Computing Sustainable Policies for Online Ridesharing
With the growing popularity of the shared economy, ridesharing services are called to transform urban mobility. Indeed, shared mobility is expected to have major environmental and economic impacts by reducing pollution (e.g. CO2 emissions and noise pollution), traffic congestion, and energy consumption]. Furthermore, ridesharing is said to become even much more attractive in a future world of self-driving cars, and spur a transition from solo driving to mass transit. Along these lines, the growing efforts in research and technology, together with novel incentive policies for shared mobility and efficient market designs will play a crucial role for the exploitation of the full potential of ridesharing, and for the revolution of the current practice in personal transportation. Despite its major potential benefits, ridesharing is nowadays still far from being widely used (according to the US Census Bureau, only around 9.3% of commuters in the US carpooled to work, compared to 76.4% who drove alone). Because of the growing concerns about climate change, congestion, and oil dependency, further research is needed to understand the benefits of ridesharing, while also considering time and privacy requirements of users. One of the reasons of this insufficient engagement by the public is the lack of effective incentive policies by regulatory authorities, who do not possess the necessary computational tools capable of quantifying the costs and the benefits of a given ridesharing adoption policy. During my project I addressed these issues by (i) developing a novel algorithm that makes large-scale, realtime ridesharing technologically feasible; and (ii) exhaustively quantifying the impact of different ridesharing policies in terms of environmental benefits (i.e. reduction of CO2 emissions, noise pollution, and traffic congestion) and quality of service (QoS) for the users. Our analysis on a publicly available real-world dataset shows that major societal benefits are expected from deploying sustainable ridesharing policies, depending on the trade-off between environmental benefits and QoS. Thus, when environmental benefits are prioritised, our approach can yield a 70.78% reduction in CO2 emissions (corresponding to up to 107.18 Tons per day) and a 80.08% reduction in traffic congestion, while achieving full occupancy on shared rides. Notice that our approach also stands as valuable computational tool for policy makers concerned with shared mobility. Our approach provides the means to estimate the expected societal benefits of ridesharing from existing travelling data records. Thus, policy makers can leverage on our approach in order to come up with action plans aimed at achieving target societal impacts within some time frame, which is the common practice in policy making (e.g. refer to the New York State Energy Plan).
Synergistic Team Formation Policies for Cooperative Learning
Similarly, modern educational institutions can implement cooperative and active learning policies that engage students in teams to participate in all learning activities in the classroom. Nonetheless, not all teams facilitate learning. For cooperative learning to be effective, students have to be grouped by carefully taking into account their competencies, personality traits, and gender. Several studies in the literature show a positive correlation between certain personality traits and team composition, and that, in order to increase team performance, team members should be heterogeneous in their individual characteristics. Even though much research in the industrial, organisational, and educational psychology fields investigated what the predictors of team success are, to the best of our knowledge, there are no computational tools that allow to form groups so as to exploit the synergies among members and to ensure that competences are adequately combined to complete a given task. To address this shortcomings, we developed an AI optimisation algorithm that takes into account personality, competence, and gender profiles of the students, and groups them into teams guaranteeing that the synergies among members are maximal. Our experimental evaluation with actual students showed that teams formed according to our approach scored marks that were, on average, 25.3% higher than the ones formed by experts, hence effectively improving the performance of the students.
Dissemination of Results
The results on shared mobility have been submitted and are currently under review in one of the most prestigious academic journal on transportation, i.e. IEEE Transactions on Intelligent Transportation Systems. In the context of cooperative learning, our paper "Synergistic Team Composition: A Computational Approach to Foster Diversity in Teams" has been published in the very prestigious journal "Knowledge-Based Systems". Furthermore, all mentioned algorithms are accessible via modern web interfaces to facilitate the process of policy computation. Specifically, EduTeams is a free cooperative learning tool (
http://eduteams.iiia.csic.es(se abrirá en una nueva ventana)) that has already been used in schools in Barcelona. A simulation tool for online large-scale ridesharing (
http://ridesharing.iiia.csic.es(se abrirá en una nueva ventana)) has been developed as a final result of my MSCA project, which has also been demonstrated at one of the top AI conferences (AAMAS) [“A Simulation Tool for Large-Scale Online Ridesharing”, AAMAS, 2018].
