How can a network of electric charging points combined with street parking be rolled out to optimal effect to complement underground park-and charge facilities?
The combined effect of new regulations and greater environmental awareness mean that the pool of electric vehicles in Paris will expand fifteen-fold by 2025. However 20% of Parisians park their vehicles (cars and motorbikes) in the street. There is therefore a significant and pressing need for kerbside charging facilities to support and foster this transition. But technical and economic studies prior to the deployment of charging points are currently very time-consuming and delay the rollout of this infrastructure.
of electric vehicles
in Paris by 2025
Bouygues Energies et Services, which specialises in electric vehicle infrastructures, was therefore keen to work with the City of Paris and new mobility experts at Sopra Steria and ALD Automotive to develop a solution to automate site studies for charging points and thus to accelerate rollouts. The aim of the challenge, in partnership with the startup Wintics, which specialises in data analytics, was to develop an interactive mapping solution to identify optimal sites for charging points. The underlying algorithm identifies sites by integrating a number of types of technical data relating to spatial constraints in the public space and various socioeconomic indicators which reflect potential usage of charging points.
Wintics develops AI solutions for Smart Cities. These solutions focus on three themes: mobility, urban planning and building management. Wintics has demonstrated the effectiveness of its algorithms on a number of occasions by winning international competitions relating to topics such as predictive maintenance of transport infrastructures and identifying excess energy consumption in buildings.
Experimentations have made it possible to develop a tool which can (i) analyse the position of items on the pavement (benches, lamp posts, trees, etc.) and kerbside parking area use (deliveries, Autolib car sharing, etc.) based on 12 activity parameters in order to identify available sites for charging points and (ii) predict potential use based on four socio-economic criteria (purchasing power, car use, existing EV ownership, and environmental sensitivity). An interactive map displays the optimal locations for charging points. The tool can also accommodate all types of constraint in order to adapt to different urban infrastructures and thus streamline the work of urban planners and save them time by responding to real needs in the local area.