Safety - SES RP3
PRB monitoring
▪ Safety remains the highest priority and ANSPs handled safety well since the outbreak of the pandemic. Safety levels overall remained as before COVID-19.
▪ 13 ANSPs achieved the RP3 targets for the Effectiveness of Safety Management for all management objectives (based on the new revised questionnaire used in 2020).
▪ In view of the lower traffic levels, incidents and accidents related to the provision of air navigation services decreased, and the rate of occurrences remained stable compared to 2019.
Effectiveness of Safety Management (EoSM) (KPI#1)
Safety occurrences
Rate of runway incursions (RIs) (PI#1)
| Rate of RI per 100,000 airport movements | ||||||||||||||
|
TOP 10 APTs in terms of movements
|
TOP 10 APTs in terms of number of RIs
|
TOP 10 APTs in terms of rate of RIs
|
||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| # | Airport name | APT mvts. | Number of RI | Rate RI per 100,000 mvts. | # | Airport name | APT mvts. | Number of RI | Rate RI per 100,000 mvts. | # | Airport name | APT mvts. | Number of RI | Rate RI per 100,000 mvts. |
| 1 | Amsterdam - Schiphol | 241,400 | 0 | 0.00 | 1 | Málaga | 61,649 | 12 | 19.47 | 1 | Bydgoszcz | 2,109 | 2 | 94.83 |
| 2 | Paris-Charles-de-Gaulle | 220,880 | 3 | 1.36 | 2 | Oslo - Gardermoe | 125,428 | 4 | 3.19 | 2 | Erfurt | 9,731 | 0 | 20.55 |
| 3 | Frankfurt | 218,605 | 0 | 2.29 | 3 | Paris-Charles-de-Gaulle | 220,880 | 3 | 1.36 | 3 | Málaga | 61,649 | 12 | 19.47 |
| 4 | Madrid - Barajas | 165,662 | 1 | 0.60 | 4 | Rouen | 21,670 | 3 | 13.84 | 4 | Berlin - Brandenburg | 43,167 | 1 | 16.22 |
| 5 | Munich | 150,740 | 0 | 2.65 | 5 | Trondheim | 36,869 | 2 | 5.42 | 5 | Rouen | 21,670 | 3 | 13.84 |
| 6 | Oslo - Gardermoe | 125,428 | 4 | 3.19 | 6 | Bydgoszcz | 2,109 | 2 | 94.83 | 6 | Saarbruecken | 8,177 | 0 | 12.23 |
| 7 | Barcelona | 122,670 | 0 | 0.00 | 7 | Krakow - Balice | 27,087 | 2 | 7.38 | 7 | Lille-Lesquin | 20,608 | 2 | 9.70 |
| 8 | Athinai-Eleftherios Venizelos | 109,145 | 0 | 0.00 | 8 | Palma de Mallorca | 76,978 | 2 | 2.60 | 8 | Poznan - Lawica | 10,833 | 1 | 9.23 |
| 9 | Vienna | 108,129 | 1 | 0.92 | 9 | Perpignan-Rivesaltes | 46,880 | 2 | 4.27 | 9 | Bremen | 26,338 | NA | 7.59 |
| 10 | Zürich | 105,403 | 2 | 1.90 | 10 | Lille-Lesquin | 20,608 | 2 | 9.70 | 10 | Krakow - Balice | 27,087 | 2 | 7.38 |
Rate of separation minima infringements (SMIs) (PI#2)
| Rate of SMI with ANS contribution per 100,000 flight hours | |||||||||||||||||||||
| # | State |
Flight hours
|
Number of SMIs
|
Rate of SMI per 100,000 flight hours
|
% variation in rate of SMIs
|
||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2020 | 2021 | 2022 | 2023 | 2024 | 2020 | 2021 | 2022 | 2023 | 2024 | 2020 | 2021 | 2022 | 2023 | 2024 | 2020 | 2021 | 2022 | 2023 | 2024 | ||
| 1 | Austria | 155,355 | NA | NA | NA | NA | 7 | NA | NA | NA | NA | 4.5 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 2 | Belgium | 55,762 | NA | NA | NA | NA | 1 | NA | NA | NA | NA | 1.8 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 3 | Bulgaria | 127,863 | NA | NA | NA | NA | 1 | NA | NA | NA | NA | 0.8 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 4 | Croatia | 106,693 | NA | NA | NA | NA | 0 | NA | NA | NA | NA | 0.0 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 5 | Cyprus | 78,614 | NA | NA | NA | NA | 0 | NA | NA | NA | NA | 0.0 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 6 | Czech Republic | 113,261 | NA | NA | NA | NA | 7 | NA | NA | NA | NA | 6.2 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 7 | Denmark | 98,936 | NA | NA | NA | NA | 0 | NA | NA | NA | NA | 0.0 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 8 | Estonia | 33,558 | NA | NA | NA | NA | 4 | NA | NA | NA | NA | 11.9 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 9 | Finland | 57,321 | NA | NA | NA | NA | 0 | NA | NA | NA | NA | 0.0 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 10 | France | 1,051,941 | NA | NA | NA | NA | 133 | NA | NA | NA | NA | 12.6 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 11 | Germany | 700,899 | NA | NA | NA | NA | 6 | NA | NA | NA | NA | 0.9 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 12 | Greece | 276,276 | NA | NA | NA | NA | 24 | NA | NA | NA | NA | 8.7 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 13 | Hungary | 116,008 | NA | NA | NA | NA | 2 | NA | NA | NA | NA | 1.7 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 14 | Ireland | 131,294 | NA | NA | NA | NA | 2 | NA | NA | NA | NA | 1.5 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 15 | Italy | 494,359 | NA | NA | NA | NA | 26 | NA | NA | NA | NA | 5.3 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 16 | Latvia | 39,170 | NA | NA | NA | NA | 1 | NA | NA | NA | NA | 2.5 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 17 | Lithuania | 36,493 | NA | NA | NA | NA | 1 | NA | NA | NA | NA | 2.7 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 18 | Luxembourg | 5,067 | NA | NA | NA | NA | 0 | NA | NA | NA | NA | 0.0 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 19 | MUAC | 289,985 | NA | NA | NA | NA | 5 | NA | NA | NA | NA | 1.7 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 20 | Malta | 40,016 | NA | NA | NA | NA | 0 | NA | NA | NA | NA | 0.0 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 21 | Netherlands | 88,456 | NA | NA | NA | NA | 31 | NA | NA | NA | NA | 35.0 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 22 | Norway | 235,547 | NA | NA | NA | NA | 27 | NA | NA | NA | NA | 11.5 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 23 | Poland | 221,029 | NA | NA | NA | NA | 8 | NA | NA | NA | NA | 3.6 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 24 | Portugal | 175,009 | NA | NA | NA | NA | 10 | NA | NA | NA | NA | 5.7 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 25 | Romania | 171,847 | NA | NA | NA | NA | 3 | NA | NA | NA | NA | 1.8 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 26 | Slovakia | 41,055 | NA | NA | NA | NA | 0 | NA | NA | NA | NA | 0.0 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 27 | Slovenia | 28,029 | NA | NA | NA | NA | 0 | NA | NA | NA | NA | 0.0 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 28 | Spain | 741,278 | NA | NA | NA | NA | 25 | NA | NA | NA | NA | 3.4 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 29 | Sweden | 199,288 | NA | NA | NA | NA | 2 | NA | NA | NA | NA | 1.0 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 30 | Switzerland | 150,242 | NA | NA | NA | NA | 0 | NA | NA | NA | NA | 0.0 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
Quality of occurences reporting
Occurrence reporting quality
For the calculation of the indicators related to SMIs and RIs (SPI1a, SPI1b, SPI1c, and SPI1d), RP3 safety supporting material requires that occurrences data reported in the ECR under Commission Regulation (EU) No 376/2014 is used. However, so far in RP3, EASA has not been able to extract data from the ECR containing all needed information to compute the SPIs. This is because of the overall poor quality of the data uploaded to the ECR: A significant part of occurrences extracted from ECR did not contain information on severity and risk, as required to compute the SPIs, and in many cases basic information was missing. Member States had to extract the occurrences from their own national databases with no further involvement from or verification by EASA.
For the calculation of the indicators related to SMIs and RIs (SPI1a, SPI1b, SPI1c, and SPI1d), the occurrences that should be used in the computation of the different rates are only those that have a “safety impact”. Whether an occurrence has a safety impact or not should be determined by NSAs using the common European Risk Classification Scheme (ERCS), and by ANSPs through the severity classification using the Risk Analysis Tool (RAT). This information was barely found encoded in the ECR’s occurrences. While there has been some improvement, it is not such that the values may be calculated using ECR data as planned.
It is likely that some have not applied the ERCS and RAT resulting in greater subjectivity in ANSP and NSA interpretations of what constitutes an occurrence that had a safety impact. Nevertheless, this does not invalidate the analysis, but it should be taken into consideration when interpreting the data.