CCAM-ERAS Labour Market Model for Self Driving

12 June 2026

While connected, cooperative and automated mobility (CCAM) is moving from experimentation to deployment, the transition raises social and economic questions related to people, jobs, and skills. The EC project CCAM-ERAS has developed a modelling framework to assess how CCAM will affect jobs across sectors and occupations, helping policymakers and educators plan for a fair, future-ready transition.

The model is based on Cambridge Econometrics’ E3ME model and can be explored on the project website via a Power Bi interface which illustrates three deployment scenarios (warehousing, passenger, and freight), each analysed under different adoption pathways to reflect diverse CCAM uptake dynamics.

This economic tool provides an overview of prospective CCAM deployment trajectories and their implications for the EU labour market, including anticipated shifts in skills requirements and employment structures. It was informed by:

  • supply chain mapping of how CCAM reshapes transport
    This research reveals where disruptions, investments, and new interdependencies are likely to occur—laying the groundwork for quantifying downstream impacts on production, value chains, and ultimately employment within the model.
  • scenarios and key assumptions (such as adoption speed, regulatory constraints, and varying uptake across freight and passenger transport contexts) that shape how CCAM may unfold across Europe
    Grounded in real-world use cases and technology readiness evidence, this research links deployment dynamics to economic drivers. This scenario framework enables comparison of alternative futures and their implications for jobs, sectors, and skills.

CCAM-ERAS has also identified competencies’ and skills’ types required across the CCAM value chain and examined where current education and training systems fall short of these emerging requirements. Building on this analysis, the project proposes practical schemes for reskilling and upskilling, including modular training approaches and lifelong learning frameworks that support evolving workforce development needs.

Find out more about the project results here and here


Source: The original articles were published here and here