Articolo in rivista, 2017, ENG, 10.1016/j.trc.2017.02.025
Berlingerio M.; Ghaddar B.; Guidotti R.; Pascale A.; Sassi A.
IBM Research Ireland, Dublin, Ireland; University of Waterloo, Waterloo, Canada; University of Pisa, Pisa, Italy; CNR-ISTI, Pisa, Italy; University of Modena and Reggio Emilia, Modena and Reggio Emilia, Italy
Carpooling, i.e. the sharing of vehicles to reach common destinations, is often performed to reduce costs and pollution. Recent work on carpooling takes into account, besides mobility matches, also social aspects and, more generally, non-monetary incentives. In line with this, we present GRAAL, a data-driven methodology for GReen And sociAL carpooling. GRAAL optimizes a carpooling system not only by minimizing the number of cars needed at the city level, but also by maximizing the enjoyability of people sharing a trip. We introduce a measure of enjoyability based on people's interests, social links, and tendency to connect to people with similar or dissimilar interests. GRAAL computes the enjoyability within a set of users from crowd-sourced data, and then uses it on real world datasets to optimize a weighted linear combination of number of cars and enjoyability. To tune this weight, and to investigate the users' interest on the social aspects of carpooling, we conducted an online survey on potential carpooling users. We present the results of applying GRAAL on real world crowd-sourced data from the cities of Rome and San Francisco. Computational results are presented from both the city and the user perspective. Using the crowd-sourced weight, GRAAL is able to significantly reduce the number of cars needed, while keeping a high level of enjoyability on the tested data-set. From the user perspective, we show how the entire per-car distribution of enjoyability is increased with respect to the baselines.
Transportation research. Part C, Emerging technologies 80 , pp. 20–36
Carpooling, Social networks, Multi-objective optimization, User study
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 382556
Year: 2017
Type: Articolo in rivista
Creation: 2018-01-16 18:31:45.000
Last update: 2021-04-08 19:59:18.000
CNR authors
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1016/j.trc.2017.02.025
URL: https://www.sciencedirect.com/science/article/pii/S0968090X17300657
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:382556
DOI: 10.1016/j.trc.2017.02.025
Scopus: 2-s2.0-85018589160
ISI Web of Science (WOS): 000403989800002