Safety - SES RP3

PRB monitoring

▪ Safety levels overall remained at pre-COVID-19 pandemic levels.

▪ 16 ANSPs already achieved the RP3 targets for the effectiveness of safety management for all Management Objectives (two years before the end of RP3). The remaining 20 ANSPs are expected to meet the targets by the end of RP3.

▪ The rate of accidents and incidents remained in line with the trend over the past 10 years, continuously decreasing.

Effectiveness of Safety Management (EoSM) (KPI#1)

Number of ANSPs on Target

16 out of 36 ANSPs already achieved the RP3 targets in 2022, reaching a minimum of maturity level D in safety risk management and a minimum of maturity level C in all other Management Objectives.

Actual versus planned

ANSPs are performing better than planned within the safety risk management area of their performance plans: 11 ANSPs planned to achieve the target level D, whereas 18 ANSPs already achieved the level of the RP3 target in 2022.

For other Management Objectives, 30 ANSPs had planned to already achieve the RP3 target level in 2022, but only 27 ANSPs managed to do so. Overall, the majority (if not all) ANSPs are foreseen to reach the target for RP3 by 2024.

ANSPs achieving the targets and Score

• 18 out of 36 ANSPs achieved the 2024 RP3 target level D for safety risk management.
• 27 out of 36 ANSPs achieved the 2024 RP3 target level C for all other MOs (the four management objectives other than safety risk management).
• 16 out of 36 ANSPs achieved the 2024 EoSM targets for RP3 in full.

The average EoSM score achieved by all ANSPs is 89. The minimum score achieved by an individual ANSPs is 69, while the maximum EoSM score is 100, which is already achieved by seven ANSPs.

Number of ANSPs per Management Objective

18 ANSPs did not reach the target level D for the safety risk management objective, five ANSPs did not reach the safety culture management objective, while four ANSPs did not reach the safety policy & objectives, assurance promotion management objectives, respectively.

Interdependency

Most ANSPs are at maturity levels C and D for interdependencies management objective. There is room for improvement since two ANSPs are at level B. This is particularly important after the pandemic when the pressures to trade-off resources towards other business objectives of the organisation are intensified due to loss of traffic and revenues in such period.

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 416,455 0 NA 1 Gran Canaria 123,713 10 8.08 1 Lublin 3,549 4 112.71
2 Paris-Charles-de-Gaulle 408,647 2 0.49 2 Málaga 146,633 10 6.82 2 Zielona Gora - Babimost 1,041 1 96.06
3 Frankfurt 386,542 1 0.26 3 Barcelona 283,493 7 2.47 3 Olsztyn-Mazury 1,414 1 70.72
4 Madrid - Barajas 351,963 5 1.42 4 Warsaw 144,737 6 4.15 4 Lodz - Lublinek 3,542 2 56.47
5 Munich 288,141 0 0.00 5 Stockholm - Arlanda 170,284 5 2.94 5 Bydgoszcz 3,451 1 28.98
6 Barcelona 283,493 7 2.47 6 Madrid - Barajas 351,963 5 1.42 6 Wroclaw - Strachowice 26,388 3 11.37
7 Palma de Mallorca 220,908 3 1.36 7 Prague 99,006 5 5.05 7 Istres-Le Tubé 19,750 2 10.13
8 Dublin 220,865 2 0.91 8 Stavanger 69,156 4 5.78 8 Poznan - Lawica 22,684 2 8.82
9 Rome - Fiumicino 212,315 0 0.00 9 Lublin 3,549 4 112.71 9 Gran Canaria 123,713 10 8.08
10 Zürich 211,125 0 0.00 10 Nice-Côte d’Azur 154,584 4 2.59 10 Rzeszow - Jasionka 14,340 1 6.97

RI rate

Compared with 2021, in 2022, the rate of runway incursions decreased by almost 40%, while the rate of separation minima infringements remained stable

RI 2021-2022

The increase in traffic levels in 2022 has not resulted in an increase of the indicators of all types of RIs. The rate of RIs of all types decreased by 31.1% while the rate of RIs with ANS contribution increased by 5.8%. Similarly, while the number of RIs decreased (by 14.4%), the number of RIs with ANS contribution increased by 43.8%. It seems that at Member State level, the management of risks associated to RIs of all types showed improvement. Such trend is not observed in the indicators of RIs with ANS contribution. This should be deeper investigated by ANSPs.

RI with Safety Impact by State

The highest rate occurred in Sweden (26.9), followed by Spain (9.9), although Sweden declared it was not able to differentiate occurrences that had a safety impact and therefore reported all types of RIs regardless of the associated safety risk. This means that the number of RIs in Sweden is higher, and so the rate is not comparable to other Member States.

Three Member States (Sweden, Spain, Greece) reported the RIs at all airports within their territory, not exclusively those included in their Performance Plans, which may influence on why two of these Member States show the highest rates in the graph.

RI with Safety Impact by Airport

Out of 146 airports included in the performance plans, 57 airports reported RIs that had any ATS/CNS contribution. The 89 airports included in the performance and charging scheme that reported no RIs are not shown in the figure. The majority of airports reported one or two RIs. This makes the rate of runway incursions at airports with a low number of movements very susceptible to variations in the number of occurrences. For example, within the top ten airports with the highest rates of runway incursions, nine had fewer than 30,000 airport movements.

Airports with significant higher traffic figures with highest rates of RIs are GCLP (Gran Canaria) with 8.1 RI per 100.000 movements, followed by LEMG (Málaga) and ENZV (Stavanger) with 6.8 and 5.8 per 100.000 movements, respectively. LEMG uses an automatic recording tool to identify RIs, which may have an effect on the number of reported events. ENAIRE, SKYWAY and Avinor who are responsible for the provision of ANS services at GCLP, LEMG, and ENZV, respectively, have a good record in the EoSM questionnaire in managing safety risks (maturity C or D). Nevertheless, these providers should consider looking into the reasons contributing to these rates and take appropriate mitigating actions, if necessary.

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 155,355 202,666 317,434 NA NA 7 9 14 NA NA 4.5 4.4 4.4 NA NA NA -2% -1% NA NA
2 Belgium 55,762 134,413 97,089 NA NA 1 5 6 NA NA 1.8 3.7 6.2 NA NA NA +108% +66% NA NA
3 Bulgaria 127,863 174,114 290,422 NA NA 1 0 0 NA NA 0.8 0.0 0.0 NA NA NA -100% 0% NA NA
4 Croatia 106,693 155,957 249,018 NA NA 0 0 3 NA NA 0.0 0.0 1.2 NA NA NA 0% 0% NA NA
5 Cyprus 78,614 116,453 167,670 NA NA 0 3 6 NA NA 0.0 2.6 3.6 NA NA NA 0% +39% NA NA
6 Czech Republic 113,261 135,047 178,983 NA NA 7 11 8 NA NA 6.2 8.2 4.5 NA NA NA +32% -45% NA NA
7 Denmark 98,936 99,279 183,043 NA NA 0 2 0 NA NA 0.0 2.0 0.0 NA NA NA 0% -100% NA NA
8 Estonia 33,558 37,464 48,360 NA NA 4 5 3 NA NA 11.9 13.3 6.2 NA NA NA +12% -54% NA NA
9 Finland 57,321 62,275 88,850 NA NA 0 3 8 NA NA 0.0 4.8 9.0 NA NA NA 0% +87% NA NA
10 France 1,051,941 1,415,222 2,178,853 NA NA 133 272 304 NA NA 12.6 19.2 13.9 NA NA NA +52% -27% NA NA
11 Germany 700,899 952,606 1,263,985 NA NA 6 8 22 NA NA 0.9 0.8 1.7 NA NA NA -2% +107% NA NA
12 Greece 276,276 418,381 650,415 NA NA 24 31 42 NA NA 8.7 7.4 6.5 NA NA NA -15% -13% NA NA
13 Hungary 116,008 149,648 259,939 NA NA 2 7 6 NA NA 1.7 4.7 2.3 NA NA NA +172% -51% NA NA
14 Ireland 131,294 105,105 288,261 NA NA 2 7 8 NA NA 1.5 6.7 2.8 NA NA NA +338% -58% NA NA
15 Italy 494,359 747,998 1,141,849 NA NA 26 33 81 NA NA 5.3 4.4 7.1 NA NA NA -16% +61% NA NA
16 Latvia 39,170 46,440 52,501 NA NA 1 0 1 NA NA 2.5 0.0 1.9 NA NA NA -100% 0% NA NA
17 Lithuania 36,493 47,794 47,286 NA NA 1 0 2 NA NA 2.7 0.0 4.2 NA NA NA -100% 0% NA NA
18 Luxembourg 5,067 11,425 17,665 NA NA 0 3 4 NA NA 0.0 26.3 22.6 NA NA NA 0% -14% NA NA
19 MUAC 289,985 311,843 545,651 NA NA 5 0 8 NA NA 1.7 0.0 1.5 NA NA NA -100% 0% NA NA
20 Malta 40,016 44,905 62,700 NA NA 0 1 0 NA NA 0.0 2.2 0.0 NA NA NA 0% -100% NA NA
21 Netherlands 88,456 101,649 155,388 NA NA 31 47 33 NA NA 35.0 46.2 21.2 NA NA NA +32% -54% NA NA
22 Norway 235,547 257,160 646,054 NA NA 27 14 84 NA NA 11.5 5.4 13.0 NA NA NA -53% +139% NA NA
23 Poland 221,029 278,330 361,376 NA NA 8 15 39 NA NA 3.6 5.4 10.8 NA NA NA +49% +100% NA NA
24 Portugal 175,009 215,958 406,816 NA NA 10 13 30 NA NA 5.7 6.0 7.4 NA NA NA +5% +22% NA NA
25 Romania 171,847 247,561 384,582 NA NA 3 4 12 NA NA 1.8 1.6 3.1 NA NA NA -7% +93% NA NA
26 Slovakia 41,055 54,376 86,171 NA NA 0 2 0 NA NA 0.0 3.7 0.0 NA NA NA 0% -100% NA NA
27 Slovenia 28,029 40,145 61,705 NA NA 0 2 4 NA NA 0.0 5.0 6.5 NA NA NA 0% +30% NA NA
28 Spain 741,278 954,783 1,632,981 NA NA 25 59 89 NA NA 3.4 6.2 5.4 NA NA NA +83% -12% NA NA
29 Sweden 199,288 218,597 333,262 NA NA 2 19 31 NA NA 1.0 8.7 9.3 NA NA NA +769% +7% NA NA
30 Switzerland 150,242 137,471 318,606 NA NA 0 2 5 NA NA 0.0 1.4 1.6 NA NA NA 0% +8% NA NA

SMI

Compared with 2021, in 2022, the rate of separation minima infringements remained stable.

SMI 2021-2022

The increase in traffic levels in 2022 has naturally resulted in an increase of SMI occurrences of all types, namely regardless the factors involved, including those with ANS contribution, by 62.4% and 55.7%, respectively. However, the rates have experienced a small decrease of 0.9% and 2.5%, respectively.

SMI by State

The highest rate occurred in Luxemburg (22.6). Because the number of controlled hours is very small in its airspace, any small increases in absolute number of occurrences results in very significant changes in the rate, as it has been the case in 2022. LU has reported training of new ATCOs completed together with a new simulator. ANA (Luxemburgish ANSP) will monitor the ATCO refresher training to make special emphasis on SMI early detection, avoidance and recovery to reduce current rate.

On the opposite side, three Member States (Denmark, Malta, and Slovakia) reported no SMIs within their airspace.

Quality of occurences reporting

Occurrence reporting quality

SPI1a, SPI1b, SPI1c, SPI1d, and SPI2 were computed using information gathered from the submitted PMRs. This data was taken directly from what Member States reported in their PMRs without further verification against the occurrences reported in the European Central Repository (ECR), as foreseen by the RP3 safety supporting material.

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. A significant part of occurrences extracted from ECR did not contain information on severity and risk, as required to compute the SPIs. Member States had to extract the occurrences from their own national databases with no further involvement from 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 using the severity classification using the Risk Analysis Tool (RAT).8 This information was barely found encoded in the ECR’s occurrences.

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.