TítuloA Computational Approach to Quantify the Benefits of Ridesharing for Policy Makers and Travellers
Publication TypeJournal Article
Year of PublicationIn Press
AuthorsBistaffa F, Blum C, Cerquides J, Farinelli A, Rodríguez-Aguilar JA
Palabras claveCollective Intelligence, environmental benefits, integer linear programming, online stochastic combinatorial optimisation, policy making, Ridesharing, smart cities

Peer-to-peer ridesharing enables people to arrange
one-time rides with their own private cars, without the involve-
ment of professional drivers. It is a prominent collective intel-
ligence application producing significant benefits both for indi-
viduals (reduced costs) and for the entire community (reduced
pollution and traffic). Despite these very promising poten-
tial advantages, the percentage of users who currently adopt
ridesharing solutions is very low, well below the adoption rate
required to achieve said benefits. One of the reasons of this
insufficient engagement by the public is the lack of effective
incentive policies by regulatory authorities, who are not able
to estimate the costs and the benefits of a given ridesharing
adoption policy. Here we address these issues by (i) developing
a novel algorithm that makes large-scale, real-time peer-to-
peer ridesharing technologically feasible; and (ii) exhaustively
quantifying the impact of different ridesharing scenarios in
terms of environmental benefits (i.e., reduction of CO 2 emissions,
noise pollution, and traffic congestion) and quality of service
for the users. Our analysis on a real-world dataset shows that
major societal benefits are expected from deploying peer-to-peer
ridesharing depending on the trade-off between environmental
benefits and quality of service. Results on a real-world dataset
show that our approach can produce reductions up to a 70.78%
in CO 2 emissions and up to 80.08% in traffic congestion.