Safety, 10, 1–5.
22. Kornstaedt, L., 2019. GRF Methodology History and Development Process. GRF Workshop, Frankfurt Maeng, S. K., Jung, Y. S., Choi, J. K., & Kwon, B. H., 2012. Development of runway incursion risk assessment checklist. Journal of the Korean Society for Aviation and Aeronautics, 20(1), 46-54. https://doi.org/10.12985/KSAA.2012.20.1.044
23. Niu, Y., Jiang, X., Meng, F., Wang, R., Ju, G., Zhang, S., & Meng, Z., 2021. Techniques and methods for runway friction measurement: A review of state of the art. IEEE Transactions on Instrumentation and Measurement, 70, 1-17. https://doi.org/10.1109/TIM.2021.3092062
24. Pasindu, H. R., Fwa, T. F., & Ong, G. P., 2016. Analytical evaluation of aircraft operational risks from runway rutting. International Journal of Pavement Engineering, 17(9), 810-817. https://doi.org/10.1080/10298436.2015.1019501
25. Pestana, G., Reis, P., & da Silva, T. R., 2021. Smart Surveillance of Runway Conditions. In Intelligent Transport Systems, From Research and Development to the Market Uptake: 4th EAI International Conference, INTSYS 2020, Virtual Event, December 3, 2020, Proceedings 4 (pp. 252-270). Springer International Publishing.
26. Procházka, J., & Kameník, M., 2013. Contaminated Runway Operations-Adverse weather. MAD-Magazine of Aviation Development, 1(4), 3-7. https://doi.org/10.14311/MAD.2013.04.01
27. Sama, D., Gnabahou, D. A., Ouattara, F., Zidouemba, M., Diassibo, O., & Sandwidi, S. A., 2022. Global Reporting Format (GRF) Application Automation for Runway Surface Conditions in West Africa. Advances in Aerospace Science and Technology, 7(3), 135-145. https://doi.org/10.4236/aast.2022.73009
28. Schoenherr, T., Ellram, L. M., & Tate, W. L., 2015. A note on the use of survey research firms to enable empirical data collection. Journal of Business Logistics, 36(3), 288-300. https://doi.org/10.1111/jbl.12092
29. Tabachnick, B.G., & Fidell, L.S., 2019. Using Multivariate Statistics (Seventh Edition). New Jersey: Pearson.
30. Tuncal, A., Uslu, S., & Dursun, E., 2021. A Milestone to Enhance Runway Safety: The New Global Reporting Format. Revista de Investigaciones Universidad del Quindío, 33(1), 168-178. https://doi.org/10.33975/riuq.vol33n1.551
31. Van Eekeren, R., Wright, S., & Čokorilo, O., 2018. Early cost safety analysis of runway events. International Journal for Traffic & Transport Engineering, 8(3), 261-270. http://dx.doi.org/10.7708/ijtte.2018.8(3).01
32. Vorobyeva, O., Bartok, J., Šišan, P., Nechaj, P., Gera, M., Kelemen, M., Polishchuk, V., & Gaál, L., 2020. Assessing the contribution of data mining methods to avoid aircraft run-off from the runway to increase the safety and reduce the negative environmental impacts. International Journal of Environmental Research and Public Health, 17(3), 796. https://doi.org/10.3390/ijerph17030796