Data-driven estimation of inclination towards the use EV-sharing systems
Tesi esterna in azienda
Parole chiave DRIVING-STYLE ESTIMATION, MACHINE LEARNING; REAL-T
Riferimenti FABRIZIO DABBENE
Descrizione The thesis will focus on the viability of a large-scale diffusion of vehicle-shared mobility.
More precisely, we are interested in using objective data (driving-style, driving range, charging time, relative fuel prices, consumer characteristics, availability of charging stations, ...) and traditional surveys on people preferences to estimate (a) their inclinations to passing up car ownership in favor of car membership (b) inclinations towards the adoption of an electrical-vehicle.
The candidate will be asked to develop strategies and algorithms to extract the most relevant features of each aspect of the problem. Besides methods derived from inferential statistics (discriminant analysis, density reconstruction via kernel functions, nearest-neighbor, spectral clustering, etc.), the thesis will focus on machine learning techniques, such as support vector machines, clustering algorithms, rule generation methods.
Conoscenze richieste Basic mathematical modeling, Basic signal processing, Convex optimization, Basic coding skills (MATLAB is
Note The thesis will be carried out under the supervision of the staff of the System and Modeling Control Group at the Institute of Electronics, Computer and Telecommunication Engineering of National Research Council of Italy (IEIIT-CNR), Prof. Mara Tanelli of Politecnico di Milano (DEIB) and other members of the MoVE group of the DEIB (www.move.deib.polimi.it), and in collaboration with the Rulex, Inc. (www.rulex.ai).
Scadenza validita proposta 20/11/2019 PROPONI LA TUA CANDIDATURA