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PROACTIVE ROAD SAFETY RISK PREDICTION THROUGH MULTI-SOURCE AI AND BIG DATA TO ENABLE DATA-DRIVEN DECISIONS AND INTERVENTIONS

Objective

ROAD4SAFE aims to enhance road safety by predicting and preventing crashes before they occur, leveraging Artificial Intelligence (AI) and big data. The project will develop an AI-powered Digital Twin (DT) of road traffic and infrastructure, integrating data from multiple sources (connected vehicles, infrastructure sensors, environmental and historical data) to generate real-time risk predictions at local and network-level hazard maps. These predictions will feed into a Decision Support System (DSS) that prioritizes and triggers proactive safety measures, delivered to users via Human-Machine Interfaces (HMIs) and Cooperative Intelligent Transport Systems (C-ITS). The project will focus on addressing data fragmentation and improving data quality by developing a federated, FAIR-compliant data architecture and AI models that ensure bias control, optimal sample size, and explainability. Additionally, ROAD4SAFE will tackle challenges in data acquisition and integration, providing solutions for low-latency, real-time data processing through cloud-edge hybrid systems. Two complementary pilots will be conducted: the Rustenfeld Living Lab (Austria), testing critical scenarios in tunnels and entrance-exit ramps, and the Bizkaia Connected Corridor (Spain), assessing network-scale risk prediction. The project will develop and test predictive AI models for real-time hazard forecasting and implement these through a dynamic, multi-scale Decision Support System. By the end of the project, technologies will reach TRL 5-6, offering a comprehensive solution for proactive safety management, aligned with the EU's Vision Zero goal.

Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-RIA - HORIZON Research and Innovation Actions

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Call for proposal

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(opens in new window) HORIZON-CL5-2026-01

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Coordinator

UNIVERSIDAD DE VIGO
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 861 625,00
Address
LG CAMPUS LAGOAS MARCOSENDE
36310 VIGO PONTEVEDRA
Spain

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Region
Noroeste Galicia Pontevedra
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

No data

Participants (12)

Partners (5)