[:en]
Microsoft Power BI file content :
Nota: The anticipation of cancelations concerns individual slots and not the whole series. In other words, and as an example, for a series covering the whole season that is canceled at the beginning of the season the first date would be canceled with no anticipation and the last date of the season for that series with the whole season anticipation)
The graphic in tab 2 shows the horizon of cancellations per month. Each chart shows (one chart per month) the number of cancellations (y-axis) per anticipation of cancellation (in number of days, x-axis) –
The chart in tab 1 shows the horizon of cancellations per date of operation.
For every date shown on the horizontal axis, it shows the number of slots that were originally scheduled on this date and cancelled:
A – On the date of operation
B – Between 1 and 7 days in advance
C – Between 8 and 14 days in advance
D – Between 15 and 21 days in advance
E – Between 22 and 28 days in advance
F – Between 29 and 59 days in advance
G – More than 60 days in advance
It is possible to highlight any of the curves by clicking on it.
The data are aggregated for the following list of airports. There is one chart for all airports combined (tab 1) and one per airport (starting on tab 3).
CDG – Paris Charles de Gaulle FR
LYS – Lyon FR
NCE – Nice FR
NTE – Nantes FR
ORY – Orly FR
For all these airports, data were aggregated for cancellations requested for flights scheduled between 1st of March and the end of Summer 2020 IATA season and flights scheduled for the first week of Winter 2020 IATA season.
All data were extracted by slot coordinators as of September 31st.
Highlights:
The graph essentially shows that:
– Very late cancellations, less than one week (cat A and B), were requested by airlines for flights scheduled in March and in very few cases scheduled for later periods;
– Late cancellations, between one and two weeks (cat C), were requested by airlines mainly for flights scheduled at the beginning of April and also at a lower scale every 2 weeks from mid April until now;
– Cancellations made 3 or 4 weeks in advance (cat D and E) represent a very large amount of all cancellations for flights scheduled mid April and at the beginning of May and June (more than 50%);
– Early cancellations (cat F) were almost non-existent for flights scheduled in March, not numerous for flights scheduled in April and represent already a significant proportion of the flight cancellations originally scheduled in May and June. Naturally, all cancellations made for flights scheduled after beginning of July were with more than 4 weeks in advance.
For cancellations of flights originally scheduled in July, August, September and October 2020, the graphic clearly shows that airlines are still deciding on cancellations on a monthly basis.[:fr]
Microsoft Power BI file content :
Nota: The anticipation of cancelations concerns individual slots and not the whole series. In other words, and as an example, for a series covering the whole season that is canceled at the beginning of the season the first date would be canceled with no anticipation and the last date of the season for that series with the whole season anticipation)
The graphic in tab 2 shows the horizon of cancellations per month. Each chart shows (one chart per month) the number of cancellations (y-axis) per anticipation of cancellation (in number of days, x-axis) –
The chart in tab 1 shows the horizon of cancellations per date of operation.
For every date shown on the horizontal axis, it shows the number of slots that were originally scheduled on this date and cancelled:
A – On the date of operation
B – Between 1 and 7 days in advance
C – Between 8 and 14 days in advance
D – Between 15 and 21 days in advance
E – Between 22 and 28 days in advance
F – Between 29 and 59 days in advance
G – More than 60 days in advance
It is possible to highlight any of the curves by clicking on it.
The data are aggregated for the following list of airports. There is one chart for all airports combined (tab 1) and one per airport (starting on tab 3).
CDG – Paris Charles de Gaulle FR
LYS – Lyon FR
NCE – Nice FR
NTE – Nantes FR
ORY – Orly FR
For all these airports, data were aggregated for cancellations requested for flights scheduled between 1st of March and the end of Summer 2020 IATA season and flights scheduled for the first week of Winter 2020 IATA season.
All data were extracted by slot coordinators as of October 31st.
Highlights:
The graph essentially shows that:
– Very late cancellations, less than one week (cat A and B), were requested by airlines for flights scheduled in March and in very few cases scheduled for later periods;
– Late cancellations, between one and two weeks (cat C), were requested by airlines mainly for flights scheduled at the beginning of April and also at a lower scale every 2 weeks from mid April until now;
– Cancellations made 3 or 4 weeks in advance (cat D and E) represent a very large amount of all cancellations for flights scheduled mid April and at the beginning of May and June (more than 50%);
– Early cancellations (cat F) were almost non-existent for flights scheduled in March, not numerous for flights scheduled in April and represent already a significant proportion of the flight cancellations originally scheduled in May and June. Naturally, all cancellations made for flights scheduled after beginning of July were with more than 4 weeks in advance.
For cancellations of flights originally scheduled in July, August, September and October 2020, the graphic clearly shows that airlines are still deciding on cancellations on a monthly basis.[:]