Search Results for author: Francisco Camara Pereira

Found 8 papers, 3 papers with code

Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning

2 code implementations28 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.

Offline RL reinforcement-learning +2

Attitudes and Latent Class Choice Models using Machine learning

no code implementations20 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.

Discrete Choice Models

Open vs Closed-ended questions in attitudinal surveys -- comparing, combining, and interpreting using natural language processing

no code implementations3 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.

Autonomous Vehicles

Transfer learning for cross-modal demand prediction of bike-share and public transit

no code implementations17 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.

Time Series Time Series Analysis +1

Predictive and Prescriptive Performance of Bike-Sharing Demand Forecasts for Inventory Management

1 code implementation28 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.

Decision Making Management

Semi-nonparametric Latent Class Choice Model with a Flexible Class Membership Component: A Mixture Model Approach

no code implementations6 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.

Clustering Discrete Choice Models

A Neural-embedded Choice Model: TasteNet-MNL Modeling Taste Heterogeneity with Flexibility and Interpretability

2 code implementations3 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.

Benchmarking Discrete Choice Models

Multi-output Bus Travel Time Prediction with Convolutional LSTM Neural Network

no code implementations7 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.

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