F R A N C H E T A I

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Absorbing Traffic Pollution with AI

Inspired by the Cotoneaster franchetii, a super-plant acknowledged for filtering 20% more automobile air pollutants emissions, the solution proposes monitoring and estimating the GHG emissions produced by transportation solutions within smart cities across Europe.

Absorbing <span class="f_700">Traffic Pollution</span> with AI

Absorbing Traffic Pollution with AI

Funded by AI4Cities PCP

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Challenges being solved

FranchetAI targets Smart Cities, concretely the municipalities and operators (e.g., public transit, EV charging, logistics) together with travellers and citizens' communities.

1
Mobility-as-a-Service:

Mobility-as-a-Service:

  • High usage of private vehicles within city boundaries. Relatively low attractiveness of public transportation.

  • Lack of understanding and real-time prediction of passenger behaviour.

  • Lack of motivation among citizens to change their habits due to low awareness of the impact of their mobility choices on their carbon footprint.

2
Traffic Flow Optimisation:

Traffic Flow Optimisation:

  • Optimisation of the location of EV charging stations.

  • Predictive traffic planning taking into account the effect of the weather, safety issues, person disabilities, among others.

  • A better understanding of the potential demand for new modes of travel and the necessity to develop a predictive usage model for these emerging mobility domains (e.g., e-scooters, e-bikes, personal vehicle sharing, just to name a few).

3
Optimisation of logistics:

Optimisation of logistics:

  • Half-empty vehicles making logistics deliveries.

  • Very little or no task coordination between transport suppliers

  • Difficulties for consumers and producers to get a unified perspective on the CO2 emissions created by local and long-distance trade

  • Deliveries made during peak traffic hours leading to increases in congestion

  • Deliveries made by cars using fossil fuel

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This is part of the AI4Cities project that has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 871914