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Route Performance : YUL - CDG

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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.

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Route Performance : YUL - CDG

  1. 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. 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. 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. 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.

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