1.59 - URBY.SENSE - Urban mobility analysis and prediction for non-routine scenarios using digital footprints

Project Description

In this project we propose to study individual’s mobility for mining non-routine (leisure, social, etc.) mobility patterns from multiple data sources. The following mobility patterns are of great interest: locations of significance, modes of transport, trajectory patterns and location-based activities for destination choice modelling. Data collected via ubiquitous devices and smart metering combined with data from social media platforms provides a range of new close-to-real-time information that can be combined with the data from more traditional sources (surveys, transport system records and static data) for urban efficient mobility planning and management. When considered in isolation, each of these data sources has gaps/missing observations, so the matching of multiple data sources can facilitate transport analysis, and enable operators to better tune – even on the fly – public transportation within cities with the aim of travelling at lower costs, faster and producing a smaller carbon footprint.

Research Team


  • Rui Gomes (coordinator)
  • Carlos Bento 
  • Ana Cristina da Costa Oliveira Alves 
  • Marco Veloso 
  • Anabela Ribeiro 
  • Francisco Nibau Antunes
  • Alexandre Almeida
  • Inês Cunha
  • Marta Mercier
  • Merkebe Getachew Demissie
  • Frederico Marques


  • Ana Aguiar
  • Daniel Moura
  • João Rodrigues


  • Assaf Biderman (MIT)
  • Bart van Arem (TUDelft)
  • João Abreu (IST)
Financial Support
  • FCT - PTDC/ECM-TRA/6803/2014
  • URBY.SENSE is co-financed by COMPETE 2020, Portugal 2020 - Operational Program for Competitiveness and Internationalization (POCI), European Union’s ERDF (European Regional Development Fund), and the Portuguese Foundation for Science and Technology (FCT).
Stage of Progress
  • Concluded in 2018