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Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes
Published in Transportation Research Part C: Emerging Technologies, 2020
How can we estimate the true (i.e. latent) demand of mobility from incomplete historical observations? This paper approaches this problem by introducing a novel Gaussian process architecture to overcome the censored nature of the demand process.
Recurrent Flow Networks: A Recurrent Latent Variable Model for Spatio-Temporal Density Modelling
Published in (Submitted), 2020
Spatio-Temporal density estimation is one the open challenges in machine learning and statistical sciences. In this work we introduce Recurrent Flow Netwroks, a novel generative neural architecture to model complex data distributions. We show how these models can effectively model and predict future urban mobility distributions.
Generalized Multi-Output Gaussian Process Censored Regression
Published in (Submitted), 2020
In this work, we show how (i) exploiting correlations between multiple signals (i.e. multi-output) and (ii) allow for input-dependent noise (i.e. heteroscedasticity) can fundamentally improve censored data estimation.
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems
Published in (Submitted), 2021
In this work, we propose the use of graph neural networks to centrally control AMoD systems. We argue that graph neural networks exhibit a number of desirable properties, and propose an actor-critic formulation as a general approach to learn proactive, scalable, and transferable rebalancing policies.