All modes of transport, whether public or private, are affected by weather conditions.
Disruptions can range from minor delays to complete service stoppages:
For road transportation, even minor weather changes can significantly impact traffic, especially during peak congestion hours.
For public transport, including trains, boats, and aircraft, services can be delayed, diverted, or even canceled.
Weather conditions are a crucial factor in deciding to use alternative transport modes such as cycling.
Predicting the impact of meteorological conditions is therefore a critical challenge.
Accurate forecasts allow us to anticipate traffic, communicate timely information, adapt the transportation system,
and minimize the risks of disruptions or damages.
Today, operators of public transport and road traffic systems have accumulated large quantities of data. These can be combined with meteorological observations to develop predictive models. The goal is to anticipate potential disruptions across different modes of transport, improve communication, adapt public services, and implement the most effective preventive measures.
Driverless cars represent one of the most exciting and rapidly growing technologies that will undoubtedly change our lives in the near future. While most information used by self-driving vehicles comes from real-time sensor data processing, incorporating predictive models like weather and traffic forecasts could provide the ultimate competitive advantage.
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