AI-research project for sustainable mobility

The project is a collaboration with the Center for Applied Intelligent Systems Research (CAISR) at Halmstad University, represented by Slawomir Nowaczyk, Professor of Machine Learning; and it is partially funded by Vinnova, Sweden’s innovation agency within the call Fordonsstrategisk forskning och innovation, Effektiva och uppkopplade transportsystem.

A data-driven approach can visualise mobility patterns and give new insights

Using a data-driven approach, the FREEDOM project’s objective is to find new insights and create digital services that will help shape sustainable mobility offers for car makers. More specifically, WirelessCar and its project partner aim to identify common travel patterns of both vehicles and people, and to quantify the crucial factors affecting the efficiency of the whole system.

Challenges facing the transport sector

Passenger cars account for nearly 41% of the global CO2 emissions from the transport sector, making them one of the most significant challenges facing cities today. It is also an excellent opportunity. Society is witnessing a shift in attitude towards solutions that both protect the planet and improve people’s lives. However, innovative transport solutions require accurate insights as input to decision-makers, and the FREEDOM project aims to make those data-based insights more accessible.

Slawomir Nowaczyk, professor of Machine Learning at Halmstad University: “Using vehicle data in the right way has huge potential, which we aim to explore—to decouple pollution and CO2 emissions from the mission of providing necessary mobility for all. Many different actors will benefit from the data of millions of connected vehicles once it is analysed in the FREEDOM project.”

Using connected car data in a wider scope will pave the way

Natalie Lucca, Product Owner Data & Analytics at WirelessCar and one of the project creators: “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. Machine learning algorithms will help develop services that lead to sustainable and efficient resource utilisation, while at the same time being realistic in terms of convenience and cost.”

To make a significant contribution to the goal of sustainable mobility will require the meeting and cross-pollination of the following: technology development, studies of user motives, needs and prime movers, and value creation for different stakeholders—commercially as well as socially and environmentally.

One important output of the FREEDOM project, therefore, are the interviews and observation studies with users about mobility preferences, hopes and fears concerning changing to electrified mobility, changing driving styles and everyday practices.

The unique combination of quantitative and qualitative research will be the foundation for the FREEDOM project, one that secures a solid contribution to sustainable mobility