Predictive Approaches for Safer Urban Environment’
The EU-funded ‘Predictive Approaches for Safer Urban Environment’ (PHOEBE) aims to develop an integrated, dynamic human-centred predictive safety assessment framework in urban areas. This will be achieved by bringing together the inter-disciplinary power of traffic simulation, road safety assessment, human behaviour, mode shift and induced demand modelling and new and emerging mobility data.
Focused on vulnerable road users' safety, the 3.5-year-long PHOEBE project will draw inspiration from real-world scenarios in the three pilot cities of Athens (GR), Valencia (ES) and West Midlands (UK). Testing activities will be performed across the use cases to simulate and forecast the impact of changes on safety in different scenarios of disruptions or transitions across urban transport networks.
Predicting and visualising the safety and socioeconomic outcomes of new forms of transport, new technologies, or regulatory and behavioural changes from the individual (micro) level up to the network-wide (macro) level will also be a significant game-changer for urban stakeholders. The results of PHOEBE can be used as a blueprint by other European cities to develop their knowledge products, such as socioeconomic analysis model, urban road safety assessment, human behaviour and choice modelling.
Vehicles and VRU Virtual eValuation of Road Safety
In order to set policies for road safety in the coming decades and push for Vision Zero, an accepted and reliable method for the comparison of safety measures for CCAM is needed. Following the Safe System approach, the V4SAFETY method will deal with the safety of all road users, from vulnerable road users to vehicle occupants.
V4SAFETY will provide a prospective safety assessment framework that can handle a large variety of safety measures, ranging from in-vehicle safety technology, infrastructure solutions to regulations that influence road user behaviour. It includes methods to project the results in future scenarios and over EU regions for use by policy makers, authorities and consumer organizations.
To understand differences between studies and to understand the influence of underlying data, assumptions and models, the method provides tools to characterise the influence of the contributing factors and their uncertainties.
The resulting transparency and consistency in simulation-based safety assessment leads to much improved comparability and reliability of safety assessment conclusions.