Home-work carpooling for social mixing
Name
Home_work_Carpooling_for_Social_Mixing_Preprtint.pdf
Description
Accepted version
Size
437.32 KB
Format
Adobe PDF
Checksum (MD5)
e6ca817a79a99ff11cda7d2c46fcf9ff
Author(s)
Librino, Federico
Renda, Maria Elena
Santi, Paolo
Martelli, Francesca
Resta, Giovanni
Duarte, Fábio
Ratti, Carlo
Zhao, Jinhua
Date Issued
July 2019
Journal
Transportation
Publisher
Springer Science and Business Media LLC
Citation
Librino, Federico et al. "Home-work carpooling for social mixing." Transportation (July 2019): doi.org/10.1007/s11116-019-10038-2 © 2019 Springer Science+Business Media, LLC
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. SENSEable City Laboratory
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s11116-019-10038-2
