When every second counts, safety must not be an afterthought. Emergency journeys with blue lights and sirens in heavy city traffic present emergency services with major challenges on a daily basis. The SAFEPol research project addresses precisely this issue: Together with partners from the police, academia and public administration, TraffGo Road – a subsidiary of Materna – is developing technological solutions designed to make emergency journeys smarter, safer and more proactive. The focus is on connected vehicles, machine-learning systems and traffic lights that adapt to the situation.
SAFEPol is a publicly funded research project that examines how police emergency journeys can be made safer and more efficient in urban traffic. The key approach: where possible, emergency vehicles should no longer have to pass through junctions when the lights are red, but should instead be given priority and a ‘green’ light. In doing so, SAFEPol is pursuing a clear objective: to improve road safety during emergency response operations whilst simultaneously reducing the strain on emergency services personnel in highly stressful situations.
Whilst traditional emergency vehicle routing is often optimised for the fire service – with fixed starting points and known incident locations – the situation for the police is significantly more complex. Emergency vehicles are often ‘out in the field’ and must spontaneously find the best route to the incident location.
SAFEPol therefore combines two innovative approaches: dynamic, data-driven routing and intelligent control of traffic lights along the selected route. The aim is to guide emergency vehicles through green lights as consistently as possible, thereby avoiding dangerous traffic situations.
The technological basis of SAFEPol is what is known as C-ITS technology (Cooperative Intelligent Transport Systems) – a key application of V2X communication (Vehicle-to-Everything communication). This involves vehicles and transport infrastructure communicating directly with one another, for example via on-board units in the vehicle and so-called roadside units at traffic lights.
Such cooperative systems are already familiar from other applications, such as bus priority in local public transport or warning systems on motorways. SAFEPol utilises this technology to provide proactive support for emergency journeys: traffic lights are adjusted in good time, traffic flows are directed in a targeted manner and, ideally, traffic jams are cleared before the emergency vehicle even arrives.
Artificial intelligence also plays a central role in the project – though not as an end in itself, but as a learning optimisation tool. The AI evaluates traffic data, analyses successful and less successful routing strategies, and continuously improves the routing.
This is not about spectacular language models, but about data-driven intelligence: the intelligent analysis of traffic data to optimise routes and signal control strategies. For the project partners, this represents a consistent application of AI, which has already been in use for years in similar contexts.
TraffGo Road, a subsidiary of the Materna Group, plays a central role in the project. Within the project, TraffGo Road provides the backend for processing C-ITS messages and acts as a system integrator for the participating partners. These include, amongst others, the State Office for Central Police Services of North Rhine-Westphalia, the German Police University, research institutions and other technology partners.
It is precisely this integration capability that is crucial: SAFEPol thrives on the interaction between a wide variety of stakeholders from government, academia and industry, and on a technical platform that enables this collaboration.
SAFEPol is currently being trialled in collaboration with the city of Oberhausen. The local authority has a modern network of smart traffic lights that can respond flexibly to traffic conditions – a key prerequisite for the project. Test drives are being carried out in a real-world test site to assess the interaction between routing, AI-supported analysis and traffic light control under real-world conditions. The findings will then be analysed and utilised for a potential roll-out to other cities.
The insights gained from the project are not limited to Oberhausen. SAFEPol is part of the ‘NeueWege.IN.NRW’ funding programme and is explicitly aimed at local authorities throughout the state. Discussions with other cities are already underway.
Furthermore, the technologies used are also relevant for other applications, such as emergency services, critical infrastructure or, in the future, heavy goods and special transport. SAFEPol thus serves as an exemplary demonstration of how research, practical application and digitalisation can work together to solve real-world societal challenges.
SAFEPol exemplifies a form of innovation that does not remain abstract but has a tangible impact. Through intelligent networking, data-driven decision-making and close collaboration between public and private partners, a solution is being developed that can measurably improve road safety.
For Materna and TraffGo Road, the project is yet another example of how technological expertise, system integration and social responsibility come together, and how digitalisation is applied where it is truly needed.
“The aim of SAFEPol is to make emergency response operations safer – not only for the emergency services themselves, but for all road users. Through intelligent routing and proactive traffic light control, the aim is to avoid dangerous situations – such as driving through red lights at junctions – as far as possible. Technology is being used here in a targeted manner to combine safety, efficiency and practicality.”
Dr Joachim Wahle, Managing Director of TraffGo Road
Project name: SAFEPol
Objective: To improve road safety during police emergency call-outs
Project partners
TraffGo Road, SWARCO, mobaix, Ruhr University Bochum, Duisburg Regional Police Headquarters (LZPD), City of Oberhausen, German Police University (DHPol)
Approach
Intelligent routing for emergency response journeys
Dynamic adjustment of traffic light phasing along the route
Use of cooperative intelligent transport systems (C-ITS)
Technology
Vehicle-to-infrastructure communication (C-ITS)
AI-supported analysis of traffic data for route optimisation
Role of TraffGo Road
Backend for processing C-ITS messages
System integration of the project partners involved
Status
Research project with a real-world test site in Oberhausen
Field trials in collaboration with the police and local authority
Outlook
Transferability to other towns
Potential for further areas of application, such as emergency services and critical infrastructure
