CHALLENGE
One of the key priorities for highway authorities and operators is to reduce the risk of accidents. Every event can bring traffic to a standstill and cost thousands in emergency response and clean-up. There is also the human impact an accident can have on all those involved. Valerann worked with a Spanish operator that needed a better way to manage and pre-empt accidents on a busy stretch of road.
SOLUTION
Valerann’s Lanternn by ValerannTM(LbV) product is powered by a Data Fusion engine that uses Artificial Intelligence (AI) and Machine Learning (ML) to analyze diverse data sources, which th engenerates a real-time risk profile for different stretches of road. In this case,the software calculated the risk profile of the client’s motorway on a rainy summer morning.
Valerann had access to a decade of historical accident and event data for this motorway. From this information, LbV created a risk profile for the road based on different times of the day and in various conditions, which would then be set against real-time conditions to determine the current risk profile.
Actual conditions for the road were calculated by monitoring a range of data sources:weather data, legacy infrastructure, CCTV, radar loops, social media, connected cars, and information from crowdsourcing apps such as Waze and Google Maps.
OUTCOME
By 8am UTC time on the day of the accident, Lbv’s risk modeling algorithm had calculated that there was an elevated risk on one particular stretch of the road. Unusually heavy rain on the curved road, preceded by several very sunny days, combined with real-time traffic conditions, had together created hazardous conditions.
By 2pm UTC time, LbV had received a report of an accident from Waze on the stretch of the road flagged as high risk. As the area was already categorized as especially hazardous, the unverified report was prioritized, and by using around 600 other data points the report was confirmed as true and another alert to the operator was sent.
Shortly after, the traffic controller could direct their camera to the area of the accident (which was clearly identified by Valerann’s vision system algorithm) and calculate how many cars had stopped.
In total, Valerann’s software technology presented the traffic controller with three clear and concise alerts:
· The elevated risk of an accident
· The accident that subsequently occurred
· The location of emergency vehicles and others afterwards.
This is despite ingesting and processing hundreds of different data points, which would normally overwhelm a human operator.
