  Ride-sharing on the daily home-work-home commute can help individuals save on
gasoline and other car-related costs, while at the same time it can reduce
traffic and pollution. This paper assesses the potential of ride-sharing for
reducing traffic in a city, based on mobility data extracted from 3G Call
Description Records (CDRs, for the cities of Barcelona and Madrid) and from
Online Social Networks (Twitter, collected for the cities of New York and Los
Angeles). We first analyze these data sets to understand mobility patterns,
home and work locations, and social ties between users. We then develop an
efficient algorithm for matching users with similar mobility patterns,
considering a range of constraints. The solution provides an upper bound to the
potential reduction of cars in a city that can be achieved by ride-sharing. We
use our framework to understand the different constraints and city
characteristics on this potential benefit. For example, our study shows that
traffic in the city of Madrid can be reduced by 59% if users are willing to
share a ride with people who live and work within 1 km; if they can only accept
a pick-up and drop-off delay up to 10 minutes, this potential benefit drops to
24%; if drivers also pick up passengers along the way, this number increases to
53%. If users are willing to ride only with people they know ("friends" in the
CDR and OSN data sets), the potential of ride-sharing becomes negligible; if
they are willing to ride with friends of friends, the potential reduction is up
to 31%.
