2 code implementations • 28 Feb 2023 • Carolin Schmidt, Daniele Gammelli, Francisco Camara Pereira, Filipe Rodrigues
Autonomous Mobility-on-Demand (AMoD) systems are an evolving mode of transportation in which a centrally coordinated fleet of self-driving vehicles dynamically serves travel requests.
no code implementations • 20 Feb 2023 • Lorena Torres Lahoz, Francisco Camara Pereira, Georges Sfeir, Ioanna Arkoudi, Mayara Moraes Monteiro, Carlos Lima Azevedo
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.
no code implementations • 3 May 2022 • Vishnu Baburajan, João de Abreu e Silva, Francisco Camara Pereira
So, we propose a modeling framework that allows respondents to use their preferred questionnaire type to answer the survey and enable analysts to use the modeling frameworks of their choice to predict behavior.
no code implementations • 17 Mar 2022 • Mingzhuang Hua, Francisco Camara Pereira, Yu Jiang, Xuewu Chen
The urban transportation system is a combination of multiple transport modes, and the interdependencies across those modes exist.
1 code implementation • 28 Jul 2021 • Daniele Gammelli, Yihua Wang, Dennis Prak, Filipe Rodrigues, Stefan Minner, Francisco Camara Pereira
Bike-sharing systems are a rapidly developing mode of transportation and provide an efficient alternative to passive, motorized personal mobility.
no code implementations • 6 Jul 2020 • Georges Sfeir, Maya Abou-Zeid, Filipe Rodrigues, Francisco Camara Pereira, Isam Kaysi
The proposed model formulates the latent classes using mixture models as an alternative approach to the traditional random utility specification with the aim of comparing the two approaches on various measures including prediction accuracy and representation of heterogeneity in the choice process.
2 code implementations • 3 Feb 2020 • Yafei Han, Francisco Camara Pereira, Moshe Ben-Akiva, Christopher Zegras
Our formulation consists of two modules: a neural network (TasteNet) that learns taste parameters (e. g., time coefficient) as flexible functions of individual characteristics; and a multinomial logit (MNL) model with utility functions defined with expert knowledge.
no code implementations • 7 Mar 2019 • Niklas Christoffer Petersen, Filipe Rodrigues, Francisco Camara Pereira
Accurate and reliable travel time predictions in public transport networks are essential for delivering an attractive service that is able to compete with other modes of transport in urban areas.