  Carpooling has the potential to transform itself into a mass transportation
mode by abandoning its adherence to deterministic passenger-driver matching for
door-to-door journeys, and by adopting instead stochastic matching on a network
of fixed meeting points. Stochastic matching is where a passenger sends out a
carpooling request at a meeting point, and then waits for the arrival of a
self-selected driver who is already travelling to the requested meeting point.
Crucially there is no centrally dispatched driver. Moreover, the carpooling is
assured only between the meeting points, so the onus is on the passengers to
travel to/from them by their own means. Thus the success of a stochastic
carpooling service relies on the convergence, with minimal perturbation to
their existing travel patterns, to the meeting points which are highly
frequented by both passengers and drivers. Due to the innovative nature of
stochastic carpooling, existing off-the-shelf workflows are largely
insufficient for this purpose. To fill the gap in the market, we introduce a
novel workflow, comprising of a combination of data science and GIS (Geographic
Information Systems), to analyse driver GPS traces. We implement it for an
operational stochastic carpooling service in south-eastern France, and we
demonstrate that relaxing door-to-door matching reduces passenger waiting
times. Our workflow provides additional key operational indicators, namely the
driver flow maps, the driver flow temporal profiles and the driver
participation rates.
