Cheaper traffic data, less congestion

 

 

AI for a more reliable traffic information

 

The solution is able to reconstruct the missing traffic data with excellent accuracy.

 

 

 

 

 

 

 

 

//KEY RESULTS

The counting algorithm developed in the framework of this project uses artificial intelligence to reconstruct road data with a reduced number of sensors while maintaining a reliable level of information on the number of vehicles on the road. The goal is to provide a better service to the road users and to reduce traffic congestion.

 

 

The experimentation took place in Paris.

 

// PARTNERS INVOLVED

Partners provided data pertaining the road usage in the fast lanes of the Ile de France region. Wintics, the startup selected for this project, develops artificial intelligence algorithm applied to city environments, with a strong focus on solving mobility issues using traffic data.

 

 

// PAIN POINT & OPPORTUNITY

 

The maintenance of road sensors is very expensive and complex as a result of construction works, road closings, and security. Renewing every single sensor would cost approximately 42 million Euros. The cost is not sustainable, which forces the DIRIF to optimize their numbers by relying on alternative solutions.

 

 

 

In every city, embed sensors in road infrastructures are very costly and ineffective because they requires high maintenance costs. This technology provides a solution for real time reliable traffic information while reducing the usage of road sensors.

 

 

 

 

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A program designed by NUMA in partnership with the City of Paris