Total coverage combined with unrivalled incident detection times: Valerann’s globally successful new AI platform goes straight to the heart of the data
First and foremost, let’s start with the all-important data: 100% of the road network monitored; 93% noise reduction; over 95% of road accidents detected and classified in under 5 minutes, concluding with a 25% reduction in response times compared to traditional solutions.
Essentially, these numbers tell us two things. Firstly, today it is possible to meticulously control what happens – moment by moment – along a road network with the aim of achieving the best possible levels of prevention of road accidents. Secondly, even in the unfortunate cases where these accidents do occur, the intervention of the operators can be vital, meaning that precious seconds aren’t wasted and the decision-making process is significantly more rapid and effective.
So, how can these goals be achieved? It’s all thanks to remarkable technological innovation in the field of traffic monitoring and, in particular, thanks to a truly revolutionary “data approach”.
The story we want to tell you is about Valerann, a very young company but one that already has a resume dripping with industry awards for its high-tech support activity for highway authorities and road operators.
The company, headquartered and engineered in Tel Aviv (Israel) and London (UK) and with commercial offices in the USA and Spain, began developing its ATMS (Advanced Traffic Management System), Lanternn by Valerann™, in September 2021, exactly two years ago. The acclaim received in the subsequent months has been a real “technological boom”, with successful application cases recorded in Spain (AP-53, Galicia), Israel (Ayalon Highway, Tel Aviv), Costa Rica (Ruta 27), Peru (Lima Express), Chile (Costa Arauco Highway, see our in-depth look at this link), USA (Pocahontas Parkway), and the UK (Milton Keynes), where the platform was implemented on the occasion of the 2022 UEFA Women’s Football Championships as part of a traffic monitoring and management project developed in collaboration with ESA, the European Space Agency.
On that occasion, its satellite technology “dialogued” with the aforementioned Valerann platform’s (abbreviated as LbV) Artificial Intelligence for real-time traffic analysis (go to the in-depth study).
The “Valerann case”, by virtue of its results and approach – generating road safety through innovation – is an example of a connection that fully embodies the spirit of VISIONJ and one that we have chosen to explore, due to the participation of the Anglo-Israeli company, as a sponsor, at ASECAP’s (the association of European toll operators) annual ASECAP Days event that opens today, September 18, in Istanbul, Turkey.
Tomorrow, September 19, in addition to numerous interventions whose details can be found at this link, a poster session is also planned in which Valerann co-founder Michael Vardi will speak, with a presentation entitled “Using AI to Reduce Traffic-Related Fatalities: Enhancing Situational Awareness and Proactive Traffic Management”. To better understand his thinking, click the link below to watch an interview that Vardi gave to the London Business School.
The following day, September 20, it will be the turn of Vardi’s Valerann colleague Jacob Rainbow, with a speech entitled “Accidents as Seen Through the Lens of Real Time Data and how we can learn from them”.
But how does the Valerann platform work? It all starts (as we have also done on the digital pages of VISIONJ, for example at this link) with the concept of Deep Data Fusion.
This is the multi-level analysis of enormous volumes of data from various disparate sources in real time (we are talking, it is estimated, about 100 million data points processed in real time per day) with simultaneous “transformation” (which includes noise cleaning) into precise information and extremely targeted notifications for the service of traffic control centre operators.
At the beginning of the process, one of the strengths of the Valerann solution is undoubtedly the data collection, which occurs on a very wide spectrum. The data comes both directly from the roadside infrastructure already available to the operators (radar, cameras, sensors, etc.), with which the Valerann platform integrates, and from additional sources (weather stations, navigation apps, connected vehicles, maps, and much more).
In addition, it should be emphasized that the system also takes historical data into the equation, going back, in the collection and subsequent analysis, as far as 10 years where possible: an “ingredient” that can really become a fundamental parameter in the risk assessment process.
Along Every Road
Before getting to the heart of the aspect of data processing and then its destination, it is necessary to dwell for a moment on one of the other key strengths of Valerann’s proposal, namely the brand new software, Lanternn by Valerann™, which is called upon to perform the function of “interpreter” of all the data collected.
In fact, it performs a task of extreme precision that, also thanks to the support of machine learning algorithms, takes into account the particularities and peculiarities of each road infrastructure. In essence, the system is tailored to each “road” reality and each different context, allowing for monitoring that is never the same, but continuously updated according to the specific characteristics and needs of each road infrastructure.
What follows, therefore, is a “cleaning” of the data that before being sent to the control room is subjected to the process of “enrichment” and “filtering”, so that the traffic operators can have the best possible information to manage the road system.
To a Safer World
From here, a new era has begun in the field of road safety, in the name of accident prevention and in the key of sustainability. The Valerann project, in addition to the clear objective of improving safety, has the ambition to introduce a real revolution in the way we understand road infrastructure, transforming them from mere “streets” into authentic “smart roads”, with benefits that could also extend to the environment, considering the contribution that could come from the more efficient management of traffic flows.
This is a vision that deserves attention; a vision that could really take us, finally, to a safer world.