In this work we consider the design of a Free Floating Car Sharing (FFCS) system based on Electric Vehicles. We face the problems of finding the optimal placement of charging stations, and the design of smart car return policies, i.e, how many and where to place charging stations, and whether to ask or not customers to return the car to a charging pole. We leverage actual data containing rentals performed by Car2Go customers. We obtain information for several months worth of actual trips in the city of Turin, our use case. Via trace driven simulations, we replay the exact same trips while simulating electric car based FFCS, to accurately gauge battery discharging and recharging. With this, we compare different charging station placements, also driven by optimisation algorithms. Moreover, we observe the impact of collaborative or selfish car return policies. Results are surprisingly: just as few as 13 charging stations (52 poles) guarantee a fleet of 377 vehicles running in a 1 million inhabitant city to work flawlessly, with limited customer’s discomfort. We believe our data driven methodology helps researchers and car sharing providers discerning different design solutions. For this, we make available all data and tools to foster further studies in these directions.
Free floating electric car sharing design: Data driven optimisation / Cocca, Michele; Giordano, Danilo; Mellia, Marco; Vassio, Luca. - In: PERVASIVE AND MOBILE COMPUTING. - ISSN 1574-1192. - ELETTRONICO. - 55(2019), pp. 59-75.
|Titolo:||Free floating electric car sharing design: Data driven optimisation|
|Data di pubblicazione:||2019|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.pmcj.2019.02.007|
|Appare nelle tipologie:||1.1 Articolo in rivista|