Deployment of Connected and Autonomous Vehicles (CAVs) will lead to many changes in the operation of road networks and in travel behaviour. Whilst previous research activities in adaptive traffic signal control in CAVs environment focused only on how the emerging technologies contribute in alleviating urban traffic problems and improvement of traffic characteristics such as speed, volume, queues, and delay, the SENECA project aims at harvesting potential of forthcoming technologies for more efficient emergency management. Given the huge capabilities of CAVs for improvement of traffic management, SENECA focus is on the evaluation of CAVs impact in the mixed traffic and on an improvement of the performance of signalized intersections during normal and emergency conditions by eliminating unnecessary transport delays and the risk of incidents that are experienced by emergency vehicles. The key factor contributing to the high originality of the SENECA project is a design of the architecture of dynamic adaptive traffic management systems for efficient emergency response systems in Slovakia and Israel


The aim of the project is to evaluate and quantify potential benefits of intelligent transport systems and information communication technologies deployment for future emergency rescue systems in urban areas by following these objectives:

  • To gain understanding of factors that are imposing unnecessary transport delays and a risk of emergency vehicles incidents at signalized intersections.
  • To develop approaches enabling an efficient sharing of information between traffic management and emergency rescue systems.
  • To design and develop optimization algorithms for traffic control of signalized intersections under the mixed traffic composed of vehicles with various automation levels enabling efficient emergency response.
  • To evaluate the proposed concepts on realistic case studies developed and run in Slovakia and Israel

Expected results:

The results of the project are expected to contribute to the current scientific level in the corresponding research areas:

  • Traffic management and control during emergency events,
  • Traffic signal planning in the context of the mixed traffic and emergency situations, e.g. routing, vehicle priorities and dynamic lane allocation,

Communication architecture for a reliable traffic management data exchange between the traffic management entity and conventional vehicles supported by the data gathered by CAVs and sensors built-in traffic management and IoT infrastructure in the context of the emergency situations and the mixed traffic conditions.