Mapping Passenger Trajectories to Train Schedules - industrial paper
L. Padoan, B. Zamengo, F. Silvestri
2024 ·
In Proc. 32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems ·
external link
·
local copy
We introduce a novel algorithm based on a Generalized Suffix Tree (GST) to efficiently link passenger trajectories to train schedules, addressing challenges
like large data volumes and noisy input trajectories.
Enhancing mobility and tourism through data analytics and generative AI
B. Zamengo, G. Mantegari, D. Cappellari
2024 ·
ASA 2024 - Università di Roma La Sapienza ·
external link
·
local copy
We explored the use of Big Data, from Mobile Network Data, GPS Data and Floating Car Data (FCD) to study mobility and tourism, presenting some use cases.
Assessing veracity of big data: An in-depth evaluation process from the comparison of Mobile phone traces and groundtruth data in traffic monitoring
A. Nalin, V. Vignali, C. Lantieri, D. Cappellari, B. Zamengo, A. Simone
2024 ·
Journal of Transport Geography ·
external link
·
local copy
Evaluation of Mobile Network Data veracity against ground-truth traffic measurements, with a detailed quality assessment workflow.
Una nuova metodologia di stima del turismo con i dati di telefonia mobile
A. Righi et al.
2024 ·
Rivista di statistica ufficiale ·
external link
·
local copy
Along with ISTAT and Vodafone, we introduced the “Usual Environment” definition to improve mobile network big data for tourism.
Mobility ChatBot: supporting decision making in mobility data with chatbots
L. Padoan, M. Cesetti, L. Brunello, M. Antonelli, B. Zamengo, F. Silvestri
2024 ·
GenAI4MoDA @ IEEE MDM 2024 Workshop ·
external link
·
local copy
We adapted a generative-AI based chatbot to support users interacting with mobility data.
Learning urban areas from tourist data: a case study with spatially constrained clustering and Airbnb data
E. Rossi, M. Agnolon, B. Zamengo, F. Silvestri
2023 ·
IC2S2 | 9th International Conference on Computational Social Science ·
external link
·
local copy
Spatially constrained clustering on Airbnb + tourist presence signals to infer functional urban areas.
Exploring mobile network data for tourism statistics: the collaboration between Istat and Vodafone Business Italia
ISTAT, Vodafone Business Italia, Motion Analytica (B. Zamengo et al.)
2022 ·
Rivista di statistica ufficiale ·
external link
·
local copy
Official-statistics experimentation using Mobile Network Data for domestic, inbound and outbound tourism indicators.
Big Data Analytics in mobile cellular networks as enabler for innovative statistics to evaluate the effects of Recovery and Resilience Plan actions
A. Zaramella, D. Di Sorte, D. Cappellari, B. Zamengo
2022 ·
SIS 2022 | Book of Short Papers ·
external link
·
local copy
Potential of Mobile Network Analytics to evaluate policy actions under the Recovery and Resilience Plan.
Mobilità e logistica sostenibili. Analisi e indirizzi strategici per il futuro
Ministero delle Infrastrutture e Mobilità Sostenibili (Mims), FSResearchCentre, B. Zamengo et al.
2021 ·
Ministero delle Infrastrutture e Mobilità Sostenibili (Mims) ·
external link
·
local copy
Telco-data based analyses contributing to the national strategic document on sustainable mobility and logistics.
ISTAT e Vodafone Business Italia per il Futuro delle Statistiche sul Turismo
A. Righi, L. Cavallo, E. Cerasti, M. di Torrice, M.T. Santoro, T. Tuoto, L. Valentino, A. Zaramella, D. di Sorte,
B. Zamengo, D. Bertocchi
2021 ·
14ª Conferenza Nazionale di Statistica (poster) ·
external link
·
local copy
Come i dati di telefonia mobile possono contribuire alle statistiche sperimentali sulla mobilità
F. Altarocca, M. Caputi, F. de Fausti, L. Franconi, D. Ichim, S. Mastroluca, R. Radini, A. Zaramella, D. di Sorte,
B. Zamengo, G. Mantegari
2021 ·
14ª Conferenza Nazionale di Statistica (poster) ·
external link
·
local copy