Quoting Laxintl (Reply 5): I assume you are using MSA's for the population right? Are you looking at all airports in the MSA?
For instance the Los Angeles-Long Beach-Santa Ana MSA has at 5 commercial airports LAX, BUR, LGB and SNA and PMD which just recently received service again. |
In the airport traffic figure for Los Angeles,
LAX,
SNA,
LGB and
BUR were counted.
PMD was not. These are the airports contained within the Los Angeles, Long Beach, Santa Ana metro area as defined by the US census. The populations are all from the 2006 US Census Bureau estimate.
Quoting Laxintl (Reply 5): Also how do you account for the each areas propensity of travel? For instance geographic and socia-economic reasons cause certain regions to travel more then others. For instance the SF Bay Area has one of the highest propensities for travel in the country on per-capita basis. |
There is an income effect which is the per capita income of that area. This variable actually isn't very significant in the model, but is incorporated to show a difference between income and travel propensity for passengers in Little Rock Arkansas compared to New York.
I could only use statistics for per capita income since I could not find any other data to model propensity to travel.
Quoting Laxintl (Reply 5): Also how do you correct for places that have disproportionate amount of entertainment/tourism travel. Places like Orlando obviously receive many time their population in tourist visitors. |
Yes there is a variable that corrects for tourism/vacation destinations. This is a yes or no variable. Destinations like Orlando, Miami, Las Vegas, Phoenix, etc have this extra variable included.
Quoting Bobnwa (Reply 7): Very interesting analysis. One question I have, is where does the expected passengers number come from? |
That is the basis of the model. I took data from 81 metro areas where I had USDOT information on number of passengers per year. I then wrote an equation based on the six factors that I chose to be able to predict the number of passengers. This is where the expected passengers number comes from. I then compared expected passengers to actual passengers to find out if a market is over served or underserved. I will not post my equation or data on here, but will supply it on request.
The 85% number comes from how accurately the expected values related to the true values of passenger counts. Using the R-squared value from the residuals, I found an accuracy of 85%. I can explain more on request.
Quoting Tom in NO (Reply 6): The metro population figure for New Orleans is way off.....it's closer to 1.4 million IIRC.....that combined with the fact that MSY airlines are increasing service puts this survey into the "interesting, but I'm not going to base any decisions on it" pile. |
I was surprised by the result from New Orleans as well. The population number that I used is from the 2006 estimate from the US Census Bureau. That was the most accurate number I could find for each city. If you notice though, the difference between estimated and actual traffic for
MSY is very close.
MSY is about spot on.
However in this model, New Orleans was not listed as a tourist destination. If it was, then that would show that the city is very underserved. It was a judgement decision on my part to make it that way.
If you have never designed an airplane part before, let the real designers do the work!