In this report, Europe, i.e. the European airspace, is defined as the area where the 41 EUROCONTROL member states provide air navigation services, excluding the oceanic areas and the Canary islands (c.f. Figure fig-EUR-airspace). In 2016, EUROCONTROL signed a comprehensive agreement with Israel and Morocco. Both comprehensive agreement States will be successively fully integrated into the working structures of EUROCONTROL, including performance monitoring. Within this report, these states are included in the reported network traffic volumes.
+In this report, Europe, i.e. the European airspace, is defined as the area where the 41 EUROCONTROL member states provide air navigation services, excluding the oceanic areas and the Canary islands (c.f. Figure 1.2). In 2016, EUROCONTROL signed a comprehensive agreement with Israel and Morocco. Both comprehensive agreement States will be successively fully integrated into the working structures of EUROCONTROL, including performance monitoring. Within this report, these states are included in the reported network traffic volumes.
EUROCONTROL is an inter-governmental organisation working towards a highly harmonised European air traffic management system. In general, air traffic services are provided by air navigation service providers entrusted by the different EUROCONTROL member states. Dependent on the local and national regimes, there is a mix of civil and military service providers, and integrated service provision.
The Maastricht Upper Area Control Center is operated by EUROCONTROL on behalf of 4 States (Netherlands, Belgium, Luxemburg, and Germany). It is the only multi-national cross-border air traffic unit in Europe at the time being. Given the European context and airspace structure, the European area comprises 37 ANSPs with 62 en-route centres and 16 stand-alone Approach Control Units (i.e. totalling 78 air traffic service units).
Europe employs a collaborative approach to manage and service airspace and air traffic. This includes the integration of military objectives and requirements which need to be fully coordinated within the ATM System. A variety of coordination cells/procedures exists between civil air traffic control centres and air defence units reflecting the local practices. Many EUROCONTROL member states are members of NATO and have their air defence centres / processes for civil-military coordination aligned under the integrated NATO air defence system.
@@ -525,28 +525,28 @@As concerns airport-related operational air navigation performance, this edition of the comparison report addresses the performance at a set of selected airports. These airports represent the top-10 or most relevant airports in terms of IFR movements in both regions and allow to make meaningful comparisons.
In Brazil, the selected airports play a significant role for the national and regional connectivity, including the major hubs for international air traffic. These study airports have consolidated systems and structured processes for data collection in support of this comparison report.
For the European context, the study airports comprise the busiest airports in several states exhibiting a mix of national, regional, and international air traffic. These airports are also characterised by varying operational constraints that make them excellent candidates for an international comparison. All of these airports are subject to the performance monitoring under the EUROCONTROL Performance Review System and provide movement related data on the basis of a harmonised data specification.
-Figure fig-scope-airports provides an overview of the location of the chosen study airports within both regions. The airports are also listed in Table tbl-scopetable.
+Figure 1.3 provides an overview of the location of the chosen study airports within both regions. The airports are also listed in Table 1.1.
-
-
@@ -1027,7 +1027,7 @@ 1.4 Data Sources
The nature of the performance indicator requires the collection of data from different sources. DECEA Performance Section and PRU investigated the comparability of the data available in both regions, including the data pre-processes, data cleaning and aggregation, to ensure a harmonised set of data for performance comparison purposes.
DECEA mainly uses data from the tower systems of the main airports as a data source for performance studies. The control tower system collects and provides data for each landing and take-off operation automatically. This edition blended ANAC (Brazilian CAA) official and public data with DECEA’s data to increase precision for specific indicators, adding a pre-processing phase to the data analytical work. The provided data include such items as the times of operations, gate entry and exit, and flight origin and destination.
-Within the European context, PRU has established a variety of performance-related data collection processes. For this report the main sources are the European Air Traffic Flow Management System (ETFMS 2) complemented with airport operator reported data. These sources are combined to establish a flight-by-flight record. This ensures consistent data for arrivals and departures at the chosen study airports. The data is collected on a monthly basis and typically processed for the regular performance reporting under the EUROCONTROL Performance Review System and the Single European Sky Performance and Charging Scheme (EUROCONTROL 2019).
+Within the European context, PRU has established a variety of performance-related data collection processes. For this report the main sources are the European Air Traffic Flow Management System (ETFMS 2) complemented with airport operator reported data. These sources are combined to establish a flight-by-flight record. This ensures consistent data for arrivals and departures at the chosen study airports. The data is collected on a monthly basis and typically processed for the regular performance reporting under the EUROCONTROL Performance Review System and the Single European Sky Performance and Charging Scheme (EUROCONTROL 2019).
1.5 Structure of the Report
@@ -1043,7 +1043,7 @@
-
+Figure fig-annual-traffic-timeline shows the regional traffic development in Brazil and Europe. In both regions the unprecedented decline in air traffic occurred in March 2020 in the aftermath of the pandemic-declaration by the World Health Organisation. However, there is a difference in terms of the overall recovery. The European recovery is characterised by two waves, while a single setback is observed in Brazil in second quarter of 2021. The European pattern demonstrates the difficulty in the coordination of a joint policy of curbing the pandemic and managing travel related constraints. With different states in Europe introducing public health and travel constraints at differnt times, intra-European traffic was affected by the piece-meal approach. Brazil - and its policy on air transport - benefitted from single stance on policy implementation.
+Figure 3.1 shows the regional traffic development in Brazil and Europe. In both regions the unprecedented decline in air traffic occurred in March 2020 in the aftermath of the pandemic-declaration by the World Health Organisation. However, there is a difference in terms of the overall recovery. The European recovery is characterised by two waves, while a single setback is observed in Brazil in second quarter of 2021. The European pattern demonstrates the difficulty in the coordination of a joint policy of curbing the pandemic and managing travel related constraints. With different states in Europe introducing public health and travel constraints at differnt times, intra-European traffic was affected by the piece-meal approach. Brazil - and its policy on air transport - benefitted from single stance on policy implementation.
@@ -469,7 +469,7 @@ Figure fig-annual-traffic-timeline shows the aggregated movements per airport at the whole network level. The shown total does not necessarily reflect the total number of flights. Another important observation related to the data is that Brazil’s number of airports served with the TATIC tool (Tower ATC System) has increased. Despite raising the processed total daily flight number, this difference is mostly transparent for this study as these additional airports handle only a small number of movements on a day-to-day basis.
+
For Brazil, it is important to remember that Figure 3.1 shows the aggregated movements per airport at the whole network level. The shown total does not necessarily reflect the total number of flights. Another important observation related to the data is that Brazil’s number of airports served with the TATIC tool (Tower ATC System) has increased. Despite raising the processed total daily flight number, this difference is mostly transparent for this study as these additional airports handle only a small number of movements on a day-to-day basis.
The movements already surpassed the 2019 levels for the Brazilian region, confirming some economic recovery in the market. According to the CGNA (Brazilian Network Manager) assessment, general aviation is the leading actor in this frame. The share of “Light” aircraft in the fleet mix observed at Brazilian airports and the prevailing airline traffic levels still below the 2019 traffic in the airlines’ preferred airports help to confirm this thesis.
In terms of total network level air traffic, the European region is still lagging behind its pre-pandemic levels. Other analyses showed that low-cost carriers recovered more agile than the classical mainline carriers. The low-cost sector, thus, shows a higher numbers of operations than pre-pandemic as their financial model allowed for a more agile reaction in terms of staffing/crewing/servicing flights. The higher share can also be explained by a side-effect of the national support measures for some of the mainline carriers. These measures included freeing slots at major hubs and the reduction of domestic / short-haul operations. Accordingly, the European network is characterised by a change in the level of connectivity and frequency of services between the different airports.
@@ -556,7 +556,7 @@ Figure fig-apt-annual-change shows the annual change of the traffic served at the study airports in 2022 and the associated change of the traffic levels comparing 2022 and 2019.
+
Figure 3.4 shows the annual change of the traffic served at the study airports in 2022 and the associated change of the traffic levels comparing 2022 and 2019.
With Campinas (SBKP) and Rio de Janeiro (SBRJ), there are two study airports in Brazil that serviced a higher level of traffic in 2022 than in 2019. Both airports are key nodes for the domestic traffic in Brazil. Salvador (SBSV) ranged at the pre-pandemic level. The other Brazilian airports have seen - on average - a decrease of 10-20% of traffic. This suggests that the observed network level increase in movements is distributed across the Brazilian network and not focussed on the airports covered in this study.
The European airport level traffic - on average - ranged at 20% below the pre-pandemic levels. Munich (EDDM) and Rome (LIRF) observed higher reductions. With an overall weaker recovery of the air traffic demand across Europe in 2022, a similar pattern emerged. The increased network level traffic (ranging about 10% under the pre-pandemic level) is distributed across the European network and other aerodrome connections.
@@ -576,7 +576,7 @@
-Figure fig-peak-day shows the peak day traffic in 2022 for the study airports with reference to the number of runways. A varied picture can be seen for Europe. For European with more than 2 runways it needs to be noted that the runway system does not support independent operations of all available runways. Thus, the serviced peak traffic is also impacted by the runway system configuration.
+Figure 3.5 shows the peak day traffic in 2022 for the study airports with reference to the number of runways. A varied picture can be seen for Europe. For European with more than 2 runways it needs to be noted that the runway system does not support independent operations of all available runways. Thus, the serviced peak traffic is also impacted by the runway system configuration.
The measure signals the use of the available runway system.
@@ -591,7 +591,7 @@
-The year-to-year change of the peak day traffic between 2019 and 2022 is shown in Figure fig-timeline-peak-day. For the European study airports, Frankfurt (EDDF), Munich (EDDM), Paris (LFPG), and Rome (LIRF) experienced a higher drop of the daily peak traffic in comparison to 2019. Despite the not yet fully recovered demand situation at London Gatwick (EGKK) and Zurich (LSZH) showed a moderate reduction of the daily peak traffic in 2022. This suggest that airports with limited airport runway capacity managed
+The year-to-year change of the peak day traffic between 2019 and 2022 is shown in Figure 3.6. For the European study airports, Frankfurt (EDDF), Munich (EDDM), Paris (LFPG), and Rome (LIRF) experienced a higher drop of the daily peak traffic in comparison to 2019. Despite the not yet fully recovered demand situation at London Gatwick (EGKK) and Zurich (LSZH) showed a moderate reduction of the daily peak traffic in 2022. This suggest that airports with limited airport runway capacity managed
3.4 Fleet Mix
@@ -608,7 +608,7 @@ Figure fig-fleet-mix depicts the observed share of different wake turbulence categories (WTC) across the study airports in 2022. In both regions, “medium” aircraft types are the predominant aircraft type. The fleet mix - and in particular the separation requirements between the different aircraft types - is an important influencing factor for the capacity and observed (and achievable) throughput. In general, a larger proportions of heavy aircraft or aircraft with longer runway occupancy times may result in lower throughput due to the required larger wake turbulence separation or time spent on the runway. The locally defined capacity values may therefore differ based on the predominant fleet mix and operational characteristics, and ultimately result in different observed peak movement numbers or influence surface and terminal operations.
+Figure 3.7 depicts the observed share of different wake turbulence categories (WTC) across the study airports in 2022. In both regions, “medium” aircraft types are the predominant aircraft type. The fleet mix - and in particular the separation requirements between the different aircraft types - is an important influencing factor for the capacity and observed (and achievable) throughput. In general, a larger proportions of heavy aircraft or aircraft with longer runway occupancy times may result in lower throughput due to the required larger wake turbulence separation or time spent on the runway. The locally defined capacity values may therefore differ based on the predominant fleet mix and operational characteristics, and ultimately result in different observed peak movement numbers or influence surface and terminal operations.
In Brazil, a significant number of “light” types operated in 2023. For example Salvador (SBSV) serviced about 20% of “light” types. The major hubs, i.e. São Paulo Guarulhos (SBGR), Rio de Janeiro Galeão (SBGL), and Campinas (SBKP) observed a share of 15-20% of “heavy” aircraft. These airports serve also as destinations for international long-haul flights.
With the exception of Zurich (LSZH), the share of “light” types is negligible at the European study airport in 2023. London Heathrow (EGLL), Paris Charles de Gaule (LFPG), and Frankfurt (EDDF) observed the highest shares of “heavy” types.
Within the European region - and its multitude of national hubs - a significant number of international long-haul flights is operated at the chosen study airports. In Brazil, the highlighted airports, Guarulhos (SBGR), Galeão (SBGL), and Campinas (SBKP), play a major role in terms of international connectivity. It follows that medium and light types are used predominantly for inter-reginal connections. Based on the selected study airports, the underlying decentralised structure of the European network becomes more visible. Due to the geo-political composition, airports serving capitals or representing a main national hub are more frequent in Europe. This is in contrast to Brazil, where the international and heavy air traffic appears more centralised at 2-3 pre-dominant hubs.
@@ -622,7 +622,7 @@ Figure fig-fleetmix-timeline shows that the fleetmix remained fairly stable over the years. It is interesting to observe that the unprecedented decline in air transport during the pandemic phase did not substantially break this pattern. This suggests that the contraction of the traffic volume hit all segments at a similar rate 1.
+On average, Figure 3.8 shows that the fleetmix remained fairly stable over the years. It is interesting to observe that the unprecedented decline in air transport during the pandemic phase did not substantially break this pattern. This suggests that the contraction of the traffic volume hit all segments at a similar rate 1.
3.5 Summary
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@@ -478,7 +478,7 @@ 4
4.1 Arrival Punctuality
-Figure fig-arrival-punc shows the evolution of arrival punctuality for the study airports in Brazil and Europe. When comparing both regions in 2019 and 2023, Brazil’s share of early arrivals (earlier than 15 minutes before the scheduled arrival) is significantly higher than the same European portion. The share of early arrivals accounts for 20-25% across all Brazilian airports. In Europe, flights tend not to arrive significantly earlier than their scheduled time. On average, early arrival ranges between 8-15% in Europe in 2019. Recent studies conducted by the CGNA/DECEA show that air operators in Brazil declare flight times significantly longer than observed. A similar behaviour is also observed in Europe. Built-in buffer times help to achieve a high “on-time-performance” record and appeal to passengers favouring a timely arrival performance. Furthermore, both regions have regulations for passenger compensation in place which are triggered in the case of arrival delays. DECEA has already established a forum with the air operator regulator to discuss and propose solutions.
+Figure 4.1 shows the evolution of arrival punctuality for the study airports in Brazil and Europe. When comparing both regions in 2019 and 2023, Brazil’s share of early arrivals (earlier than 15 minutes before the scheduled arrival) is significantly higher than the same European portion. The share of early arrivals accounts for 20-25% across all Brazilian airports. In Europe, flights tend not to arrive significantly earlier than their scheduled time. On average, early arrival ranges between 8-15% in Europe in 2019. Recent studies conducted by the CGNA/DECEA show that air operators in Brazil declare flight times significantly longer than observed. A similar behaviour is also observed in Europe. Built-in buffer times help to achieve a high “on-time-performance” record and appeal to passengers favouring a timely arrival performance. Furthermore, both regions have regulations for passenger compensation in place which are triggered in the case of arrival delays. DECEA has already established a forum with the air operator regulator to discuss and propose solutions.
European airports saw their share of punctual flights in 2022 and 2023 decrease broadly compared to 2019, even with a more proportional distribution than the Brazilian system. For European operators, there were two primary factors contributing the the lower performance in these two years. The most significant is the returning and steadily growing demand, showing that the network of flights has little ability to absorb the delay of one specific delayed flight. A pattern already observed pre-pandemic and requiring to investigate how to increase capacity across the operational aviation value chain. The knock-on effect was amplified by local resource constraints in terms of passenger and turn-around facilitation. The incrasing traffic demand posed challenges at many airports in Europe. Delayed arrivals accumulated further reactionary delay and ultimately passed the delay systematically on to next flights. Further constraints were linked to air space and flow restrictions resulting from the geo-political conflict surrounding the Russian invasion in Ukraine. On average, arrival delays of 15 minutes or more compared to the schedule ranged between 25-35% across the Europen study airports in 2022.
@@ -505,7 +505,7 @@
-
-
On average, the share of flights arriving within -/+ 15 minutes of their scheduled time varies wider amongst the European study airports (c.f Figure fig-evolution-ARR-punc). The observed punctuality (and associated predictability) within the Brazilian system shows a more homogenuous pattern with a general trend towards 60% or more over the past two years.
+On average, the share of flights arriving within -/+ 15 minutes of their scheduled time varies wider amongst the European study airports (c.f Figure 4.2). The observed punctuality (and associated predictability) within the Brazilian system shows a more homogenuous pattern with a general trend towards 60% or more over the past two years.
@@ -519,7 +519,7 @@
-Figure fig-early-vs-late-arrivals compares the share of early and late arrivals at each study airport in 2019 and 2023. From a high-level perspective, air traffic tends to arrive well ahead of schedule in Brazil, while Europe observes a higher share of delayed arrivals. Guarulhos (SBGR) remained the Brazilian airport with the highest share of early flights in 2022 (i.e. 33%), followed by Campinas (SBKP) with 30%. Both airports are essential hubs in the country, and anticipation can be a consequence sought by air operators for better accommodation of the flight network. However, for flow control, this lack of precision is equally problematic, affecting the optimal allocation of resources for the provision of air traffic control and flow service. In turn, Madrid (LEMD) was the European element with the most significant share of early arrivals (i.e. 22%) in 2022. Pre-pandemic such a share was observed at London Heathrow in 2019. These shares still range about 11% lower than the highest shares in Brazil. The distorted nature of the European network in 2022 becomes apparent when observing the share of delayed flights. For example services at London Gatwick (EGKK) faced a share of 39% of delayed flights. Airport operators were identified as the major contributors to primary delays (ground handling, staff shortage), followed by ATFM delays. However, the aforementioned reactionary effect was the main driver of knock-on delays (EUROCONTROL Central Office of Delay Analysis 2023) 1.
+Figure 4.3 compares the share of early and late arrivals at each study airport in 2019 and 2023. From a high-level perspective, air traffic tends to arrive well ahead of schedule in Brazil, while Europe observes a higher share of delayed arrivals. Guarulhos (SBGR) remained the Brazilian airport with the highest share of early flights in 2022 (i.e. 33%), followed by Campinas (SBKP) with 30%. Both airports are essential hubs in the country, and anticipation can be a consequence sought by air operators for better accommodation of the flight network. However, for flow control, this lack of precision is equally problematic, affecting the optimal allocation of resources for the provision of air traffic control and flow service. In turn, Madrid (LEMD) was the European element with the most significant share of early arrivals (i.e. 22%) in 2022. Pre-pandemic such a share was observed at London Heathrow in 2019. These shares still range about 11% lower than the highest shares in Brazil. The distorted nature of the European network in 2022 becomes apparent when observing the share of delayed flights. For example services at London Gatwick (EGKK) faced a share of 39% of delayed flights. Airport operators were identified as the major contributors to primary delays (ground handling, staff shortage), followed by ATFM delays. However, the aforementioned reactionary effect was the main driver of knock-on delays (EUROCONTROL Central Office of Delay Analysis 2023) 1.
4.2 Departure Punctuality
@@ -541,7 +541,7 @@ Figure 4.4: Evolution of departure punctuality at study airports (2019 vs 2023)
-The preceding section highlighted how the general traffic conditions in the previous years influenced the dependability of arrival schedules. In this section, we assess the degree of departure punctuality measured as the difference between the scheduled (i.e. planned) departure versus the observed actual off-block time. Figure fig-departure-punc shows the overall departure punctuality at Brazilian and European airports in 2019 compared to 2022. Despite traffic levels in 2022 still ranging below their 2019 pre-pandemic levels, the departure punctuality in 2022 was - on average - lower than before COVID.
+The preceding section highlighted how the general traffic conditions in the previous years influenced the dependability of arrival schedules. In this section, we assess the degree of departure punctuality measured as the difference between the scheduled (i.e. planned) departure versus the observed actual off-block time. Figure 4.4 shows the overall departure punctuality at Brazilian and European airports in 2019 compared to 2022. Despite traffic levels in 2022 still ranging below their 2019 pre-pandemic levels, the departure punctuality in 2022 was - on average - lower than before COVID.
The difference in departure and arrival punctuality between 2023 and 2019 was significantly more pronounced for Europe indicating an increased strain on the turnaround processes. There has been a significant increase in poor performance days, with departure punctuality falling below 50% and arrival punctuality dropping below 60%, occurring more frequently than in 2019. On the Brazilian side, the Galeão airport (SBGL) observed the highest share of delayed departure flights. It should be noted that the SBGL is the only airport with the Apron Control service directly provided by the airport. Some inefficiency in the coordination between Tower and Apron or divergence at the indicator collection point for the location may be contributing to the observed performance.
Departure punctuality in Brazil in 2023 reaches similar levels than in 2019 and outperformed the punctuality levels observed in Europe. It is also notworthy, that in Brazil there is a higher share of flights blocking off between 15 to 5 minutes before their scheduled time. Further research may help to clarify the factors driving this phenomenon.
@@ -557,7 +557,7 @@
-Figure fig-evolution-DEP-punc shows the evolution of the departure punctuality window within 15 minutes of the scheduled departure time. On average, the predictability of departing traffic is higher than for the arrival (c.f. Figure fig-evolution-ARR-punc). The trend at the Brazilian study airports shows a homogeneous behaviour for the period 2019 through 2023. This included a higher departure punctuality within 15 minutes during the pandemic phase. There is also evidence that the increasing post-pandemic levels put a strain on the departure punctuality performance with the level of observed performance in 2023 ranging at the same levels than pre-pandemic.
+Figure 4.5 shows the evolution of the departure punctuality window within 15 minutes of the scheduled departure time. On average, the predictability of departing traffic is higher than for the arrival (c.f. Figure 4.2). The trend at the Brazilian study airports shows a homogeneous behaviour for the period 2019 through 2023. This included a higher departure punctuality within 15 minutes during the pandemic phase. There is also evidence that the increasing post-pandemic levels put a strain on the departure punctuality performance with the level of observed performance in 2023 ranging at the same levels than pre-pandemic.
On the European side, punctuality levels showed a sharp decrease post-COVID and are driven by the system-wide disruptions in 2022 and the ripple effects observed in 2023. For most of the European study airports, departure predictability remained constant or improved marginally in 2023 versus 2022. This still indicates that there exists constraints regarding the turnaround of aircraft. While variances exist, on average the share of departures within 15 minutes of the scheduled departure time ranges below the pre-pandemic levels. It is noteworthy to recall that also 2019 has seen major restrictions in the European system.
It is planned to investigate the underlying turnaround drivers in future editions of the comparison report.
@@ -573,7 +573,7 @@
1.4 Data Sources
The nature of the performance indicator requires the collection of data from different sources. DECEA Performance Section and PRU investigated the comparability of the data available in both regions, including the data pre-processes, data cleaning and aggregation, to ensure a harmonised set of data for performance comparison purposes.
DECEA mainly uses data from the tower systems of the main airports as a data source for performance studies. The control tower system collects and provides data for each landing and take-off operation automatically. This edition blended ANAC (Brazilian CAA) official and public data with DECEA’s data to increase precision for specific indicators, adding a pre-processing phase to the data analytical work. The provided data include such items as the times of operations, gate entry and exit, and flight origin and destination.
-Within the European context, PRU has established a variety of performance-related data collection processes. For this report the main sources are the European Air Traffic Flow Management System (ETFMS 2) complemented with airport operator reported data. These sources are combined to establish a flight-by-flight record. This ensures consistent data for arrivals and departures at the chosen study airports. The data is collected on a monthly basis and typically processed for the regular performance reporting under the EUROCONTROL Performance Review System and the Single European Sky Performance and Charging Scheme (EUROCONTROL 2019).
+Within the European context, PRU has established a variety of performance-related data collection processes. For this report the main sources are the European Air Traffic Flow Management System (ETFMS 2) complemented with airport operator reported data. These sources are combined to establish a flight-by-flight record. This ensures consistent data for arrivals and departures at the chosen study airports. The data is collected on a monthly basis and typically processed for the regular performance reporting under the EUROCONTROL Performance Review System and the Single European Sky Performance and Charging Scheme (EUROCONTROL 2019).
1.5 Structure of the Report
@@ -1043,7 +1043,7 @@
-
+Figure fig-annual-traffic-timeline shows the regional traffic development in Brazil and Europe. In both regions the unprecedented decline in air traffic occurred in March 2020 in the aftermath of the pandemic-declaration by the World Health Organisation. However, there is a difference in terms of the overall recovery. The European recovery is characterised by two waves, while a single setback is observed in Brazil in second quarter of 2021. The European pattern demonstrates the difficulty in the coordination of a joint policy of curbing the pandemic and managing travel related constraints. With different states in Europe introducing public health and travel constraints at differnt times, intra-European traffic was affected by the piece-meal approach. Brazil - and its policy on air transport - benefitted from single stance on policy implementation.
+Figure 3.1 shows the regional traffic development in Brazil and Europe. In both regions the unprecedented decline in air traffic occurred in March 2020 in the aftermath of the pandemic-declaration by the World Health Organisation. However, there is a difference in terms of the overall recovery. The European recovery is characterised by two waves, while a single setback is observed in Brazil in second quarter of 2021. The European pattern demonstrates the difficulty in the coordination of a joint policy of curbing the pandemic and managing travel related constraints. With different states in Europe introducing public health and travel constraints at differnt times, intra-European traffic was affected by the piece-meal approach. Brazil - and its policy on air transport - benefitted from single stance on policy implementation.
@@ -469,7 +469,7 @@ Figure fig-annual-traffic-timeline shows the aggregated movements per airport at the whole network level. The shown total does not necessarily reflect the total number of flights. Another important observation related to the data is that Brazil’s number of airports served with the TATIC tool (Tower ATC System) has increased. Despite raising the processed total daily flight number, this difference is mostly transparent for this study as these additional airports handle only a small number of movements on a day-to-day basis.
+
For Brazil, it is important to remember that Figure 3.1 shows the aggregated movements per airport at the whole network level. The shown total does not necessarily reflect the total number of flights. Another important observation related to the data is that Brazil’s number of airports served with the TATIC tool (Tower ATC System) has increased. Despite raising the processed total daily flight number, this difference is mostly transparent for this study as these additional airports handle only a small number of movements on a day-to-day basis.
The movements already surpassed the 2019 levels for the Brazilian region, confirming some economic recovery in the market. According to the CGNA (Brazilian Network Manager) assessment, general aviation is the leading actor in this frame. The share of “Light” aircraft in the fleet mix observed at Brazilian airports and the prevailing airline traffic levels still below the 2019 traffic in the airlines’ preferred airports help to confirm this thesis.
In terms of total network level air traffic, the European region is still lagging behind its pre-pandemic levels. Other analyses showed that low-cost carriers recovered more agile than the classical mainline carriers. The low-cost sector, thus, shows a higher numbers of operations than pre-pandemic as their financial model allowed for a more agile reaction in terms of staffing/crewing/servicing flights. The higher share can also be explained by a side-effect of the national support measures for some of the mainline carriers. These measures included freeing slots at major hubs and the reduction of domestic / short-haul operations. Accordingly, the European network is characterised by a change in the level of connectivity and frequency of services between the different airports.
@@ -556,7 +556,7 @@ Figure fig-apt-annual-change shows the annual change of the traffic served at the study airports in 2022 and the associated change of the traffic levels comparing 2022 and 2019.
+
Figure 3.4 shows the annual change of the traffic served at the study airports in 2022 and the associated change of the traffic levels comparing 2022 and 2019.
With Campinas (SBKP) and Rio de Janeiro (SBRJ), there are two study airports in Brazil that serviced a higher level of traffic in 2022 than in 2019. Both airports are key nodes for the domestic traffic in Brazil. Salvador (SBSV) ranged at the pre-pandemic level. The other Brazilian airports have seen - on average - a decrease of 10-20% of traffic. This suggests that the observed network level increase in movements is distributed across the Brazilian network and not focussed on the airports covered in this study.
The European airport level traffic - on average - ranged at 20% below the pre-pandemic levels. Munich (EDDM) and Rome (LIRF) observed higher reductions. With an overall weaker recovery of the air traffic demand across Europe in 2022, a similar pattern emerged. The increased network level traffic (ranging about 10% under the pre-pandemic level) is distributed across the European network and other aerodrome connections.
@@ -576,7 +576,7 @@
-Figure fig-peak-day shows the peak day traffic in 2022 for the study airports with reference to the number of runways. A varied picture can be seen for Europe. For European with more than 2 runways it needs to be noted that the runway system does not support independent operations of all available runways. Thus, the serviced peak traffic is also impacted by the runway system configuration.
+Figure 3.5 shows the peak day traffic in 2022 for the study airports with reference to the number of runways. A varied picture can be seen for Europe. For European with more than 2 runways it needs to be noted that the runway system does not support independent operations of all available runways. Thus, the serviced peak traffic is also impacted by the runway system configuration.
The measure signals the use of the available runway system.
@@ -591,7 +591,7 @@
-The year-to-year change of the peak day traffic between 2019 and 2022 is shown in Figure fig-timeline-peak-day. For the European study airports, Frankfurt (EDDF), Munich (EDDM), Paris (LFPG), and Rome (LIRF) experienced a higher drop of the daily peak traffic in comparison to 2019. Despite the not yet fully recovered demand situation at London Gatwick (EGKK) and Zurich (LSZH) showed a moderate reduction of the daily peak traffic in 2022. This suggest that airports with limited airport runway capacity managed
+The year-to-year change of the peak day traffic between 2019 and 2022 is shown in Figure 3.6. For the European study airports, Frankfurt (EDDF), Munich (EDDM), Paris (LFPG), and Rome (LIRF) experienced a higher drop of the daily peak traffic in comparison to 2019. Despite the not yet fully recovered demand situation at London Gatwick (EGKK) and Zurich (LSZH) showed a moderate reduction of the daily peak traffic in 2022. This suggest that airports with limited airport runway capacity managed
3.4 Fleet Mix
@@ -608,7 +608,7 @@ Figure fig-fleet-mix depicts the observed share of different wake turbulence categories (WTC) across the study airports in 2022. In both regions, “medium” aircraft types are the predominant aircraft type. The fleet mix - and in particular the separation requirements between the different aircraft types - is an important influencing factor for the capacity and observed (and achievable) throughput. In general, a larger proportions of heavy aircraft or aircraft with longer runway occupancy times may result in lower throughput due to the required larger wake turbulence separation or time spent on the runway. The locally defined capacity values may therefore differ based on the predominant fleet mix and operational characteristics, and ultimately result in different observed peak movement numbers or influence surface and terminal operations.
+Figure 3.7 depicts the observed share of different wake turbulence categories (WTC) across the study airports in 2022. In both regions, “medium” aircraft types are the predominant aircraft type. The fleet mix - and in particular the separation requirements between the different aircraft types - is an important influencing factor for the capacity and observed (and achievable) throughput. In general, a larger proportions of heavy aircraft or aircraft with longer runway occupancy times may result in lower throughput due to the required larger wake turbulence separation or time spent on the runway. The locally defined capacity values may therefore differ based on the predominant fleet mix and operational characteristics, and ultimately result in different observed peak movement numbers or influence surface and terminal operations.
In Brazil, a significant number of “light” types operated in 2023. For example Salvador (SBSV) serviced about 20% of “light” types. The major hubs, i.e. São Paulo Guarulhos (SBGR), Rio de Janeiro Galeão (SBGL), and Campinas (SBKP) observed a share of 15-20% of “heavy” aircraft. These airports serve also as destinations for international long-haul flights.
With the exception of Zurich (LSZH), the share of “light” types is negligible at the European study airport in 2023. London Heathrow (EGLL), Paris Charles de Gaule (LFPG), and Frankfurt (EDDF) observed the highest shares of “heavy” types.
Within the European region - and its multitude of national hubs - a significant number of international long-haul flights is operated at the chosen study airports. In Brazil, the highlighted airports, Guarulhos (SBGR), Galeão (SBGL), and Campinas (SBKP), play a major role in terms of international connectivity. It follows that medium and light types are used predominantly for inter-reginal connections. Based on the selected study airports, the underlying decentralised structure of the European network becomes more visible. Due to the geo-political composition, airports serving capitals or representing a main national hub are more frequent in Europe. This is in contrast to Brazil, where the international and heavy air traffic appears more centralised at 2-3 pre-dominant hubs.
@@ -622,7 +622,7 @@ Figure fig-fleetmix-timeline shows that the fleetmix remained fairly stable over the years. It is interesting to observe that the unprecedented decline in air transport during the pandemic phase did not substantially break this pattern. This suggests that the contraction of the traffic volume hit all segments at a similar rate 1.
+On average, Figure 3.8 shows that the fleetmix remained fairly stable over the years. It is interesting to observe that the unprecedented decline in air transport during the pandemic phase did not substantially break this pattern. This suggests that the contraction of the traffic volume hit all segments at a similar rate 1.
3.5 Summary
diff --git a/docs/03-traffic_characterisation_files/figure-html/fig-timeline-peak-day-1.png b/docs/03-traffic_characterisation_files/figure-html/fig-timeline-peak-day-1.png
index 2f22372..285d1e7 100644
Binary files a/docs/03-traffic_characterisation_files/figure-html/fig-timeline-peak-day-1.png and b/docs/03-traffic_characterisation_files/figure-html/fig-timeline-peak-day-1.png differ
diff --git a/docs/04-predictability.html b/docs/04-predictability.html
index d7b290c..5226e85 100644
--- a/docs/04-predictability.html
+++ b/docs/04-predictability.html
@@ -478,7 +478,7 @@ 4
4.1 Arrival Punctuality
-Figure fig-arrival-punc shows the evolution of arrival punctuality for the study airports in Brazil and Europe. When comparing both regions in 2019 and 2023, Brazil’s share of early arrivals (earlier than 15 minutes before the scheduled arrival) is significantly higher than the same European portion. The share of early arrivals accounts for 20-25% across all Brazilian airports. In Europe, flights tend not to arrive significantly earlier than their scheduled time. On average, early arrival ranges between 8-15% in Europe in 2019. Recent studies conducted by the CGNA/DECEA show that air operators in Brazil declare flight times significantly longer than observed. A similar behaviour is also observed in Europe. Built-in buffer times help to achieve a high “on-time-performance” record and appeal to passengers favouring a timely arrival performance. Furthermore, both regions have regulations for passenger compensation in place which are triggered in the case of arrival delays. DECEA has already established a forum with the air operator regulator to discuss and propose solutions.
+Figure 4.1 shows the evolution of arrival punctuality for the study airports in Brazil and Europe. When comparing both regions in 2019 and 2023, Brazil’s share of early arrivals (earlier than 15 minutes before the scheduled arrival) is significantly higher than the same European portion. The share of early arrivals accounts for 20-25% across all Brazilian airports. In Europe, flights tend not to arrive significantly earlier than their scheduled time. On average, early arrival ranges between 8-15% in Europe in 2019. Recent studies conducted by the CGNA/DECEA show that air operators in Brazil declare flight times significantly longer than observed. A similar behaviour is also observed in Europe. Built-in buffer times help to achieve a high “on-time-performance” record and appeal to passengers favouring a timely arrival performance. Furthermore, both regions have regulations for passenger compensation in place which are triggered in the case of arrival delays. DECEA has already established a forum with the air operator regulator to discuss and propose solutions.
European airports saw their share of punctual flights in 2022 and 2023 decrease broadly compared to 2019, even with a more proportional distribution than the Brazilian system. For European operators, there were two primary factors contributing the the lower performance in these two years. The most significant is the returning and steadily growing demand, showing that the network of flights has little ability to absorb the delay of one specific delayed flight. A pattern already observed pre-pandemic and requiring to investigate how to increase capacity across the operational aviation value chain. The knock-on effect was amplified by local resource constraints in terms of passenger and turn-around facilitation. The incrasing traffic demand posed challenges at many airports in Europe. Delayed arrivals accumulated further reactionary delay and ultimately passed the delay systematically on to next flights. Further constraints were linked to air space and flow restrictions resulting from the geo-political conflict surrounding the Russian invasion in Ukraine. On average, arrival delays of 15 minutes or more compared to the schedule ranged between 25-35% across the Europen study airports in 2022.
@@ -505,7 +505,7 @@
-
-
On average, the share of flights arriving within -/+ 15 minutes of their scheduled time varies wider amongst the European study airports (c.f Figure fig-evolution-ARR-punc). The observed punctuality (and associated predictability) within the Brazilian system shows a more homogenuous pattern with a general trend towards 60% or more over the past two years.
+On average, the share of flights arriving within -/+ 15 minutes of their scheduled time varies wider amongst the European study airports (c.f Figure 4.2). The observed punctuality (and associated predictability) within the Brazilian system shows a more homogenuous pattern with a general trend towards 60% or more over the past two years.
@@ -519,7 +519,7 @@
1.5 Structure of the Report
@@ -1043,7 +1043,7 @@
-
+Figure fig-annual-traffic-timeline shows the regional traffic development in Brazil and Europe. In both regions the unprecedented decline in air traffic occurred in March 2020 in the aftermath of the pandemic-declaration by the World Health Organisation. However, there is a difference in terms of the overall recovery. The European recovery is characterised by two waves, while a single setback is observed in Brazil in second quarter of 2021. The European pattern demonstrates the difficulty in the coordination of a joint policy of curbing the pandemic and managing travel related constraints. With different states in Europe introducing public health and travel constraints at differnt times, intra-European traffic was affected by the piece-meal approach. Brazil - and its policy on air transport - benefitted from single stance on policy implementation.
+Figure 3.1 shows the regional traffic development in Brazil and Europe. In both regions the unprecedented decline in air traffic occurred in March 2020 in the aftermath of the pandemic-declaration by the World Health Organisation. However, there is a difference in terms of the overall recovery. The European recovery is characterised by two waves, while a single setback is observed in Brazil in second quarter of 2021. The European pattern demonstrates the difficulty in the coordination of a joint policy of curbing the pandemic and managing travel related constraints. With different states in Europe introducing public health and travel constraints at differnt times, intra-European traffic was affected by the piece-meal approach. Brazil - and its policy on air transport - benefitted from single stance on policy implementation.
@@ -469,7 +469,7 @@ Figure fig-annual-traffic-timeline shows the aggregated movements per airport at the whole network level. The shown total does not necessarily reflect the total number of flights. Another important observation related to the data is that Brazil’s number of airports served with the TATIC tool (Tower ATC System) has increased. Despite raising the processed total daily flight number, this difference is mostly transparent for this study as these additional airports handle only a small number of movements on a day-to-day basis.
+
For Brazil, it is important to remember that Figure 3.1 shows the aggregated movements per airport at the whole network level. The shown total does not necessarily reflect the total number of flights. Another important observation related to the data is that Brazil’s number of airports served with the TATIC tool (Tower ATC System) has increased. Despite raising the processed total daily flight number, this difference is mostly transparent for this study as these additional airports handle only a small number of movements on a day-to-day basis.
The movements already surpassed the 2019 levels for the Brazilian region, confirming some economic recovery in the market. According to the CGNA (Brazilian Network Manager) assessment, general aviation is the leading actor in this frame. The share of “Light” aircraft in the fleet mix observed at Brazilian airports and the prevailing airline traffic levels still below the 2019 traffic in the airlines’ preferred airports help to confirm this thesis.
In terms of total network level air traffic, the European region is still lagging behind its pre-pandemic levels. Other analyses showed that low-cost carriers recovered more agile than the classical mainline carriers. The low-cost sector, thus, shows a higher numbers of operations than pre-pandemic as their financial model allowed for a more agile reaction in terms of staffing/crewing/servicing flights. The higher share can also be explained by a side-effect of the national support measures for some of the mainline carriers. These measures included freeing slots at major hubs and the reduction of domestic / short-haul operations. Accordingly, the European network is characterised by a change in the level of connectivity and frequency of services between the different airports.
@@ -556,7 +556,7 @@ Figure fig-apt-annual-change shows the annual change of the traffic served at the study airports in 2022 and the associated change of the traffic levels comparing 2022 and 2019.
+
Figure 3.4 shows the annual change of the traffic served at the study airports in 2022 and the associated change of the traffic levels comparing 2022 and 2019.
With Campinas (SBKP) and Rio de Janeiro (SBRJ), there are two study airports in Brazil that serviced a higher level of traffic in 2022 than in 2019. Both airports are key nodes for the domestic traffic in Brazil. Salvador (SBSV) ranged at the pre-pandemic level. The other Brazilian airports have seen - on average - a decrease of 10-20% of traffic. This suggests that the observed network level increase in movements is distributed across the Brazilian network and not focussed on the airports covered in this study.
The European airport level traffic - on average - ranged at 20% below the pre-pandemic levels. Munich (EDDM) and Rome (LIRF) observed higher reductions. With an overall weaker recovery of the air traffic demand across Europe in 2022, a similar pattern emerged. The increased network level traffic (ranging about 10% under the pre-pandemic level) is distributed across the European network and other aerodrome connections.
@@ -576,7 +576,7 @@
-Figure fig-peak-day shows the peak day traffic in 2022 for the study airports with reference to the number of runways. A varied picture can be seen for Europe. For European with more than 2 runways it needs to be noted that the runway system does not support independent operations of all available runways. Thus, the serviced peak traffic is also impacted by the runway system configuration.
+Figure 3.5 shows the peak day traffic in 2022 for the study airports with reference to the number of runways. A varied picture can be seen for Europe. For European with more than 2 runways it needs to be noted that the runway system does not support independent operations of all available runways. Thus, the serviced peak traffic is also impacted by the runway system configuration.
The measure signals the use of the available runway system.
@@ -591,7 +591,7 @@
-The year-to-year change of the peak day traffic between 2019 and 2022 is shown in Figure fig-timeline-peak-day. For the European study airports, Frankfurt (EDDF), Munich (EDDM), Paris (LFPG), and Rome (LIRF) experienced a higher drop of the daily peak traffic in comparison to 2019. Despite the not yet fully recovered demand situation at London Gatwick (EGKK) and Zurich (LSZH) showed a moderate reduction of the daily peak traffic in 2022. This suggest that airports with limited airport runway capacity managed
+The year-to-year change of the peak day traffic between 2019 and 2022 is shown in Figure 3.6. For the European study airports, Frankfurt (EDDF), Munich (EDDM), Paris (LFPG), and Rome (LIRF) experienced a higher drop of the daily peak traffic in comparison to 2019. Despite the not yet fully recovered demand situation at London Gatwick (EGKK) and Zurich (LSZH) showed a moderate reduction of the daily peak traffic in 2022. This suggest that airports with limited airport runway capacity managed
3.4 Fleet Mix
@@ -608,7 +608,7 @@ Figure fig-fleet-mix depicts the observed share of different wake turbulence categories (WTC) across the study airports in 2022. In both regions, “medium” aircraft types are the predominant aircraft type. The fleet mix - and in particular the separation requirements between the different aircraft types - is an important influencing factor for the capacity and observed (and achievable) throughput. In general, a larger proportions of heavy aircraft or aircraft with longer runway occupancy times may result in lower throughput due to the required larger wake turbulence separation or time spent on the runway. The locally defined capacity values may therefore differ based on the predominant fleet mix and operational characteristics, and ultimately result in different observed peak movement numbers or influence surface and terminal operations.
+Figure 3.7 depicts the observed share of different wake turbulence categories (WTC) across the study airports in 2022. In both regions, “medium” aircraft types are the predominant aircraft type. The fleet mix - and in particular the separation requirements between the different aircraft types - is an important influencing factor for the capacity and observed (and achievable) throughput. In general, a larger proportions of heavy aircraft or aircraft with longer runway occupancy times may result in lower throughput due to the required larger wake turbulence separation or time spent on the runway. The locally defined capacity values may therefore differ based on the predominant fleet mix and operational characteristics, and ultimately result in different observed peak movement numbers or influence surface and terminal operations.
In Brazil, a significant number of “light” types operated in 2023. For example Salvador (SBSV) serviced about 20% of “light” types. The major hubs, i.e. São Paulo Guarulhos (SBGR), Rio de Janeiro Galeão (SBGL), and Campinas (SBKP) observed a share of 15-20% of “heavy” aircraft. These airports serve also as destinations for international long-haul flights.
With the exception of Zurich (LSZH), the share of “light” types is negligible at the European study airport in 2023. London Heathrow (EGLL), Paris Charles de Gaule (LFPG), and Frankfurt (EDDF) observed the highest shares of “heavy” types.
Within the European region - and its multitude of national hubs - a significant number of international long-haul flights is operated at the chosen study airports. In Brazil, the highlighted airports, Guarulhos (SBGR), Galeão (SBGL), and Campinas (SBKP), play a major role in terms of international connectivity. It follows that medium and light types are used predominantly for inter-reginal connections. Based on the selected study airports, the underlying decentralised structure of the European network becomes more visible. Due to the geo-political composition, airports serving capitals or representing a main national hub are more frequent in Europe. This is in contrast to Brazil, where the international and heavy air traffic appears more centralised at 2-3 pre-dominant hubs.
@@ -622,7 +622,7 @@ Figure fig-fleetmix-timeline shows that the fleetmix remained fairly stable over the years. It is interesting to observe that the unprecedented decline in air transport during the pandemic phase did not substantially break this pattern. This suggests that the contraction of the traffic volume hit all segments at a similar rate 1.
+On average, Figure 3.8 shows that the fleetmix remained fairly stable over the years. It is interesting to observe that the unprecedented decline in air transport during the pandemic phase did not substantially break this pattern. This suggests that the contraction of the traffic volume hit all segments at a similar rate 1.
3.5 Summary
diff --git a/docs/03-traffic_characterisation_files/figure-html/fig-timeline-peak-day-1.png b/docs/03-traffic_characterisation_files/figure-html/fig-timeline-peak-day-1.png
index 2f22372..285d1e7 100644
Binary files a/docs/03-traffic_characterisation_files/figure-html/fig-timeline-peak-day-1.png and b/docs/03-traffic_characterisation_files/figure-html/fig-timeline-peak-day-1.png differ
diff --git a/docs/04-predictability.html b/docs/04-predictability.html
index d7b290c..5226e85 100644
--- a/docs/04-predictability.html
+++ b/docs/04-predictability.html
@@ -478,7 +478,7 @@ 4
4.1 Arrival Punctuality
-Figure fig-arrival-punc shows the evolution of arrival punctuality for the study airports in Brazil and Europe. When comparing both regions in 2019 and 2023, Brazil’s share of early arrivals (earlier than 15 minutes before the scheduled arrival) is significantly higher than the same European portion. The share of early arrivals accounts for 20-25% across all Brazilian airports. In Europe, flights tend not to arrive significantly earlier than their scheduled time. On average, early arrival ranges between 8-15% in Europe in 2019. Recent studies conducted by the CGNA/DECEA show that air operators in Brazil declare flight times significantly longer than observed. A similar behaviour is also observed in Europe. Built-in buffer times help to achieve a high “on-time-performance” record and appeal to passengers favouring a timely arrival performance. Furthermore, both regions have regulations for passenger compensation in place which are triggered in the case of arrival delays. DECEA has already established a forum with the air operator regulator to discuss and propose solutions.
+Figure 4.1 shows the evolution of arrival punctuality for the study airports in Brazil and Europe. When comparing both regions in 2019 and 2023, Brazil’s share of early arrivals (earlier than 15 minutes before the scheduled arrival) is significantly higher than the same European portion. The share of early arrivals accounts for 20-25% across all Brazilian airports. In Europe, flights tend not to arrive significantly earlier than their scheduled time. On average, early arrival ranges between 8-15% in Europe in 2019. Recent studies conducted by the CGNA/DECEA show that air operators in Brazil declare flight times significantly longer than observed. A similar behaviour is also observed in Europe. Built-in buffer times help to achieve a high “on-time-performance” record and appeal to passengers favouring a timely arrival performance. Furthermore, both regions have regulations for passenger compensation in place which are triggered in the case of arrival delays. DECEA has already established a forum with the air operator regulator to discuss and propose solutions.
European airports saw their share of punctual flights in 2022 and 2023 decrease broadly compared to 2019, even with a more proportional distribution than the Brazilian system. For European operators, there were two primary factors contributing the the lower performance in these two years. The most significant is the returning and steadily growing demand, showing that the network of flights has little ability to absorb the delay of one specific delayed flight. A pattern already observed pre-pandemic and requiring to investigate how to increase capacity across the operational aviation value chain. The knock-on effect was amplified by local resource constraints in terms of passenger and turn-around facilitation. The incrasing traffic demand posed challenges at many airports in Europe. Delayed arrivals accumulated further reactionary delay and ultimately passed the delay systematically on to next flights. Further constraints were linked to air space and flow restrictions resulting from the geo-political conflict surrounding the Russian invasion in Ukraine. On average, arrival delays of 15 minutes or more compared to the schedule ranged between 25-35% across the Europen study airports in 2022.
@@ -505,7 +505,7 @@
-
-
Figure fig-annual-traffic-timeline shows the regional traffic development in Brazil and Europe. In both regions the unprecedented decline in air traffic occurred in March 2020 in the aftermath of the pandemic-declaration by the World Health Organisation. However, there is a difference in terms of the overall recovery. The European recovery is characterised by two waves, while a single setback is observed in Brazil in second quarter of 2021. The European pattern demonstrates the difficulty in the coordination of a joint policy of curbing the pandemic and managing travel related constraints. With different states in Europe introducing public health and travel constraints at differnt times, intra-European traffic was affected by the piece-meal approach. Brazil - and its policy on air transport - benefitted from single stance on policy implementation.
+Figure 3.1 shows the regional traffic development in Brazil and Europe. In both regions the unprecedented decline in air traffic occurred in March 2020 in the aftermath of the pandemic-declaration by the World Health Organisation. However, there is a difference in terms of the overall recovery. The European recovery is characterised by two waves, while a single setback is observed in Brazil in second quarter of 2021. The European pattern demonstrates the difficulty in the coordination of a joint policy of curbing the pandemic and managing travel related constraints. With different states in Europe introducing public health and travel constraints at differnt times, intra-European traffic was affected by the piece-meal approach. Brazil - and its policy on air transport - benefitted from single stance on policy implementation.
Figure fig-annual-traffic-timeline shows the aggregated movements per airport at the whole network level. The shown total does not necessarily reflect the total number of flights. Another important observation related to the data is that Brazil’s number of airports served with the TATIC tool (Tower ATC System) has increased. Despite raising the processed total daily flight number, this difference is mostly transparent for this study as these additional airports handle only a small number of movements on a day-to-day basis. +
For Brazil, it is important to remember that Figure 3.1 shows the aggregated movements per airport at the whole network level. The shown total does not necessarily reflect the total number of flights. Another important observation related to the data is that Brazil’s number of airports served with the TATIC tool (Tower ATC System) has increased. Despite raising the processed total daily flight number, this difference is mostly transparent for this study as these additional airports handle only a small number of movements on a day-to-day basis.
The movements already surpassed the 2019 levels for the Brazilian region, confirming some economic recovery in the market. According to the CGNA (Brazilian Network Manager) assessment, general aviation is the leading actor in this frame. The share of “Light” aircraft in the fleet mix observed at Brazilian airports and the prevailing airline traffic levels still below the 2019 traffic in the airlines’ preferred airports help to confirm this thesis.
In terms of total network level air traffic, the European region is still lagging behind its pre-pandemic levels. Other analyses showed that low-cost carriers recovered more agile than the classical mainline carriers. The low-cost sector, thus, shows a higher numbers of operations than pre-pandemic as their financial model allowed for a more agile reaction in terms of staffing/crewing/servicing flights. The higher share can also be explained by a side-effect of the national support measures for some of the mainline carriers. These measures included freeing slots at major hubs and the reduction of domestic / short-haul operations. Accordingly, the European network is characterised by a change in the level of connectivity and frequency of services between the different airports.
Figure fig-apt-annual-change shows the annual change of the traffic served at the study airports in 2022 and the associated change of the traffic levels comparing 2022 and 2019. +
Figure 3.4 shows the annual change of the traffic served at the study airports in 2022 and the associated change of the traffic levels comparing 2022 and 2019.
With Campinas (SBKP) and Rio de Janeiro (SBRJ), there are two study airports in Brazil that serviced a higher level of traffic in 2022 than in 2019. Both airports are key nodes for the domestic traffic in Brazil. Salvador (SBSV) ranged at the pre-pandemic level. The other Brazilian airports have seen - on average - a decrease of 10-20% of traffic. This suggests that the observed network level increase in movements is distributed across the Brazilian network and not focussed on the airports covered in this study.
The European airport level traffic - on average - ranged at 20% below the pre-pandemic levels. Munich (EDDM) and Rome (LIRF) observed higher reductions. With an overall weaker recovery of the air traffic demand across Europe in 2022, a similar pattern emerged. The increased network level traffic (ranging about 10% under the pre-pandemic level) is distributed across the European network and other aerodrome connections.
@@ -576,7 +576,7 @@Figure fig-peak-day shows the peak day traffic in 2022 for the study airports with reference to the number of runways. A varied picture can be seen for Europe. For European with more than 2 runways it needs to be noted that the runway system does not support independent operations of all available runways. Thus, the serviced peak traffic is also impacted by the runway system configuration.
+Figure 3.5 shows the peak day traffic in 2022 for the study airports with reference to the number of runways. A varied picture can be seen for Europe. For European with more than 2 runways it needs to be noted that the runway system does not support independent operations of all available runways. Thus, the serviced peak traffic is also impacted by the runway system configuration.
The measure signals the use of the available runway system.
The year-to-year change of the peak day traffic between 2019 and 2022 is shown in Figure fig-timeline-peak-day. For the European study airports, Frankfurt (EDDF), Munich (EDDM), Paris (LFPG), and Rome (LIRF) experienced a higher drop of the daily peak traffic in comparison to 2019. Despite the not yet fully recovered demand situation at London Gatwick (EGKK) and Zurich (LSZH) showed a moderate reduction of the daily peak traffic in 2022. This suggest that airports with limited airport runway capacity managed
+The year-to-year change of the peak day traffic between 2019 and 2022 is shown in Figure 3.6. For the European study airports, Frankfurt (EDDF), Munich (EDDM), Paris (LFPG), and Rome (LIRF) experienced a higher drop of the daily peak traffic in comparison to 2019. Despite the not yet fully recovered demand situation at London Gatwick (EGKK) and Zurich (LSZH) showed a moderate reduction of the daily peak traffic in 2022. This suggest that airports with limited airport runway capacity managed
3.4 Fleet Mix
@@ -608,7 +608,7 @@Figure fig-fleet-mix depicts the observed share of different wake turbulence categories (WTC) across the study airports in 2022. In both regions, “medium” aircraft types are the predominant aircraft type. The fleet mix - and in particular the separation requirements between the different aircraft types - is an important influencing factor for the capacity and observed (and achievable) throughput. In general, a larger proportions of heavy aircraft or aircraft with longer runway occupancy times may result in lower throughput due to the required larger wake turbulence separation or time spent on the runway. The locally defined capacity values may therefore differ based on the predominant fleet mix and operational characteristics, and ultimately result in different observed peak movement numbers or influence surface and terminal operations.
+Figure 3.7 depicts the observed share of different wake turbulence categories (WTC) across the study airports in 2022. In both regions, “medium” aircraft types are the predominant aircraft type. The fleet mix - and in particular the separation requirements between the different aircraft types - is an important influencing factor for the capacity and observed (and achievable) throughput. In general, a larger proportions of heavy aircraft or aircraft with longer runway occupancy times may result in lower throughput due to the required larger wake turbulence separation or time spent on the runway. The locally defined capacity values may therefore differ based on the predominant fleet mix and operational characteristics, and ultimately result in different observed peak movement numbers or influence surface and terminal operations.
In Brazil, a significant number of “light” types operated in 2023. For example Salvador (SBSV) serviced about 20% of “light” types. The major hubs, i.e. São Paulo Guarulhos (SBGR), Rio de Janeiro Galeão (SBGL), and Campinas (SBKP) observed a share of 15-20% of “heavy” aircraft. These airports serve also as destinations for international long-haul flights.
With the exception of Zurich (LSZH), the share of “light” types is negligible at the European study airport in 2023. London Heathrow (EGLL), Paris Charles de Gaule (LFPG), and Frankfurt (EDDF) observed the highest shares of “heavy” types.
Within the European region - and its multitude of national hubs - a significant number of international long-haul flights is operated at the chosen study airports. In Brazil, the highlighted airports, Guarulhos (SBGR), Galeão (SBGL), and Campinas (SBKP), play a major role in terms of international connectivity. It follows that medium and light types are used predominantly for inter-reginal connections. Based on the selected study airports, the underlying decentralised structure of the European network becomes more visible. Due to the geo-political composition, airports serving capitals or representing a main national hub are more frequent in Europe. This is in contrast to Brazil, where the international and heavy air traffic appears more centralised at 2-3 pre-dominant hubs.
@@ -622,7 +622,7 @@ Figure fig-fleetmix-timeline shows that the fleetmix remained fairly stable over the years. It is interesting to observe that the unprecedented decline in air transport during the pandemic phase did not substantially break this pattern. This suggests that the contraction of the traffic volume hit all segments at a similar rate 1.
+On average, Figure 3.8 shows that the fleetmix remained fairly stable over the years. It is interesting to observe that the unprecedented decline in air transport during the pandemic phase did not substantially break this pattern. This suggests that the contraction of the traffic volume hit all segments at a similar rate 1.
On average, Figure 3.8 shows that the fleetmix remained fairly stable over the years. It is interesting to observe that the unprecedented decline in air transport during the pandemic phase did not substantially break this pattern. This suggests that the contraction of the traffic volume hit all segments at a similar rate 1.
3.5 Summary
diff --git a/docs/03-traffic_characterisation_files/figure-html/fig-timeline-peak-day-1.png b/docs/03-traffic_characterisation_files/figure-html/fig-timeline-peak-day-1.png index 2f22372..285d1e7 100644 Binary files a/docs/03-traffic_characterisation_files/figure-html/fig-timeline-peak-day-1.png and b/docs/03-traffic_characterisation_files/figure-html/fig-timeline-peak-day-1.png differ diff --git a/docs/04-predictability.html b/docs/04-predictability.html index d7b290c..5226e85 100644 --- a/docs/04-predictability.html +++ b/docs/04-predictability.html @@ -478,7 +478,7 @@4
4.1 Arrival Punctuality
-Figure fig-arrival-punc shows the evolution of arrival punctuality for the study airports in Brazil and Europe. When comparing both regions in 2019 and 2023, Brazil’s share of early arrivals (earlier than 15 minutes before the scheduled arrival) is significantly higher than the same European portion. The share of early arrivals accounts for 20-25% across all Brazilian airports. In Europe, flights tend not to arrive significantly earlier than their scheduled time. On average, early arrival ranges between 8-15% in Europe in 2019. Recent studies conducted by the CGNA/DECEA show that air operators in Brazil declare flight times significantly longer than observed. A similar behaviour is also observed in Europe. Built-in buffer times help to achieve a high “on-time-performance” record and appeal to passengers favouring a timely arrival performance. Furthermore, both regions have regulations for passenger compensation in place which are triggered in the case of arrival delays. DECEA has already established a forum with the air operator regulator to discuss and propose solutions.
+Figure 4.1 shows the evolution of arrival punctuality for the study airports in Brazil and Europe. When comparing both regions in 2019 and 2023, Brazil’s share of early arrivals (earlier than 15 minutes before the scheduled arrival) is significantly higher than the same European portion. The share of early arrivals accounts for 20-25% across all Brazilian airports. In Europe, flights tend not to arrive significantly earlier than their scheduled time. On average, early arrival ranges between 8-15% in Europe in 2019. Recent studies conducted by the CGNA/DECEA show that air operators in Brazil declare flight times significantly longer than observed. A similar behaviour is also observed in Europe. Built-in buffer times help to achieve a high “on-time-performance” record and appeal to passengers favouring a timely arrival performance. Furthermore, both regions have regulations for passenger compensation in place which are triggered in the case of arrival delays. DECEA has already established a forum with the air operator regulator to discuss and propose solutions.
European airports saw their share of punctual flights in 2022 and 2023 decrease broadly compared to 2019, even with a more proportional distribution than the Brazilian system. For European operators, there were two primary factors contributing the the lower performance in these two years. The most significant is the returning and steadily growing demand, showing that the network of flights has little ability to absorb the delay of one specific delayed flight. A pattern already observed pre-pandemic and requiring to investigate how to increase capacity across the operational aviation value chain. The knock-on effect was amplified by local resource constraints in terms of passenger and turn-around facilitation. The incrasing traffic demand posed challenges at many airports in Europe. Delayed arrivals accumulated further reactionary delay and ultimately passed the delay systematically on to next flights. Further constraints were linked to air space and flow restrictions resulting from the geo-political conflict surrounding the Russian invasion in Ukraine. On average, arrival delays of 15 minutes or more compared to the schedule ranged between 25-35% across the Europen study airports in 2022.
On average, the share of flights arriving within -/+ 15 minutes of their scheduled time varies wider amongst the European study airports (c.f Figure fig-evolution-ARR-punc). The observed punctuality (and associated predictability) within the Brazilian system shows a more homogenuous pattern with a general trend towards 60% or more over the past two years.
+On average, the share of flights arriving within -/+ 15 minutes of their scheduled time varies wider amongst the European study airports (c.f Figure 4.2). The observed punctuality (and associated predictability) within the Brazilian system shows a more homogenuous pattern with a general trend towards 60% or more over the past two years.
Figure fig-early-vs-late-arrivals compares the share of early and late arrivals at each study airport in 2019 and 2023. From a high-level perspective, air traffic tends to arrive well ahead of schedule in Brazil, while Europe observes a higher share of delayed arrivals. Guarulhos (SBGR) remained the Brazilian airport with the highest share of early flights in 2022 (i.e. 33%), followed by Campinas (SBKP) with 30%. Both airports are essential hubs in the country, and anticipation can be a consequence sought by air operators for better accommodation of the flight network. However, for flow control, this lack of precision is equally problematic, affecting the optimal allocation of resources for the provision of air traffic control and flow service. In turn, Madrid (LEMD) was the European element with the most significant share of early arrivals (i.e. 22%) in 2022. Pre-pandemic such a share was observed at London Heathrow in 2019. These shares still range about 11% lower than the highest shares in Brazil. The distorted nature of the European network in 2022 becomes apparent when observing the share of delayed flights. For example services at London Gatwick (EGKK) faced a share of 39% of delayed flights. Airport operators were identified as the major contributors to primary delays (ground handling, staff shortage), followed by ATFM delays. However, the aforementioned reactionary effect was the main driver of knock-on delays (EUROCONTROL Central Office of Delay Analysis 2023) 1.
+Figure 4.3 compares the share of early and late arrivals at each study airport in 2019 and 2023. From a high-level perspective, air traffic tends to arrive well ahead of schedule in Brazil, while Europe observes a higher share of delayed arrivals. Guarulhos (SBGR) remained the Brazilian airport with the highest share of early flights in 2022 (i.e. 33%), followed by Campinas (SBKP) with 30%. Both airports are essential hubs in the country, and anticipation can be a consequence sought by air operators for better accommodation of the flight network. However, for flow control, this lack of precision is equally problematic, affecting the optimal allocation of resources for the provision of air traffic control and flow service. In turn, Madrid (LEMD) was the European element with the most significant share of early arrivals (i.e. 22%) in 2022. Pre-pandemic such a share was observed at London Heathrow in 2019. These shares still range about 11% lower than the highest shares in Brazil. The distorted nature of the European network in 2022 becomes apparent when observing the share of delayed flights. For example services at London Gatwick (EGKK) faced a share of 39% of delayed flights. Airport operators were identified as the major contributors to primary delays (ground handling, staff shortage), followed by ATFM delays. However, the aforementioned reactionary effect was the main driver of knock-on delays (EUROCONTROL Central Office of Delay Analysis 2023) 1.
4.2 Departure Punctuality
@@ -541,7 +541,7 @@Figure 4.4: Evolution of departure punctuality at study airports (2019 vs 2023)
The preceding section highlighted how the general traffic conditions in the previous years influenced the dependability of arrival schedules. In this section, we assess the degree of departure punctuality measured as the difference between the scheduled (i.e. planned) departure versus the observed actual off-block time. Figure fig-departure-punc shows the overall departure punctuality at Brazilian and European airports in 2019 compared to 2022. Despite traffic levels in 2022 still ranging below their 2019 pre-pandemic levels, the departure punctuality in 2022 was - on average - lower than before COVID.
+The preceding section highlighted how the general traffic conditions in the previous years influenced the dependability of arrival schedules. In this section, we assess the degree of departure punctuality measured as the difference between the scheduled (i.e. planned) departure versus the observed actual off-block time. Figure 4.4 shows the overall departure punctuality at Brazilian and European airports in 2019 compared to 2022. Despite traffic levels in 2022 still ranging below their 2019 pre-pandemic levels, the departure punctuality in 2022 was - on average - lower than before COVID.
The difference in departure and arrival punctuality between 2023 and 2019 was significantly more pronounced for Europe indicating an increased strain on the turnaround processes. There has been a significant increase in poor performance days, with departure punctuality falling below 50% and arrival punctuality dropping below 60%, occurring more frequently than in 2019. On the Brazilian side, the Galeão airport (SBGL) observed the highest share of delayed departure flights. It should be noted that the SBGL is the only airport with the Apron Control service directly provided by the airport. Some inefficiency in the coordination between Tower and Apron or divergence at the indicator collection point for the location may be contributing to the observed performance.
Departure punctuality in Brazil in 2023 reaches similar levels than in 2019 and outperformed the punctuality levels observed in Europe. It is also notworthy, that in Brazil there is a higher share of flights blocking off between 15 to 5 minutes before their scheduled time. Further research may help to clarify the factors driving this phenomenon.