Airlines are concerned for route development, but if there is no business for certain routes/city, there will be no operation to that city/airport. While airports acts as facilitators for airlines to encourage them to operates for new routes or increase their frequencies. also airports offered a good service at a most convenient cost for airlines. That is why each airport in the world is always concerned about the route development, they are publishing the airlines traffic/statistics on monthly bases, exploring the future business potential of existing/operating routes.

1. Setting Targets
For
Montreal YUL and Paris CDG Route
Route Development
YUL-CDG
By: Mohammed Salem Awad
AviationConsultant
Data Source: http://ec.europa.eu/eurostat/data/database

2. 2
Route Development - YUL-CDG
"The task of Airlines that done by Airports"
Route development is always governed by two main
factors, Supply and Demand, Supply in terms of capacity
offered i.e Seats, Demand in terms of Traffic Demand i.e
Passengers.
So Airlines are concerned for route development, but if there is no business for certain
routes/city, there will be no operation to that city/airport. While airports acts as facilitators
for airlines to encourage them to operates for new routes or increase their frequencies. also
airports offered a good service at a most convenient cost for airlines. That is why each
airport in the world is always concerned about the route development, they are publishing the
airlines traffic/statistics on monthly bases, exploring the future business potential of
existing/operating routes.
However, the principle of the route development is based on the annually and monthly trends
of Demand (Passengers) and Supply (Seats). i.e the basic idea is to define the future
patterned of these two inputs – passengers and seats (as a time series), in other words we
have to predict the behavior of the these factors which consequently lead us to the term of
Forecasting.
Therefore, the general rule in forecasting for Goodness of Fit is to accept the result when R-
square is greater than 80%. However, that does not create sound in practice, as it is subject to
the selection of the analysist decision, which comes with different results above 80 % that
gives different answers. So to avoid that we have to set a specified figure as a Target.
Input Data:
The basic data is available from Eurostat – European Commission, Air Transport section, for
a period Jan 2013 to June 2017, per country, per airport, for Passengers and Seats at total
level and on monthly bases.
Setting Annual Targets:
The best way to set up annual target and minimize the data discrepancy is to address the data
by two trend models using the concept of 12 months rolling method for Passengers and
Seats.
Here we implement two trend models by using Add a trend line in XLS sheet:
First – General Trend Model using the concept of Straight Line equation.
Second – Most Recent Data Trend Model Using a Polynomial Model – Second-degree
equation. This reflects the impact of most recent data on the path of general trend. The mid-
point is the most convenient forecast annual result at Dec 2018. As shown in graphs.
Predicting Route Performance (2018) - Annually
Annual route performance is calculated by L/Factor = Paxs/Seats.
So for 2018
2018 Paxs = 1,189,911 Pax. At R2 = 87.42 %.
2018 Seats = 1,300,672 Seat. At R2 = 89.14 %.
Route Performance (L/Factor = Paxs/Seats) for 2018 = 85.44 %.
Since Growth Seat is greater than GrowthPax , this is a diverge case.
i.e the gap become wider and wider tends to lowering the performance level
By: Mohammed Salem Awad
AviationConsultant
Data Source: http://ec.europa.eu/eurostat/data/database

3. 3
As long as the gap between two models is small, the more accurate approaching value for
setting annual target (Dec 2018) otherwise we have to select the half way distance between
two extreme targets of these models. As shown above
Predicting Route Performance (Monthly):
Three possible output we can get when we forecast supply and demand.
The first possible output when the growth of traffic demand is greater than the growth of
capacity offered. This led us to converge case, as the gap becomes narrow and narrow (good
performance in future).
The second possible output when the growth of traffic demand is lesser than the growth of
capacity offered. This led us to diverge case, as the gap becomes wider and wider (poor
performance in future).
The third possible output when the growth of traffic demand is almost equal to the growth of
capacity offered. This led us to leveling case, as we have a constant gap (and the
performance will sustain its pervious figures to be in the future or keeping the gap between
them at constant distance).
So the best way is to set a targets ( define figures to be achieved ) and that can be done by
top – down approach first for annual target then for monthly targets, that’s fulfil the first
condition (Annual Target).
YUL- CDG - Getting the Complete Picture (Monthly):
Based on input data - three years database, July-2014 to June 2017 (total traffic), two
monthly forecasted models are setup for Passengers and Seats, it is head-to-head analysis for
passengers and seats for actual values and forecasted one. Both model fits fairly, as shown
below

4. 4
Results:
The study shows the highest performance for route
YUL-CDG – total traffic in 2018 will be 91.74 %
at month of Oct while the lowest will be 74.85 %
in Nov while the annual performance will be 85.44
%. Since the growth of seat is greater than growth
of passengers, this will lead to increase the gap and
consequently poor performance in future (diverge
case between Passengers and Seats), in spite of
good performance recorded. In general, the route
shows high performance.