|Title||Home-work Carpooling for Social Mixing|
|Publication Type||Journal Article|
|Year of Publication||2019|
|Authors||Federico Librino, Elena Renda, Giovanni Resta, Paolo Santi, Fabio Duarte, Carlo Ratti, Jinhua Zhao|
Shared mobility is widely recognized for its contribution in reducing carbon footprint, trafﬁc congestion, parking needs and transportation-related costs in urban and suburban areas. In this context, the use of carpooling in home-work commute is particularly appealing for its potential of lessening the number of cars and kilometers traveled, consequently reducing major causes of trafﬁc in cities. Accordingly, most of the carpooling algorithms are optimized for reducing total travel time, cost, and other transportation-related metrics. In this paper, we analyze carpooling from a new perspective, investigating the question of whether it can be used also as a tool to favor social integration, and to what extent social beneﬁts should be traded off with transportation efﬁciency. By incorporating traveler’s social characteristics into a recently introduced network-based approach to model ride-sharing opportunities, we deﬁne two social-related carpooling problems: how to maximize the number of rides shared between people belonging to different social groups, and how to maximize the amount of time people spend together along the ride. For each of the problems, we provide corresponding optimal and computationally efﬁcient solutions. We then demonstrate our approach on two datasets collected in the city of Pisa, Italy, and Cambridge, US, and quantify the potential social beneﬁts of carpooling, and how they can be traded off with traditional transportation-related metrics. When collectively considered, the models, algorithms, and results presented in this paper broaden the perspective from which carpooling problems are typically analyzed to encompass multiple disciplines including urban planning, public policy, and social sciences.