Planning for more resilient urban transport systems: Lessons learned from the Covid-19 pandemic

Abstract

Grounded on public sensorization initiatives to monitor the Lisbon's mobility system as a whole, the Integrative Learning from Urban Data and Situational Context for City Mobility Optimization (ILU) research project was initially designed as a means of providing decision support tools for the city of Lisbon to advance towards sustainable mobility. This paper reviews a significant number of research outcomes developed in the scope of the ILU project that are aligned with the envisaged goal. These are comprehensively analyzed through an integrated framework to identify how different theories and methods anchored in data science and transport planning were applied to the different datasets of the public transport services.