289 citations · 636 across the 24 of their papers we have counts for
25 papers
Graph Reinforcement Learning for Network Control via Bi-Level Optimization
Daniele Gammelli, James Harrison, Kaidi Yang +3
Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world problems. However, (1) traditional op…
Context-aware Bayesian Mixed Multinomial Logit Model
Mirosława Łukawska, Anders Fjendbo Jensen, Filipe Rodrigues
The mixed multinomial logit model assumes constant preference parameters of a decision-maker throughout different choice situations, which may be considered too strong for certain…
Representation learning of rare temporal conditions for travel time prediction
Niklas Petersen, Filipe Rodrigues, Francisco Pereira
Predicting travel time under rare temporal conditions (e.g., public holidays, school vacation period, etc.) constitutes a challenge due to the limitation of historical data. If at…
On the importance of stationarity, strong baselines and benchmarks in transport prediction problems
Filipe Rodrigues
Over the last years, the transportation community has witnessed a tremendous amount of research contributions on new deep learning approaches for spatio-temporal forecasting. These…
Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand
Daniele Gammelli, Kaidi Yang, James Harrison +3
Autonomous Mobility-on-Demand (AMoD) systems represent an attractive alternative to existing transportation paradigms, currently challenged by urbanization and increasing travel ne…
Unboxing the graph: Neural Relational Inference for Mobility Prediction
Mathias Niemann Tygesen, Francisco C. Pereira, Filipe Rodrigues
Predicting the supply and demand of transport systems is vital for efficient traffic management, control, optimization, and planning. For example, predicting where from/to and when…
Predictive and Prescriptive Performance of Bike-Sharing Demand Forecasts for Inventory Management
Daniele Gammelli, Yihua Wang, Dennis Prak +3
Bike-sharing systems are a rapidly developing mode of transportation and provide an efficient alternative to passive, motorized personal mobility. The asymmetric nature of bike dem…
Deep Spatio-Temporal Forecasting of Electrical Vehicle Charging Demand
Frederik Boe Hüttel, Inon Peled, Filipe Rodrigues +1
Electric vehicles can offer a low carbon emission solution to reverse rising emission trends. However, this requires that the energy used to meet the demand is green. To meet this…
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems
Daniele Gammelli, Kaidi Yang, James Harrison +3
Autonomous mobility-on-demand (AMoD) systems represent a rapidly developing mode of transportation wherein travel requests are dynamically handled by a coordinated fleet of robotic…
Modeling Censored Mobility Demand through Quantile Regression Neural Networks
Frederik Boe Hüttel, Inon Peled, Filipe Rodrigues +1
Shared mobility services require accurate demand models for effective service planning. On the one hand, modeling the full probability distribution of demand is advantageous becaus…
Gaussian Process Latent Class Choice Models
Georges Sfeir, Filipe Rodrigues, Maya Abou-Zeid
We present a Gaussian Process - Latent Class Choice Model (GP-LCCM) to integrate a non-parametric class of probabilistic machine learning within discrete choice models (DCMs). Gaus…
Generalized Multi-Output Gaussian Process Censored Regression
Daniele Gammelli, Kasper Pryds Rolsted, Dario Pacino +1
When modelling censored observations, a typical approach in current regression methods is to use a censored-Gaussian (i.e. Tobit) model to describe the conditional output distribut…
Semi-nonparametric Latent Class Choice Model with a Flexible Class Membership Component: A Mixture Model Approach
Georges Sfeir, Maya Abou-Zeid, Filipe Rodrigues +2
This study presents a semi-nonparametric Latent Class Choice Model (LCCM) with a flexible class membership component. The proposed model formulates the latent classes using mixture…
Recurrent Flow Networks: A Recurrent Latent Variable Model for Density Modelling of Urban Mobility
Daniele Gammelli, Filipe Rodrigues
Mobility-on-demand (MoD) systems represent a rapidly developing mode of transportation wherein travel requests are dynamically handled by a coordinated fleet of vehicles. Crucially…
Scaling Bayesian inference of mixed multinomial logit models to very large datasets
Filipe Rodrigues
Variational inference methods have been shown to lead to significant improvements in the computational efficiency of approximate Bayesian inference in mixed multinomial logit model…
Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes
Daniele Gammelli, Inon Peled, Filipe Rodrigues +3
Transport demand is highly dependent on supply, especially for shared transport services where availability is often limited. As observed demand cannot be higher than available sup…
Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models
Filipe Rodrigues, Nicola Ortelli, Michel Bierlaire +1
Specifying utility functions is a key step towards applying the discrete choice framework for understanding the behaviour processes that govern user choices. However, identifying t…
Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor Failures
Filipe Rodrigues, Carlos Lima Azevedo
Reinforcement learning (RL) constitutes a promising solution for alleviating the problem of traffic congestion. In particular, deep RL algorithms have been shown to produce adaptiv…
Multi-output Bus Travel Time Prediction with Convolutional LSTM Neural Network
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…
A Bayesian Additive Model for Understanding Public Transport Usage in Special Events
Filipe Rodrigues, Stanislav S. Borysov, Bernardete Ribeiro +1
Public special events, like sports games, concerts and festivals are well known to create disruptions in transportation systems, often catching the operators by surprise. Although…
Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data
Filipe Rodrigues, Francisco C. Pereira
Accurately modeling traffic speeds is a fundamental part of efficient intelligent transportation systems. Nowadays, with the widespread deployment of GPS-enabled devices, it has be…
Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation
Filipe Rodrigues, Kristian Henrickson, Francisco C. Pereira
Traffic speed data imputation is a fundamental challenge for data-driven transport analysis. In recent years, with the ubiquity of GPS-enabled devices and the widespread use of cro…
Beyond expectation: Deep joint mean and quantile regression for spatio-temporal problems
Filipe Rodrigues, Francisco C. Pereira
Spatio-temporal problems are ubiquitous and of vital importance in many research fields. Despite the potential already demonstrated by deep learning methods in modeling spatio-temp…
Learning Supervised Topic Models for Classification and Regression from Crowds
Filipe Rodrigues, Mariana Lourenço, Bernardete Ribeiro +1
The growing need to analyze large collections of documents has led to great developments in topic modeling. Since documents are frequently associated with other related variables,…