Abstract
The adoption of electric vehicles (EV) has to be complemented with the right charging infrastructure roll-out. This infrastructure is already in place in many cities throughout the main markets of China, EU and USA. Public policies are both taken at regional and/or at a city level targeting both EV adoption, but also charging infrastructure management. A growing trend is the increasing idle time over the years (time an EV is connected without charging), which directly impacts on the sizing of the infrastructure, hence its cost or availability. Such a phenomenon can be regarded as an opportunity but may very well undermine the same initiatives being taken to promote adoption; in any case it must be measured, studied, and managed. The time an EV takes to charge depends on its initial/final state of charge (SOC) and the power being supplied to it. The problem however is to estimate the time the EV remains parked after charging (idle time), as it depends on many factors which simple statistical analysis cannot tackle. In this study we apply supervised machine learning to a dataset from the Netherlands and analyze three regression algorithms, Random Forest, Gradient Boosting and XGBoost, identifying the most accurate one and main influencing parameters. The model can provide useful information for EV users, policy maker and network owners to better manage the network, targeting specific variables. The best performing model is XGBoost with an R 2 score of 60.32% and mean absolute error of 1.11. The parameters influencing the model the most are: The time of day in which the charging sessions start and the total energy supplied with 22.35%, 15.57% contribution respectively. Partial dependencies of variables and model performances are presented and implications on public policies discussed.

Abstract
Many transportation environmental life cycle analyses neglect the contribution of the energy supply infrastructures. In alternative light duty vehicle technologies, it has been shown through case studies that this can be a relevant factor. However, no model that can generalise the evaluation of energy and emissions from construction, maintenance and decommissioning of such infrastructure to analyse different scenarios currently exists. A model is proposed, focussing on electricity and on hydrogen supply through centralised steam methane reforming (H-2(a)) and on-site electrolysis (H-2(b)). The model outputs are in gCO(2eq)/MJ and MJ(eq)/MJ of the final energy. Model main inputs are the region's electricity mix, the annual distance driven, supply chain losses and the number of vehicles per station or chargers. The evaluation of the number of vehicles served per each charger/station as a function of annual distance driven is presented. The uncertainty is estimated by using the pedigree matrix, impact uncertainty and literature estimates. The model shows consistency in the results and uncertainty range. Charging policies that minimise the electricity infrastructure burden should incentivise approximately 37% of normal charging. H-2(a) pipeline lifetime should be extended. Efforts in the electrolyser should be undertaken to approximate the ratio of vehicles per station with a conventional one.