João Tiago Aparicio‘s master’s thesis has been considered the best in the area of ​​intelligent urban mobility, among those produced at the University of Lisbon (ULisboa), in 2022.

Modelling and Assessing Resilience in Multimodal Transportation Systems proposed to enable transportation operators to efficiently manage the demand for transportation in each segment of the network. The full thesis abstract is available below*. Aparicio is currently a PhD student at the Information and Decision Support Systems (IDSS) Research Area at INESC-ID.

The University of Lisbon, through networkMOV (an interdisciplinary thematic network in the field of intelligent urban mobility), awards the ULisboa-networkMOV Prize (Prémio ULisboa RedeMOV) to recognize the best master’s and doctoral final projects carried out at ULisboa related to the theme of intelligent urban mobility. The prize for the best master’s final project, amounting to €1,500, was first awarded in 2021.

The award ceremony took place on July 14th, in the Conference Hall of Reitoria da Universidade de Lisboa, as part of the networkMOV Conferences “Conversas à sexta” (“Friday Conversations”).


*Modelling and Assessing Resilience in Multimodal Transportation Systems (Full abstract): Currently, more than 50% of the global population lives in urban areas. This brings various mobility challenges, particularly pushed by commuting needs in the public transport network to get to work, school, university and several other places daily. In this context, transport demand, city information and operational concerns need to be aligned. As such, this thesis aims to contribute to a more sustainable mobility solution by proposing and empirically testing methods to assess the resilience of a multimodal transport system. Resilience is seen in both static and dynamic settings, looking at aspects in the network topology and user flow and demand. Our hypothesis is that the appropriate multi-layered and traffic-sensitive modelling of this network can promote the integrate analysis of different modalities and characterize resilience. To this end, we propose three major contributions, a robustness assessment model along with the analysis of dynamics and the demand and supply changes as a means to characterize resilience. Within this multilayer network, citizens’ mobility patterns can be understood and represented. In particular we resort to the the use of agglomerative hierarchical clustering and weighted digraphs to this end. The results of this study allow decision-makers to understand the vulnerabilities and ongoing changes to the citizen multimodal patterns in a multimodal transportation network. Moreover, we highlighted the changes in passenger traffic demand during Covid-19 pandemic.