Journal articles:

• Smith, P. and Sørensen, K. and Nardelli, B. and Chauhan, A. and Christensen, A. and John, M. and Rodrigues, F. and Mariani, P.
  Reconstruction of subsurface ocean state variables using Convolutional Neural Networks with combined satellite and in situ data [PDF]
  Frontiers in Marine Science, 2023
• Parslov, A. and Petersen, N. and Rodrigues, F.
  Short-term bus travel time prediction for transfer synchronization with intelligent uncertainty handling [PDF]
  Expert Systems with Applications, 2023
• Li, Z. and Huang, P. and Wen, C. and Li, J. and Rodrigues, F.
  Prediction of departure delays at original stations using deep learning approaches: A combination of route conflicts and rolling stock connections [PDF]
  Expert Systems with Applications, 2023
• Tygesen, M. and Pereira, F. and Rodrigues, F.
  Unboxing the graph: Towards interpretable graph neural networks for transport prediction through neural relational inference [PDF]
  Transportation research part C: Emerging Technologies, 2023
• Hüttel, F. and Rodrigues, F. and Pereira, F.
  Mind the gap: Modelling difference between censored and uncensored electric vehicle charging demand [PDF]
  Transportation Research Part C: Emerging Technologies, 2023
• Janstrup, K. and Kostic, B. and Møller, M. and Rodrigues, F. and Borysov, S. and Pereira, F.
  Predicting injury-severity for cyclist crashes using natural language processing and neural network modelling [PDF]
  Safety Science, 2023
• Hüttel, F. and Peled, I. and Rodrigues, F. and Pereira, F.
  Modeling Censored Mobility Demand Through Censored Quantile Regression Neural Networks [PDF]
  IEEE Transactions on Intelligent Transportation Systems, 2022
• Rodrigues, F.
  Scaling Bayesian inference of mixed multinomial logit models to very large datasets [PDF] [Code]
  Transportation Research Part B: Methodological, 2022
• Sfeir., G. and Abou-Zeid, M. and Rodrigues, F.
  Gaussian process latent class choice models [PDF]
  Transportation Research Part C: Emerging Technologies, 2022
• Gammelli, D. and Rodrigues, F.
  Recurrent Flow Networks: A Recurrent Latent Variable Model for Density Modelling of Urban Mobility [PDF] [Code]
  in Pattern Recognition, Elsevier, 2022
• Gammelli, D. and Rolsted, K. and Pacino, D. and Rodrigues, F.
  Generalized Multi-Output Gaussian Process Censored Regression [PDF] [Code]
  in Pattern Recognition, Elsevier, 2022
• Li, Z. and Huang, P. and Wen, C. and Jiang, X. and Rodrigues, F.
  Prediction of train arrival delays considering route conflicts at multi-line stations [PDF]
  in Transportation Research Part C: Emerging Technologies, Elsevier, 2022
• Gammelli, D. and Wang, Y. and Prak, D. and Rodrigues, F. and Minner, S. and Pereira, F.
  Predictive and prescriptive performance of bike-sharing demand forecasts for inventory management [PDF] [Code]
  Transportation Research Part C: Emerging Technologies, 2022
• Sfeir, G. and Abou-Zeid, M. and Rodrigues, F. and Pereira, F. and Kaysi, I.
  Semi-nonparametric Latent Class Choice Model with a Flexible Class Membership Component: A Mixture Model Approach
  in Journal of Choice Modelling, Elsevier, 2021
• Kostic, B. and Loft, M., Rodrigues, F. and Borysov, S.
  Deep Survival Modelling for Shared Mobility  [PDF]
  in Transportation Research Part C, Elsevier, 2021
• Rodrigues, F. and Ortelli, N. and Bierlaire, M. and Pereira, F. C.
  Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models [PDF] [Code]
  in IEEE Transactions on Intelligent Transportation Systems, 2020
• Gammelli, D. and Peled, I. and Rodrigues, F. and Pacino, D and Kurtaran, H. and Pereira, F.
  Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes [PDF] [Code]
  in Transportation Research Part C, Elsevier, 2020
• Rodrigues, F. and Pereira, F. C.
  Beyond expectation: Deep joint mean and quantile regression for spatio-temporal problems [PDF] [Code]
  in IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2020
• Markou, I. and Rodrigues, F. and Pereira, F. C.
  Is Travel Demand Actually Deep? An Application in Event Areas Using Semantic Information [PDF]
  in IEEE Transactions on Intelligent Transportation Systems, 2019
• Rodrigues, F. and Markou, I. and Pereira, F. C.
  Combining Time-Series and Textual Data for Taxi Demand Prediction in Event Areas: a Deep Learning Approach [PDF] [Code+Data]
  in Information Fusion, Elsevier, 2018
• Rodrigues, F. and Pereira, F. C.
  Heteroscedastic Gaussian Processes for Uncertainty Modeling in Large-Scale Crowdsourced Traffic Data [PDF]
  in Transportation Research Part C, Elsevier, 2018
• Rodrigues, F. and Henrickson, K. and Pereira, F. C.
  Multi-output Gaussian Processes for Crowdsourced Traffic Data Imputation [PDF] [Code]
  in IEEE Transactions on Intelligent Transportation Systems, 2018
• Petersen, N. and Rodrigues, F. and Pereira, F. C.
  Multi-output Bus Travel Time Prediction with Convolutional LSTM Neural Network
  in Expert Systems with Applications, Elsevier, 2018
• Rodrigues, F. and Lourenço, M. and Ribeiro, B. and Pereira, F. C.
  Learning Supervised Topic Models for Classification and Regression from Crowds [PDF] [Code] [Data]
  in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2017
• Ioulia, M. and Rodrigues, F. and Pereira, F. C.
  Use of Taxi-Trip Data in Analysis of Demand Patterns for Detection and Explanation of Anomalies
  in Transportation Research Record (TRR): Journal of the Transportation Research Board, 2017
• Rodrigues, F. and Borysov, S. and Ribeiro, B. and Pereira, F. C.
  A Bayesian Additive Model for Understanding Public Transport Usage in Special Events [PDF] [Code]
  in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2016
• Pereira, F. C. and Rodrigues, F. and Polisciuc, E and Ben-Akiva, M
  Why So Many People? Explaining Non-Habitual Transport Overcrowding with Internet Data [PDF] [Code]
  in IEEE Transactions on Intelligent Transportation Systems, 2015
• Jiang, S. and Alves, A. and Rodrigues, F. and Pereira, F. C. and Ferreira, J.
  Mining Point-of-Interest Data from Social Networks for Urban Land Use Classification and Disaggregation [PDF]
  in Computers, Environment and Urban Systems, Elsevier, 2015
• Rodrigues, F. and Pereira, F. C. and Ribeiro, B.
  Sequence Labeling with Multiple Annotators [PDF] [Code] [Data]
  in Machine Learning, Springer, 2014
• Rodrigues, F. and Pereira, F. C. and Ribeiro, B.
  Learning from Multiple Annotators: Distinguishing Good from Random Labelers [PDF] [Code] [Data]
  in Pattern Recognition Letters, Elsevier, 2013
• Pereira, F. and Rodrigues, F. and Ben-Akiva, M.
  Text Analysis in Incident Duration Prediction [PDF]
  in Transportation Research Part C, Elsevier, 2013
• Pereira, F. and Rodrigues, F. and Ben-Akiva, M.
  Using data from the web to predict public transport arrivals under special events scenarios [PDF]
  in Journal of Intelligent Transportation Systems, Taylor & Francis, 2013
• Rodrigues, F. and Alves, A. and Polisciuc, E. and Jiang, S. and Ferreira, J. and Pereira, F.
  Estimating Disaggregated Employment Size from Points-of-Interest and Census Data: From Mining the Web to Model Implementation and Visualization
  in International Journal on Advanced Intelligent Systems, vol. 7, 2013

Conference papers:

• Gammelli, D. and Harrison, J. and Yang, K. and Pavone, M. and Rodrigues, F., Pereira, F.
  Graph Reinforcement Learning for Network Control via Bi-Level Optimization [PDF]
  International Conference on Machine Learning (ICML), 2023
• Gammelli, D. and Yang, K and Harrison, J and Rodrigues, F. and Pereira, F. and Pavone, M.
  Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand [PDF] [Code]
  in 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022
• Gammelli, D. and Yang, K and Harrison, J and Rodrigues, F. and Pereira, F. and Pavone, M.
  Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems [PDF] [Code]
  in the 60th IEEE Conference on Decision and Control (CDC), 2021
• Hüttel, F. and Peled, I. and Rodrigues, F. and Pereira, F.
  Deep Spatial Temporal Forecasting of Electrical Vehicle Charging Demand  [PDF]
  in ICML Workshop on Tackling Climate Change with Machine Learning, 2021
• Rodrigues, F. and Azevedo, C. L.
  Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor Failures [PDF] [Code]
  in IEEE Intelligent Transportation Systems Conference (ITSC), 2019
• Rodrigues, F. and Pereira, F. C.
  Deep Learning from Crowds [PDF] [Code] [Data]
  in The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), 2018 (oral)
• Rodrigues, F. and Lourenço, M. and Ribeiro, B. and Pereira, F. C.
  Learning Supervised Topic Models from Crowds [PDF] [Code] [Data]
  in AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2015
• Rodrigues, F. and Pereira, F. C. and Ribeiro, B.
  Gaussian Process Classification and Active Learning with Multiple Annotators [PDF] [Code] [Data]
  in International Conference on Machine Learning (ICML), 2014
• Pereira, F. C. and Rodrigues, F. and Ben-Akiva, M.
  Internet as Sensor: Case Study with Special Events
  in Transportation Research Board (TRB), 2012
• Rodrigues, F. and Pereira, F. C. and Alves, A.O. and Jiang, S. and Ferreira, J.
  Automatic Classification of Points-of-Interest for Land-use Analysis
  in GEOProcessing: The Fourth International Conference on Advanced Geographic Information Systems, Applications, and Services, 2012
• Alves, A. O. and Rodrigues, F. and Pereira, F. C.
  Tagging Space from Information Extraction and Popularity of Points of Interest
  in International Joint Conference on Ambient Intelligence, 2011
• Jiang, S. and Rodrigues, F. and Alves, A. O. and Pereira, F. C. and Ferreira, J.
  Towards an Activity-based Approach for Estimating Travel Destinations
  in World Conference in Transport Research (WCTR), 2010
• Alves, A. O. and Pereira, F. C. and Rodrigues, F. and Oliveirinha, J.
  Place in Perspective: Extracting Online Information About Points of Interest
  in International Joint Conference on Ambient Intelligence, 2010

Book chapters:

• Peled, I. and Rodrigues, F. and Pereira, F.
  Model-based Machine Learning for Transportation
  in Mobility Patterns, Big Data and Transport Analytics, Elsevier, 2019
• Henrickson, K. and Rodrigues, F. and Ghorpade, A.
  Big Data Preparation Challenges and Tools
  in Mobility Patterns, Big Data and Transport Analytics, Elsevier, 2019
• Antunes, F. and O’Sullivan, A. and Rodrigues, F. and Pereira, F. C.
  A Review of Heteroscedasticity Treatment with Gaussian Processes and Quantile Regression Meta-models
  in Seeing Cities Through Big Data, Springer, 2017

Thesis

• Rodrigues, F.
  Probabilistic Models for Learning from Crowdsourced Data [PDF] (PhD thesis)
  2016
• Rodrigues, F.
  POI Mining and Generation (MSc thesis)
  2010

Technical reports

• Rodrigues, F.
  Graphical Models, Inference and Learning [PDF]
  2015
• Rodrigues, F.
  Gaussian Process Classification: A Message-Passing Viewpoint [PDF]
  2014