Most hotspot analyses still rely mainly on accident databases. This approach is useful, but it also comes with clear limitations: it only reflects recorded crashes, requires significant manual effort, and is typically updated only occasionally. More importantly, it leads to a reactive perspective—locations are flagged as risky only after accidents occur.
This naturally leads to a key question: how can we identify potentially unsafe locations before accidents happen?
What if your daily commute could help prevent the next road accident? In Greece, the SOTERIA project has moved from a bold idea to a proven reality, transforming everyday routes in Chania and Igoumenitsa into data-rich road safety laboratories.
On Headington Road, the tarmac is half finished on the cycle lane. There's a lamppost where the lane should be.
A few miles away, on Woodstock Road, the cycle lane exists, but it's regularly parked on by van and lorry drivers. On London Road, it's the surface itself: potholes and cracked tarmac that push riders into traffic. At The Plain roundabout, one approach arm guides cyclists into the blind spot of left-turning vehicles.
But how does any of that reach the people who can actually fix it?
What if virtual reality could help adolescents understand traffic dangers before an accident happens? In Saxony, Germany, the SOTERIA project is combining immersive VR technology with a smart routing app to protect young road users.
Making way for the vulnerable road users: Netcompany is steering SOTERIA toward safer urban mobility
By prioritising cycling in the European Union (EU) mobility policy, EU institutions and Member States are expected to increase financial investments to make cycling not only accessible but also safe. Of all types of road users, cyclists are the only road users among whom serious injuries and fatalities have increased over the past years.
To enhance the safety of micromobility and VRUs, UoW in the context of SOTERIA project introduces an innovative sensor kit which can be mounted on microvehicles to collect micromobility-related data, and proposes a data-driven approach to detect micromobility hazards in real time.