Articolo in rivista, 2017, ENG, 10.1016/j.trc.2017.02.025

The GRAAL of carpooling: GReen And sociAL optimization from crowd-sourced data

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

Keywords

Carpooling, Social networks, Multi-objective optimization, User study

CNR authors

Guidotti Riccardo

CNR institutes

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

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