Proposals should address all the following aspects:

• Development of an artificial intelligence (AI)-enabled digital twin of traffic and infrastructure. This would integrate historical, current, and forecast data, including crowdsourcing and infrastructure sensors, infrastructure topology and condition, along with environmental (e.g. local weather and visibility) and road and traffic conditions. Such a digital twin can allow monitoring and preventively optimising both safety and traffic flow, equally addressing congestion and resilience issues. Results from existing projects like OMICRON[1] could be considered. The proposals should also explore the possibility and usefulness of other type of data such as sociodemographic and economic data, behavioural driving data, data from security cameras, among others that could be provided by third parties (tourism, planned events, demand, etc.);
• Analyse in detail the technical challenges associated with the acquisition and use of adequate and reliable big data from multiple sensors in the road transport system, as well as the process of combining these datasets in ways that are meaningful for proactive road safety analysis;
• Develop methods and tools to predict safety-critical traffic situations at quantifiable risk levels based on real-time and historical data;
• Account for biases in the datasets and ensure that the developed AI-based models or algorithms are bias-free, so that the safety of all road users will be improved effectively in a fair, non-discriminatory way;
• Analyse in detail also the non-technical challenges associated with this approach and the inherent need to collect and share large amounts of data that can be used to identify and quantify road safety-related risk factors. Ethical, legal and economic issues should be considered and concepts be developed to overcome these challenges in terms of privacy concerns, questions of data ownership, organisational barriers etc;
• Analyse what real-time countermeasures can be taken to reduce instantaneous risk levels for all road users complementary to existing Intelligent Transport Systems (ITS) services;
• Demonstrate the feasibility of such risk predictions and targeted interventions;
• Build consensus among relevant stakeholders on possible routes for deployment in coordination with other ITS services.

Particular attention should be dedicated on establishing interoperability standards for data sharing, through the implementation of the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and leveraging on already adopted practices especially those in the relevant Common European data spaces.

Ways to leverage valuable complementary data, e.g. metadata from crash databases, should also be explored, as well as links to initiatives for European data spaces.

Research is expected to develop recommendations for updates to relevant standards and legal frameworks. International cooperation is advised, in particular with projects or partners from the US, Japan, Singapore and Australia. Knowledge and experience from other modes where similar approaches are followed in much more controlled environments should be leveraged.