I have labeled GPS location data (lat,lon) for determining whether a trip is of a certain type. The location data consists of start and end points, in the format of lat,lon coordinates. A trip is labeled by bicycle or car, and this is what I'm trying to predict based on a persons previous location habits.
For example the coordinates (lat1,lon1) to (lat2,lon2) has previously always been traveled by car, whereas (lat1,lon1) to (lat3,lon3) is usually traveled by bicycle. Hour of day data is also available which I think also could be used to predict. The city in which coordinations are collected in is small, so distance would not be a good indicator of the trip type.
I have tried to feed the start location, end location and hour of day with the label into an NN, but without results. I guess this is because lon,lat coordinates are only useful as a pair and not as independent parameters?
(A4,B7) -> Bicycle
(A4,B8) -> Car
(B7,A4) -> Car
Will the network be able to detect patterns in the data? Or is a neural network a bad idea and should I instead go for an alternative approach?