Integrative Learning from Urban Data and Situational Context for City Mobility Optimization (ILU (IDSS))

Type: National Project Project

Duration: from 2019 Jan 01 to 2022 Jun 30

Financed by: FCT

Prime Contractor: INESC-ID (Other)

Challenges: The iLU project proposes to address two major challenges: 1) the lack of an integrative analysis capable of combining different sources of urban data collected from city sensors and ticket validations in the diverse modalities of public transport, and 2) the absence of situational context in predictions and circuit recommendations. Scientific contributions: 1) to consolidate the different sources of urban data available in the Plataforma de Gestão Inteligente de Lisboa (PGIL) to allow an intermodal analysis of the mobility in the city; 2) to discover patterns of circulation from these heterogeneous data sources, particularly emerging patterns and correlations between urban traffic and its situational context; 3) anticipate congestion of traffic and access to public transport; and 4) support mobility decisions, such as reinforcing of public transport in response to specific events and positive conditioning of traffic in the city through recommendations to the citizens sensitive to situational context.


  • Câmara Municipal de Lisboa (Other) - Lisbon, Portugal
  • LNEC - Laboratório Nacional de Engenharia Civil (Other)

Principal Investigators