Search Results - "Silveira-Santos, Tulio" :: K.UTB vyhledávací portál

Understanding and Predicting Ride-Hailing Fares in Madrid: A Combination of Supervised and Unsupervised Techniques

by Silveira-Santos, Tulio, Papanikolaou, Anestis, Rangel, Thais, Manuel Vassallo, Jose
Published in Applied sciences (01.04.2023)

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Using machine learning models to predict the willingness to carry lightweight goods by bike and kick-scooter

by Silveira-Santos, Tulio, Manuel Vassallo, Jose, Torres, Ewerton
Published in Transportation research interdisciplinary perspectives (01.03.2022)

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Evaluating the performance of highway concessions through public-private partnerships using a fuzzy multi-criteria decision-making procedure

by Silveira Santos, Túlio, da Silva Portugal, Licinio, Cezar Martins Ribeiro, Paulo
Published in Transportation research interdisciplinary perspectives (01.06.2021)

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Forecasting Moped Scooter-Sharing Travel Demand Using a Machine Learning Approach

by Silveira-Santos, Tulio, Rangel, Thais, Gomez, Juan, Vassallo, Jose Manuel
Published in Sustainability (01.07.2024)

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The impact of Low Emission Zones on retail activity: Madrid Central lessons

by Tarriño-Ortiz, Javier, Soria-Lara, Julio A., Silveira-Santos, Tulio, Vassallo, José M.
Published in Transportation research. Part D, Transport and environment (01.09.2023)

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Were ride-hailing fares affected by the COVID-19 pandemic? Empirical analyses in Atlanta and Boston

by Silveira-Santos, Tulio, González, Ana Belén Rodríguez, Rangel, Thais, Pozo, Rubén Fernández, Vassallo, Jose Manuel, Díaz, Juan José Vinagre
Published in Transportation (Dordrecht) (01.06.2024)

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Journal Article

Understanding and Predicting Ride-Hailing Fares in Madrid: A Combination of Supervised and Unsupervised Techniques

Using machine learning models to predict the willingness to carry lightweight goods by bike and kick-scooter

Evaluating the performance of highway concessions through public-private partnerships using a fuzzy multi-criteria decision-making procedure

Forecasting Moped Scooter-Sharing Travel Demand Using a Machine Learning Approach

The impact of Low Emission Zones on retail activity: Madrid Central lessons

Were ride-hailing fares affected by the COVID-19 pandemic? Empirical analyses in Atlanta and Boston

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