If you don't mind me asking, roughly what frequency threshold have you found the algorithm to perform badly above, and are you aware of any algorithms or formulae which perform better in these situations?
This means that if the path between two points is not simple (around a corner) the probability drops off very quickly. If the time between measurements is in minutes, this heuristic is pretty useless (and you should really use log-scale for your numbers!)
edit: this is actually shown in figure 8 of the paper where they explore different 'sampling periods'
edit 2: I have not explored other methods yet, but it would probably make sense to start by deriving the formula the way they do, by exploring ground-truth data.
edit 3: I just noticed that my comments are largely repeating what you're saying - sorry!
Agreed, the log scale is really important to avoid arithmetic underflow =] I believe OSRM and Graphhopper both do it that way. In my implementation I've flipped from thinking of measurement/transition "probabilities" to "disparities", and I choose the final route that has the least disparity from the trail. It seems to handle trails with around a 30-60s frequency over a 5-10hr period with decent accuracy.
As with you, I have found that it still often gives ok results with slower frequencies, as long as the transition probabilities are still relatively in the same scale as each other for a particular observation pair. However it means that there's no point trying to 'tune' using the gamma and beta parameters