Freight transportation, which is essential to the global economy, also includes movement of hazardous materials that can compromise safety, the environment and human health.  Substances such as fuels and industrial solvents – flammable, toxic, and corrosive – are potentially dangerous and could exacerbate the effects of any accidents, amplifying them beyond the immediately affected area, with serious repercussions for infrastructure, the environment, and mobility. Managing accidents involving dangerous goods also entails extended response time and high costs; therefore, strict regulations and advanced technologies must be introduced to mitigate risks and ensure safety. 

Data analysis and issue identification 

To assess the risks associated with dangerous goods transportation, Movyon conducted a preliminary analysis based on data collected by an OCR (Optical Character Recognition) monitoring system.  The system detects the orange plates on dangerous containers (Figure 1) using cameras installed on highway portals located at critical points such as tunnel entrances and exits. This can be exploited to map flows and estimate risk exposure. In any case, the data analysis revealed one particularly critical aspect: in just one year, more than 2,000 vehicles carrying dangerous goods passed through the same highway tunnel within 10 seconds of each other, at a distance of less than 250 meters. In some cases, the vehicles even drove in single file, with virtually no separation. 

Although these numbers may seem small compared to the overall traffic, and despite the fact that the vehicles were formally complying with the minimum distance required by the traffic regulations, the potential risk remains high. In the event of an accident, in fact, proximity between dangerous vehicles can turn a limited event into a chain disaster, with serious consequences for the safety of people, infrastructure and the environment. 

In this context, it is valuable to consider a system capable of enhancing the safety levels of dangerous goods transportation—not only by monitoring vehicle transit, but also by providing road operators with greater real-time awareness of the presence and behavior of potentially high-risk vehicles. In fact, the road operator’s visibility remains limited to specific control sections, leaving large portions of the network unmonitored. 

Greater safety in dangerous goods transportation 

Cooperative intelligent transportation systems (C-ITS) have proven to be the most effective answer to the challenges of transporting dangerous goods. With the ability to link vehicles and road infrastructure directly, C-ITS enable a radical transformation in the approach to risk management from passive surveillance to active and intelligent control. In this context, the dynamic management of vehicle spacing takes into account various factors—such as tunnels, heavy traffic, and adverse weather conditions—and adapts accordingly. This paradigm shift enables road operators to intervene in real time, monitor critical traffic flows, and prevent potentially hazardous situations. Ultimately, C-ITS can pave the way for safer, more sustainable, and technologically advanced mobility. 

A project developed in collaboration with IVECO led to the creation of a Proof of Concept, equipping vehicles with the necessary technology to communicate in real time with road infrastructure. This enables Vehicle-to-Infrastructure (V2I) communication and advanced monitoring of dangerous goods transits. Specifically, communication between vehicles and the road infrastructure is based on the Cooperative Awareness Messages (CAM) standard. Vehicles can enrich the exchanged messages by adding specific data fields—such as the Kemler code, UN number, and quantity of transported substances—entered directly by the driver prior to departure via dedicated Human Machine Interfaces (HMI) in the vehicle or through a smartphone application. These additional data allow for detailed tracking of cargo characteristics, providing the motorway operator with a complete and real-time picture.