A Machine-Learned Ranking Algorithm for Dynamic and Personalised Car
  Pooling Services
Mattia Giovanni Campana Franca Delmastro Raffaele Bruno
Car pooling is expected to significantly help in reducing traffic congestion
and pollution in cities by enabling drivers to share their cars with travellers
with similar itineraries and time schedules. A number of car pooling matching
services have been designed in order to efficiently find successful ride
matches in a given pool of drivers and potential passengers. However, it is now
recognised that many non-monetary aspects and social considerations, besides
simple mobility needs, may influence the individual willingness of sharing a
ride, which are difficult to predict. To address this problem, in this study we
propose GoTogether, a recommender system for car pooling services that
leverages on learning-to-rank techniques to automatically derive the
personalised ranking model of each user from the history of her choices (i.e.,
the type of accepted or rejected shared rides). Then, GoTogether builds the
list of recommended rides in order to maximise the success rate of the offered
matches. To test the performance of our scheme we use real data from Twitter
and Foursquare sources in order to generate a dataset of plausible mobility
patterns and ride requests in a metropolitan area. The results show that the
proposed solution quickly obtain an accurate prediction of the personalised
user's choice model both in static and dynamic conditions.