Zobrazeno 1 - 10
of 1 092
pro vyhledávání: '"Azevedo, Carlos A."'
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems, 2024
Cycling is critical for cities to transition to more sustainable transport modes. Yet, safety concerns remain a critical deterrent for individuals to cycle. If individuals perceive an environment as unsafe for cycling, it is likely that they will pre
Externí odkaz:
http://arxiv.org/abs/2412.09835
Autor:
Nguyen, Dang Viet Anh, Flensburg, J. Victor, Cerreto, Fabrizio, Pascariu, Bianca, Pellegrini, Paola, Azevedo, Carlos Lima, Rodrigues, Filipe
With the expansion of cities over time, URT (Urban Rail Transit) networks have also grown significantly. Demand prediction plays an important role in supporting planning, scheduling, fleet management, and other operational decisions. In this study, w
Externí odkaz:
http://arxiv.org/abs/2408.15619
Emotions play a significant role in the cognitive processes of the human brain, such as decision making, learning and perception. The use of physiological signals has shown to lead to more objective, reliable and accurate emotion recognition combined
Externí odkaz:
http://arxiv.org/abs/2308.09013
Autor:
Riis, Christoffer, Antunes, Francisco N., Bolić, Tatjana, Gurtner, Gérald, Cook, Andrew, Azevedo, Carlos Lima, Pereira, Francisco Câmara
The use of Air traffic management (ATM) simulators for planing and operations can be challenging due to their modelling complexity. This paper presents XALM (eXplainable Active Learning Metamodel), a three-step framework integrating active learning a
Externí odkaz:
http://arxiv.org/abs/2308.03404
Today, many cities seek to transition to more sustainable transportation systems. Cycling is critical in this transition for shorter trips, including first-and-last-mile links to transit. Yet, if individuals perceive cycling as unsafe, they will not
Externí odkaz:
http://arxiv.org/abs/2307.13397
Autor:
Lahoz, Lorena Torres, Pereira, Francisco Camara, Sfeir, Georges, Arkoudi, Ioanna, Monteiro, Mayara Moraes, Azevedo, Carlos Lima
Latent Class Choice Models (LCCM) are extensions of discrete choice models (DCMs) that capture unobserved heterogeneity in the choice process by segmenting the population based on the assumption of preference similarities. We present a method of effi
Externí odkaz:
http://arxiv.org/abs/2302.09871
Autor:
Monteiro, Mayara Moraes, Azevedo, Carlos M. Lima, Kamargianni, Maria, Shiftan, Yoram, Gal-Tzur, Ayelet, Tavory, Sharon Shoshany, Antoniou, Constantinos, Cantelmo, Guido
Car-sharing services have been providing short-term car access to their users, contributing to sustainable urban mobility and generating positive societal and often environmental impacts. As car-sharing business models vary, it is important to unders
Externí odkaz:
http://arxiv.org/abs/2206.02448
Autor:
Riis, Christoffer, Antunes, Francisco, Hüttel, Frederik Boe, Azevedo, Carlos Lima, Pereira, Francisco Câmara
The bias-variance trade-off is a well-known problem in machine learning that only gets more pronounced the less available data there is. In active learning, where labeled data is scarce or difficult to obtain, neglecting this trade-off can cause inef
Externí odkaz:
http://arxiv.org/abs/2205.10186
Autor:
Riis, Christoffer, Antunes, Francisco, Bolić, Tatjana, Gurtner, Gérald, Cook, Andrew, Azevedo, Carlos Lima, Pereira, Francisco Câmara
Publikováno v:
In Transportation Research Part C September 2024 166
This study proposes a novel approach that combines theory and data-driven choice models using Artificial Neural Networks (ANNs). In particular, we use continuous vector representations, called embeddings, for encoding categorical or discrete explanat
Externí odkaz:
http://arxiv.org/abs/2109.12042