Anticipate the flows and adapt the network


All the modes of transport, public or private, are impacted by the weather conditions. The perturbations can go from a slight delay to a total disruption of the services:

For road transportation, even a slight degradation can have a strong impact, in particular during the peak hours of traffic congestion
For public transport with trains, boats or aircrafts, the services can be delayed, diverted or even canceled
The weather conditions are a key factor in the decision to use alternative modes of transport such as cycling

Predicting the impact of the meteorological conditions is therefore an important challenge in order to anticipate the traffic, to communicate the right information, to adapt the transportation system and thus to minimize the risks of disturbances or damages.


Today, large quantities of data have been accumulated by the operators of public transport or on the road traffic. They can be combined with meteorological observations with the objective to define predictive models in order to anticipate the possible perturbations on the different modes of transport, to improve the communication, to adapt the public offer and to take the best preventive actions.


Driverless car is one of the most exciting and fast growing technology that will definitely change our life in the near future. If most of the information used by self-driving vehicles will come from data processing of real-time sensors, predictive models such as weather or traffic forecasts should bring the ultimate competitive advantage.

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