Periodic Reporting for period 1 - iDriving (iDriving – Intelligent & Digital Roadway Infrastructure for Vehicles Integrated with Next-Gen Technologies)
Berichtszeitraum: 2024-07-01 bis 2025-12-31
Urban mobility systems across Europe are facing increasing pressure due to rising traffic volumes, road safety risks, environmental impacts, and the growing complexity of transport networks. Cities are required to balance the efficient movement of people and goods with determined and demanding climate objectives, public health concerns, and societal expectations for safer, cleaner, and liveable urban environments. In the meantime, traffic management authorities normally operate with fragmented information, limited situational awareness, and limited capacity to anticipate risks or respond proactively to rapidly changing traffic conditions.
Although large volumes of transport-related data are generated on a daily basis, existing traffic monitoring and management remains hindered, reactive, while it is also focused on isolated parts of the transport system. This limits the ability of public authorities to form a comprehensive, real-time understanding of traffic systems, and to translate available data into evidence-based decisions. Consequently, opportunities to improve road safety, reduce congestion, and optimise the use of the existing infrastructure are not fully exploited.
These challenges are closely aligned with key European policy priorities, such as road safety, sustainable urban mobility, and the digital transformation of transport systems. In the context of increasing urbanisation and the transition towards greener and more digital mobility, there is a growing need for data-driven solutions to support smarter, resilient, and more adaptive traffic management across European cities.
Against this backdrop, the overall objective of the project is to develop and demonstrate an integrated approach for monitoring, analysing, and managing traffic, to support more informed, timely, and proactive decision-making. By combining advanced sensing technologies, artificial intelligence, and digital modelling within a unified framework, the project aims to enable traffic authorities and decision makers to better understand current conditions, anticipate emerging risks, and assess alternative management strategies.
The project’s pathway to impact is based on transforming heterogeneous traffic and mobility data into actionable insights that can be directly used by decision-makers. By improving situational awareness and enabling predictive and scenario-based analysis, the project is expected to contribute mainly to road safety improvement and reduced congestion, but also to more efficient and sustainable use of urban transport infrastructure. In addition, social and behavioural factors are considered to ensure usability, trust, and long-term adoption of the developed solutions.
Although large volumes of transport-related data are generated on a daily basis, existing traffic monitoring and management remains hindered, reactive, while it is also focused on isolated parts of the transport system. This limits the ability of public authorities to form a comprehensive, real-time understanding of traffic systems, and to translate available data into evidence-based decisions. Consequently, opportunities to improve road safety, reduce congestion, and optimise the use of the existing infrastructure are not fully exploited.
These challenges are closely aligned with key European policy priorities, such as road safety, sustainable urban mobility, and the digital transformation of transport systems. In the context of increasing urbanisation and the transition towards greener and more digital mobility, there is a growing need for data-driven solutions to support smarter, resilient, and more adaptive traffic management across European cities.
Against this backdrop, the overall objective of the project is to develop and demonstrate an integrated approach for monitoring, analysing, and managing traffic, to support more informed, timely, and proactive decision-making. By combining advanced sensing technologies, artificial intelligence, and digital modelling within a unified framework, the project aims to enable traffic authorities and decision makers to better understand current conditions, anticipate emerging risks, and assess alternative management strategies.
The project’s pathway to impact is based on transforming heterogeneous traffic and mobility data into actionable insights that can be directly used by decision-makers. By improving situational awareness and enabling predictive and scenario-based analysis, the project is expected to contribute mainly to road safety improvement and reduced congestion, but also to more efficient and sustainable use of urban transport infrastructure. In addition, social and behavioural factors are considered to ensure usability, trust, and long-term adoption of the developed solutions.
The project carried out a comprehensive programme of technical and scientific activities aimed at developing, integrating, and demonstrating an advanced approach to urban traffic monitoring and management. The activities focused on converting heterogeneous transport data into meaningful, insights, supporting real-time traffic operations, longer-term planning, and policy-oriented decision-making.
A core achievement of the project is the development of an integrated framework that combines data from multiple sources, including traffic monitoring systems, mobile and infrastructure based sensors, and advanced observation technologies. These data streams are processed and analysed using artificial intelligence techniques to identify traffic conditions, violations, safety-related events, and mobility patterns, which are considered difficult to detect through conventional monitoring approaches alone.
Building on this data foundation, the project developed modelling capabilities that allow traffic systems to be represented and explored within a virtual environment. This enables decision makers to visualise current conditions, test multiple scenarios, and assess the potential effects of different traffic management strategies before these are implemented in real-world conditions. Emphasis was placed on system integration and interoperability, resulting in an end-to-end solution connecting data collection, analytics, modelling, and visual support.
The developed solutions were tested, demonstrating their technical feasibility, robustness, and practical relevance when planned to be implemented under realistic operating conditions.
A core achievement of the project is the development of an integrated framework that combines data from multiple sources, including traffic monitoring systems, mobile and infrastructure based sensors, and advanced observation technologies. These data streams are processed and analysed using artificial intelligence techniques to identify traffic conditions, violations, safety-related events, and mobility patterns, which are considered difficult to detect through conventional monitoring approaches alone.
Building on this data foundation, the project developed modelling capabilities that allow traffic systems to be represented and explored within a virtual environment. This enables decision makers to visualise current conditions, test multiple scenarios, and assess the potential effects of different traffic management strategies before these are implemented in real-world conditions. Emphasis was placed on system integration and interoperability, resulting in an end-to-end solution connecting data collection, analytics, modelling, and visual support.
The developed solutions were tested, demonstrating their technical feasibility, robustness, and practical relevance when planned to be implemented under realistic operating conditions.
The project advances beyond current practices by enabling a more integrated, predictive, and decision-oriented use of traffic and mobility data. While many existing solutions focus on specific - normally isolated - data sources or largely reactive monitoring, the project demonstrates how diverse data streams can be combined within a unified framework to support a comprehensive understanding of traffic systems and dynamics.
A key advancement is the of linking real-time observations with advanced analytics and digital modelling, allowing authorities to assess risks, and evaluate potential interventions in advance. This allows for a shift towards proactive and preventive traffic management to be planned and implemented, where impacts and potential trade offs can be assessed before measures are applied.
The project also places strong emphasis on interoperability and adaptability, reducing reliance on closed systems and enhancing transferability across different contexts. By aligning technological innovation with real-world decision-making processes, the project addresses usability, transparency, and trust in data driven systems – therefore strengthening the potential for wider uptake and long-term impact.
A key advancement is the of linking real-time observations with advanced analytics and digital modelling, allowing authorities to assess risks, and evaluate potential interventions in advance. This allows for a shift towards proactive and preventive traffic management to be planned and implemented, where impacts and potential trade offs can be assessed before measures are applied.
The project also places strong emphasis on interoperability and adaptability, reducing reliance on closed systems and enhancing transferability across different contexts. By aligning technological innovation with real-world decision-making processes, the project addresses usability, transparency, and trust in data driven systems – therefore strengthening the potential for wider uptake and long-term impact.