Publications (7)
Federated Dynamic Modeling and Learning for Spatiotemporal Data Forecasting
Thien Pham, Angelo Furno, Faïcel Chamroukhi +1
This paper presents an advanced Federated Learning (FL) framework for forecasting complex spatiotemporal data, improving upon recent state-of-the-art models. In the proposed approa…
Early Detection of Critical Urban Events using Mobile Phone Network Data
Pierre Lemaire, Angelo Furno, Stefania Rubrichi +5
Network Signalling Data (NSD) have the potential to provide continuous spatio-temporal information about the presence, mobility, and usage patterns of cell phone services by indivi…
Contextual Data Integration for Bike-sharing Demand Prediction with Graph Neural Networks in Degraded Weather Conditions
Romain Rochas, Angelo Furno, Nour-Eddin El Faouzi
Demand for bike sharing is impacted by various factors, such as weather conditions, events, and the availability of other transportation modes. This impact remains elusive due to t…
Impact of the COVID-19 pandemic on bike-sharing uses in two french towns
Angelo Furno, Bertrand Jouve, Bruno Revelli +3
Urban areas have been dramatically impacted by the sudden and fast spread of the COVID-19 pandemic. As one of the most noticeable consequences of the pandemic, people have quickly…
Optimal Subgraph on Disturbed Network
Matthieu Guillot, El-Houssaine Aghezzaf, Nour-Eddin El Faouzi +1
During the pandemic of COVID-19, the demand of the transportation systems are drastically changed both qualitatively and quantitatively and the network has become obsolete. In this…
Exploring the Multi-modal Demand Dynamics During Transport System Disruptions
Ali Shateri Benam, Angelo Furno, Nour-Eddin El Faouzi
Various forms of disruption in transport systems perturb urban mobility in different ways. Passengers respond heterogeneously to such disruptive events based on numerous factors. T…