Using GPS signal, determine is this person driving a cab
Take the taxi routes and combine them with civilian car routes to form a data set for classification. Using a map (say, from Google)
 break down each route into a sequence of roads segments, from intersection to intersection. If you only have GPS traces this will involve spatio-temporal segmentation. (Intersections/terminuses are places where cars go but stop at, and the road segments are the places cars move through to get to from one intersection to another). Model these as categorical variables (road segment 1, 2, 3, etc); abstract out the physical location. Then train a classifier than accepts a sequence as input (e.g., a recurrent neural network). Use the time of departure as another feature, modeled as two real variables, $\cos(2*\pi*t/24), \sin(2*\pi*t/24)$, where $0&lt;t&lt;24$. If you have really accurate GPS information, I would also try to estimate the rate of lane crossing; taxi drivers are well known for their aggressive driving, and the rate of lane crossing would capture this well. Finally, use a pair of boolean variables to record whether the points of departure and destination are parking areas, if you can obtain this information. I imagine taxis will be more likely to start and stop at prohibited places.

The rest is your usual hyper-parameter optimization black magic.

If you want to be able to do anything besides classify a given route as taxi-driven or not, please say so.