MovInCity is conceived as an evidence-based solution whose aim is to empower public administration with contextualized urban mobility indicators to support decision making and contribute in the design of actions plans to promote sustainable and urban infrastructures optimization. The solution will consist of visualization tools adapted to different target users with a multimodal perspective on data visualization (different visualizations adapted to their interests and needs).For this aim, private and public dashboards will be available for:
- Government/Urban planners: evidence-based urban planning indicators for improving urban areas and boost underutilized mobility alternatives.
- Public for citizens/visitors and local businesses: real-time indicators will allow users to make informed decisions on their mobility in urban areas.
MovInCity will be based on an open-source approach, following European Union data space standards. Moreover, HOPU will integrate heterogeneous datasets on mobility, public transport, urban health (air quality, weather, etc.), tourism, and socio-statistics, to contextualize urban actions’ effectiveness. The solution will integrate Artificial Intelligence and data modelling (smart data models) to assess urban environments mobility. The geo-located indicators will be correlated with local climate, noise and crowds-levels to evaluate mobility habits’ impact on theecosystem’s sustainability. In this respect, data will be transformed into valuable indicators to contextualize, model, and forecast the effect of mobility (traffic, people flows,public transport, low emission zones, etc.). The solution will includ
Usage of Standards for data interoperability:
- Apache Spark
- CrateDB
- Scikit-learn
- Keras/Tensorflow
- LSTM Recurrent Neuronal Network
- Apache NiFi
- Orion Context Broker (FIWARE)
- IEEE PAR2510
- Grafana