Monday saw me driving 800 miles home from a family Thanksgiving celebration. Due to my wife's change in plans, my only companion for the drive was our small dog (
who had a narrow escape the day before). I needed something to keep my attention. So I decided to perform an experiment in data collection. I learned a lot even from a small data sample.
The vehicle I was driving was a 2010 Subaru Forester. Some friends have the same vehicle and have been pleased with getting around 27 MPG on the highway. We typically get only 23-24 MPG on the highway and I had been wondering why.
Among the features of this car is an average gas mileage display that's tied to the trip odometer. So, sampling the gas mileage is as simple as setting the cruise control, resetting the trip odometer, driving a set distance and reading out the result. As I was crossing the relatively flat plains of Idaho (speed limit 75) this seemed to be a good opportunity to gather some data.
Over a period of several hours, I took a bunch of samples following the above method and using my GPS to track altitude changes. I abandoned samples where the altitude change was more than a few hundred feet. The result is 27 good samples.
I've posted the raw data here in case you want to play with them. Most of the samples are for 20 mile segments but some are as long as 40 and some are as short as 5 miles.
As you can imagine, the lower-speed samples got a bit tedious. But I was curious enough that I even took a side trip on a remote road (off the freeway) to get samples below 45 MPH. There's considerable variability in those results as you can see in the plot below. Halfway through the trip I refilled with fuel. I switched from regular (87 octane) to premium (92 octane) to see how that might affect mileage.
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| 2010 Subaru Forester Fuel Economy vs. Speed |
The results certainly aren't what I expected. EPA city and highway ratings have always led me to expect relatively flat miles per gallon with city being much poorer due to stop-and-go driving. Instead, I got a nearly linear downward slope. Using Excel's curve-fitting feature and found that a polynomial curve worked better than a line. The formula is embedded in the graph above.
More data points would be required to really validate this curve but it certainly fits within the margin of error of my samples. Therefore we can make some cost estimates using this formula. Notable is that peak economy is between 40 and 45 MPH – much slower than I had expected. From this I was able calculate the cost of each hour saved on this long drive.
Here's a table of results for a 800 mile drive in a 2010 Subaru Forester with average fuel cost of $3.759 per gallon. Time saved and additional cost are from a baseline speed of 55 MPH. Note that the distance is cancelled out in the Cost Per Hour Saved so those numbers are accurate regardless of the length of the trip.
| Speed | MPG | FuelCost | Time | Hrs
Saved | Addl
Cost | Cost Per
Hr Saved |
|---|
| 55 | 33.1 | $90.83 | 14.55 | | | |
| 60 | 32.1 | $93.71 | 13:33 | 1.21 | $2.89 | $2.38 |
| 65 | 30.7 | $97.88 | 12.31 | 2.24 | $7.05 | $3.15 |
| 70 | 29.0 | $103.65 | 11.43 | 3.12 | $12.82 | $4.11 |
| 75 | 27.0 | $111.55 | 10.67 | 3.88 | $20.72 | $5.34 |
| 80 | 24.6 | $122.45 | 10.00 | 4.55 | $31.62 | $6.96 |
| 84 | 22.4 | $134.31 | 9.52 | 5.02 | $43.48 | $8.66 |
Here are a few things I've learned from this:
- My friend with the other Forester drives slower on the highway than I do.
- I had not known how sensitive vehicle gas mileage is to speed.
- Everything I have read led me to expect no benefit from higher octane fuel once the vehicle's requirements have been met. In the case of the Forester, higher octane actually reduced fuel economy. This observation is confirmed by the official EPA ratings.
- I would love to see tables like the one above before purchasing my next car.
I learned a lot from this tiny data sample and my future driving habits will be changed accordingly. Now imagine what we could learn if there were a large public database of fuel economy data. Car manufacturers could optimize for specific driving patterns. Consumers would be better informed about fuel economies to expect. There are
fleet tracking devices like this one that are already reporting that data but it's locked up in private databases. If anonymous fuel economy data (speed, distance, altitude and MPG) were released there's a lot we could learn about fuel economy under a variety of conditions.
I can't wrap this post up without relating it to education. A relatively small data sample taught me a lot and will impact my future driving behavior. In the same way, it doesn't take a lot of data fed back to students and teachers before they see opportunities to improve. And when we grow from little data to
big data, revolutionary changes are on the horizon.
The first is the Four-Layer Framework for Data Standards. This framework has helped guide decisions about the Common Education Data Standards – what should be the scope and how CEDS should relate to other standards in the space. However, the framework is not limited to education standards. Any organization that's developing specifications for the exchange of data should think of these four layers and try to describe each part semi-independently.
These education standards are not exclusively data standards. Academic Standards, which include Achievement Standards and Competency Standards describe skills that students should be able to demonstrate as they achieve certain levels of education. Nevertheless, there are data standards for describing Academic Standards and for aligning content to those standards.
