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The mean of all the tracks may not be correct as you said, but it’s going to be correct for people using gps’s as it’s the average gps track. Although the intention should just be to get it ‘really correct’ rather than just suitable for gps’s. You also would have to consider the value of each track though, so that tracks recorded from a slow moving source where more influential on averaging.

But… What I tend to find is as I go through an area of bad reception, is that the path becomes more and more off, but retains the general shapes/curves of the route. If I did the route many times from the same point the tracks would gradually fan out.

Then If it is particularly bad I will take that route from the other angle. This will give good reception at both ends. I can draw the shape from the routes from each angle. Rotate them to line up with the accurate readings at both ends, and draw the mean. (I wonder if the electronic compass helps here?)

Another give away is the spacing of the dots. Firstly how frequent they are all the way along…i.e. in a car or walking. Walking will give better data. But secondly looking back to see if it has kept a fixed signal. So If you have 10 routes scattered around where the road goes, stick with the route that consistently retains it’s usual frequency.

This is all guess work, it’s not guaranteed, or a perfect formula which your (ric) post seem to suggest your looking for, but using this method I think most roads are relatively accurate.

For areas like woods though which I have found to be the worst, I think the best option is just to map them around this time of year. Avoid having to understand a splodge of dots, by getting the data before the leaves come out.