Today, the transport sector stands for 25% of global carbon dioxide emissions, a number that has to go down drastically to reach the Paris Agreement. To this end, many initiatives are ongoing: to rethink the need, to adapt behaviour, to change fuel, etc. Connected car data is a surprisingly untapped resource, and machine learning based on it is a crucial tool for making many of these mobility initiatives sustainable. Using vehicle data in the right way has a huge potential, which we aim to explore, to decouple pollution and CO2 emissions from the mission of providing the necessary mobility for all. Innovative transport solutions require accurate insights as input to decision-makers. However, car manufacturers lack detailed knowledge of real-world usage for the vehicles they produce; owners and drivers are confused about the consequences the decisions they make will have in their particular context, both for sustainability and economy; fleet operators provide inadequate arrangements and inefficient management due to lack of understanding of their distinct needs. All these actors can benefit from the data of millions of connected vehicles once it is analysed. A pipeline from a large-scale car usage data lake maintained at WirelessCar into novel machine learning algorithms developed at Halmstad University was used to help develop services that can lead to sustainable and efficient resource utilisation, while at the same time being realistic in terms of convenience and cost.