ROAD4SAFE: Proactive road safety risk prediction through multi-source AI and Big Data to enable datadriven decisions and interventions
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 (CITS).
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.
MEMBERS
1 UNIVERSIDAD DE VIGO Coordinator
2 LUNDS UNIVERSITET
3 TECHNISCHE UNIVERSITAET CHEMNITZ
4 UNIVERSIDADE DA CORUNA
5 TECHNISCHE UNIVERSITEIT DELFT
6 FUNDACION TECNALIA RESEARCH & INNOVATION
7 CENTRO DE ESTUDIOS DE MATERIALES Y CONTROL DE OB
8 HAAS ALERT LTD
9 AUTOBAHNEN- UND SCHNELLSTRASSEN-FINANZIERUNGS
10 INFRASTRUCTURE MANAGEMENT CONSULTANTS GMBH
11 XOUBA INGENIERIA S.L.
12 COMMSIGNIA Korlatolt Felelossegu Tarsasag
13 EUROPEAN UNION ROAD FEDERATION AISBL
14 QUEENSLAND UNIVERSITY OF TECHNOLOGY - QLD QUT
15 UNIVERSITY OF MASSACHUSETTS
16 The State of Queensland Acting through the Department
17 Saegis Engineering, Inc.
18 INSTITUTE FOR INFOCOMM RESEARCH
The participation of Applied Geotechnologies Research Group has been partially supported by Universidade de Vigo (Axudas propias á investigación da Universidade de Vigo, convocatoria 2025).