HighlightsCarpooling problem as multiobjective optimization of number of cars and enjoyability.Weights for the multiobjective optimization learned by means of a user study.Extraction of enjoyability and mobility demand from data like Twitter.Results on real world data, from the perspectives of the city, and the users.GRAAL can save up to 57% of the cars, while the total enjoyability is up to double.From the user perspective the total per-car distribution of enjoyability is increased.
AbstractCarpooling, 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.
The GRAAL of carpooling: GReen And sociAL optimization from crowd-sourced data
2017-02-28
17 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch