An Adaptive Bi-Level Gradient Procedure for the Estimation of Dynamic Traffic Demand. - Guido Cantelmo; Ernesto Cipriani; Andrea Gemma; Marialisa Nigro (IEEE Trans. Intell. Transp. Syst., 2014)

A Two-Steps Dynamic Demand Estimation Approach Sequentially Adjusting Generations and Distributions. - Guido Cantelmo; Francesco Viti; Ernesto Cipriani; Marialisa Nigro (ITSC, 2015)

The Impact of Route Choice Modeling on Dynamic OD Estimation. - Ernesto Cipriani; Andrea del Giudice; Marialisa Nigro; Francesco Viti; Guido Cantelmo (ITSC, 2015)

Systematic assessment of local & global signal control policies: A methodological perspective. - Guido Cantelmo; Francesco Viti; Marco Rinaldi; Chris M. J. Tampère; Michael J. Smith 002; Klaus Nokel (MT-ITS, 2015)

A Markov chain dynamic model for trip generation and distribution based on CDR. - Simone Aniello Di Donna; Guido Cantelmo; Francesco Viti (MT-ITS, 2015)

Assessing the consistency between observed and modelled route choices through GPS data. - Selini Natalia Hadjidimitriou; Mauro Dell'Amico; Guido Cantelmo; Francesco Viti (MT-ITS, 2015)

A network-wide assessment of local signal control policies' performance in practical implementations. - Guido Cantelmo; Francesco Viti; Marco Rinaldi (ITSC, 2016)

Effectiveness of the two-step dynamic demand estimation model on large networks. - Guido Cantelmo; Francesco Viti; Thierry Derrmann (MT-ITS, 2017)

Demo: MAMBA: A platform for personalised multimodal trip planning. - Sébastien Faye; Guido Cantelmo; Ibrahim Tahirou; Thierry Derrmann; Francesco Viti; Thomas Engel 001 (VNC, 2017)

Using Passive Data Collection Methods to Learn Complex Mobility Patterns: An Exploratory Analysis. - Bogdan Toader; Guido Cantelmo; Mioara Popescu; Francesco Viti (ITSC, 2018)

The Impact of Human Mobility on Edge Data Center Deployment in Urban Environments. - Piergiorgio Vitello; Andrea Capponi; Claudio Fiandrino; Guido Cantelmo; Dzmitry Kliazovich (GLOBECOM, 2019)

Crowdsensed Data Learning-Driven Prediction of Local Businesses Attractiveness in Smart Cities. - Andrea Capponi; Piergiorgio Vitello; Claudio Fiandrino; Guido Cantelmo; Dzmitry Kliazovich; Ulrich Sorger; Pascal Bouvry (ISCC, 2019)

A low dimensional model for bike sharing demand forecasting. - Guido Cantelmo; Rafal Kucharski; Constantinos Antoniou 001 (MT-ITS, 2019)

Leveraging GIS Data and Topological Information to Infer Trip Chaining Behaviour at Macroscopic Level. - Filippo Carrese; Guido Cantelmo; Gaetano Fusco; Francesco Viti (MT-ITS, 2019)

Incorporating Trip Chaining within Online Demand Estimation. - Guido Cantelmo; Moeid Qurashi; A. Arun Prakash; Constantinos Antoniou 001; Francesco Viti (CoRR, 2019)

Machine Learning from imbalanced data-sets: an application to the bike-sharing inventory problem. - Giovanni Ceccarelli; Guido Cantelmo; Marialisa Nigro; Constantinos Antoniou 001 (MT-ITS, 2021)

A Gaussian sampling heuristic estimation model for developing synthetic trip sets. - Sérgio F. A. Batista; Guido Cantelmo; Mónica Menéndez; Constantinos Antoniou 001 (Comput. Aided Civ. Infrastructure Eng., 2022)

Mobility-Driven and Energy-Efficient Deployment of Edge Data Centers in Urban Environments. - Piergiorgio Vitello; Andrea Capponi; Claudio Fiandrino; Guido Cantelmo; Dzmitry Kliazovich (IEEE Trans. Sustain. Comput., 2022)

Long-term & short-term bike sharing demand predictions using contextual data. - Mirfarnam Tabandeh; Constantinos Antoniou 001; Guido Cantelmo (MT-ITS, 2023)

Learning from Imbalanced Datasets: The Bike-Sharing Inventory Problem Using Sparse Information. - Giovanni Ceccarelli; Guido Cantelmo; Marialisa Nigro; Constantinos Antoniou 001 (Algorithms, 2023)

Machine learning for activity pattern detection. - Natalia Selini Hadjidimitriou; Guido Cantelmo; Constantinos Antoniou 001 (J. Intell. Transp. Syst., 2023)

Learning traffic flows: Graph Neural Networks for Metamodelling Traffic Assignment. - Oskar Bohn Lassen; Serio Agriesti; Mohamed Eldafrawi; Daniele Gammelli; Guido Cantelmo; Guido Gentile; Francisco Câmara Pereira (CoRR, 2025)

Learning to Learn the Macroscopic Fundamental Diagram using Physics-Informed and meta Machine Learning techniques. - Amalie Roark; Serio Agriesti; Francisco Câmara Pereira; Guido Cantelmo (CoRR, 2025)