ANYWHERE engages a citizen science campaign to validate the A4Snow tool

  • News   •   May 03, 2019

In Catalonia snow falls are not common in many areas (apart from the northern part of the country). So, when a snow event occurs it usually causes traffic disruptions and mobility issues to population because of the lack of preparedness, specially with heavy snow.

To avoid the associated incidents and minimize their negative effects, it is important for the population to know beforehand the affected roads. Therefore, within the ANYWHERE project it has been developed the A4Snow tool to forecast the road conditions and the impact in traffic circulation due to snow accumulation in the road network.

In Autumn 2018, the A4Snow tool was implemented in real-time and publicly released to raise populations’ awareness and preparedness. By allowing citizens knowing one day beforehand the forecasted road affectation derived from snow events they can adapt their mobility accordingly (both individuals and collectives such school transports).

It is expected that the information gathered will allow scientists validating and improving the accuracy of the current impact forecasting model

In order to validate the impact forecast for the road affectation, the citizens’ feedback and collaboration was requested by means of the ‘Carretera Nevada’ (Snowy road) crowdsourcing campaign, coordinated by the Center of Applied Research in Hydrometeorology (CRAHI) of the Universitat Politècnica de Catalunya · BarcelonaTech (UPC). During the winter season, this citizen science initiative aimed to gather data on the impact derived from snowfalls on Catalan road network by encouraging citizens to send photographs of the roads affected through Twitter and e-mail.

When finalizing the campaign in April 2019, the feedback and images received associated to the different snow episodes happened during the winter season will be analysed. It is expected that the information gathered will allow scientists validating and improving the accuracy of the current impact forecasting model.

  • News   •   May 03, 2019

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