
Graph Neural Network Reinforcement Learning for Autonomous MobilityonDemand Systems
Autonomous mobilityondemand (AMoD) systems represent a rapidly develop...
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Shortterm bus travel time prediction for transfer synchronization with intelligent uncertainty handling
This paper presents two novel approaches for uncertainty estimation adap...
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Modeling Censored Mobility Demand through Quantile Regression Neural Networks
Shared mobility services require accurate demand models for effective se...
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Gaussian Process Latent Class Choice Models
We present a Gaussian Process  Latent Class Choice Model (GPLCCM) to i...
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Generalized MultiOutput Gaussian Process Censored Regression
When modelling censored observations, a typical approach in current regr...
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Seminonparametric Latent Class Choice Model with a Flexible Class Membership Component: A Mixture Model Approach
This study presents a seminonparametric Latent Class Choice Model (LCCM...
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Recurrent Flow Networks: A Recurrent Latent Variable Model for SpatioTemporal Density Modelling
When modelling realvalued sequences, a typical approach in current RNN ...
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Scaling Bayesian inference of mixed multinomial logit models to very large datasets
Variational inference methods have been shown to lead to significant imp...
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Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes
Transport demand is highly dependent on supply, especially for shared tr...
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Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models
Specifying utility functions is a key step towards applying the discrete...
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Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor Failures
Reinforcement learning (RL) constitutes a promising solution for allevia...
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Multioutput Bus Travel Time Prediction with Convolutional LSTM Neural Network
Accurate and reliable travel time predictions in public transport networ...
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A Bayesian Additive Model for Understanding Public Transport Usage in Special Events
Public special events, like sports games, concerts and festivals are wel...
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MultiOutput Gaussian Processes for Crowdsourced Traffic Data Imputation
Traffic speed data imputation is a fundamental challenge for datadriven...
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Heteroscedastic Gaussian processes for uncertainty modeling in largescale crowdsourced traffic data
Accurately modeling traffic speeds is a fundamental part of efficient in...
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Beyond expectation: Deep joint mean and quantile regression for spatiotemporal problems
Spatiotemporal problems are ubiquitous and of vital importance in many ...
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Learning Supervised Topic Models for Classification and Regression from Crowds
The growing need to analyze large collections of documents has led to gr...
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Combining timeseries and textual data for taxi demand prediction in event areas: a deep learning approach
Accurate timeseries forecasting is vital for numerous areas of applicat...
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Deep learning from crowds
Over the last few years, deep learning has revolutionized the field of m...
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Filipe Rodrigues
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