NB: A version of this blog post first appeared as a special guest post on Tnooz.
As we continue to improve the Rome2Rio multi-modal search technology, we are starting to integrate pricing data into the system to help make sensible routing decisions and better inform our users. After all, price is an important part of the decision process when choosing between routes or modes of transport.
Prices for trains, buses, ferries and taxis tend to be more constant than airfares, which fluctuate with supply and demand. However, airfares do follow certain obvious trends; longer flights cost more, and some airlines are more expensive per mile flown than others.
We decided to model airfares using some simple parameters. To do this, we examined the economy class airfares displayed by Rome2Rio to users over the past 4 months, totalling some 1,780,832 price points. We grouped the airfares by distance and selected the 20th percentile fare for each distance (where 20% of fares are less, and 80% are more), to produce the following graph:
The graph shows a pretty clear linear relationship between distance traveled and airfares. Based on this data, we can create a simple equation to model this relationship:
Fare = $50 + (Distance * $0.11)
Where Fare is the cost in USD of flying Distance miles. On average, a fare costs $50 before any flight distance is taken into account, plus an average of 11 cents per mile travelled.
So what happens if we divide our data by airline? How does the 11 cents per mile flown vary per carrier?
We analyzed the average cost per mile for fares grouped by airline, using the same methodology. We only considered competitive fares – those within 2 times the cheapest fare for that price search – to remove outlier price points. We also excluded airlines where we had insufficient data.
The results are summarized below (original image):
The results are fascinating, and there are some clear trends. Budget carriers such as RyanAir and AirAsia are at the low end of the scale; short haul, turboprop operating carriers such as Regional Express and Darwin Airlines are at the high end.
There are, however, many factors which can influence per mile costs including type of aircraft flown, routes flown, local salary and fuel costs, ancillary revenue, and airport landing fees.
The results should also be taken with a grain of salt, since our sampling set is small, no statistical analysis has been performed, and the results may be biased depending upon the types of searches performed on Rome2Rio. Also, Rome2Rio may not always have access to the cheapest fares. A major, comprehensive meta-search player such as Kayak or Skyscanner could perform a more thorough analysis based on a far greater sample of search logs or their airfare caches. Nonetheless we wanted to share this data since we thought the results would be of interest to the travel industry, travel buffs, or anyone excited about big data.
Credit: Special thanks to Fenn Bailey from Adioso and Timothy O’Neil-Dunne for providing valuable feedback on the analysis.
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