18 research outputs found
Recurrent Flow Networks: A Recurrent Latent Variable Model for Density Modelling of Urban Mobility
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, the efficiency of an MoD system highly depends on
how well supply and demand distributions are aligned in spatio-temporal space
(i.e., to satisfy user demand, cars have to be available in the correct place
and at the desired time). To do so, we argue that predictive models should aim
to explicitly disentangle between temporal} and spatial variability in the
evolution of urban mobility demand. However, current approaches typically
ignore this distinction by either treating both sources of variability jointly,
or completely ignoring their presence in the first place. In this paper, we
propose recurrent flow networks (RFN), where we explore the inclusion of (i)
latent random variables in the hidden state of recurrent neural networks to
model temporal variability, and (ii) normalizing flows to model the spatial
distribution of mobility demand. We demonstrate how predictive models
explicitly disentangling between spatial and temporal variability exhibit
several desirable properties, and empirically show how this enables the
generation of distributions matching potentially complex urban topologies.Comment: 16 pages, 6 figure
Generalized Multi-Output Gaussian Process Censored Regression
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 distribution. In this paper, as in the case of missing
data, we argue that exploiting correlations between multiple outputs can enable
models to better address the bias introduced by censored data. To do so, we
introduce a heteroscedastic multi-output Gaussian process model which combines
the non-parametric flexibility of GPs with the ability to leverage information
from correlated outputs under input-dependent noise conditions. To address the
resulting inference intractability, we further devise a variational bound to
the marginal log-likelihood suitable for stochastic optimization. We
empirically evaluate our model against other generative models for censored
data on both synthetic and real world tasks and further show how it can be
generalized to deal with arbitrary likelihood functions. Results show how the
added flexibility allows our model to better estimate the underlying
non-censored (i.e. true) process under potentially complex censoring dynamics.Comment: 7 pages, 3 figures, 3 table
Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning
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. The control of these systems is typically
formulated as a large network optimization problem, and reinforcement learning
(RL) has recently emerged as a promising approach to solve the open challenges
in this space. Recent centralized RL approaches focus on learning from online
data, ignoring the per-sample-cost of interactions within real-world
transportation systems. To address these limitations, we propose to formalize
the control of AMoD systems through the lens of offline reinforcement learning
and learn effective control strategies using solely offline data, which is
readily available to current mobility operators. We further investigate design
decisions and provide empirical evidence based on data from real-world mobility
systems showing how offline learning allows to recover AMoD control policies
that (i) exhibit performance on par with online methods, (ii) allow for
sample-efficient online fine-tuning and (iii) eliminate the need for complex
simulation environments. Crucially, this paper demonstrates that offline RL is
a promising paradigm for the application of RL-based solutions within
economically-critical systems, such as mobility systems
Real-time Control of Electric Autonomous Mobility-on-Demand Systems via Graph Reinforcement Learning
Operators of Electric Autonomous Mobility-on-Demand (E-AMoD) fleets need to
make several real-time decisions such as matching available cars to ride
requests, rebalancing idle cars to areas of high demand, and charging vehicles
to ensure sufficient range. While this problem can be posed as a linear program
that optimizes flows over a space-charge-time graph, the size of the resulting
optimization problem does not allow for real-time implementation in realistic
settings. In this work, we present the E-AMoD control problem through the lens
of reinforcement learning and propose a graph network-based framework to
achieve drastically improved scalability and superior performance over
heuristics. Specifically, we adopt a bi-level formulation where we (1) leverage
a graph network-based RL agent to specify a desired next state in the
space-charge graph, and (2) solve more tractable linear programs to best
achieve the desired state while ensuring feasibility. Experiments using
real-world data from San Francisco and New York City show that our approach
achieves up to 89% of the profits of the theoretically-optimal solution while
achieving more than a 100x speedup in computational time. Furthermore, our
approach outperforms the best domain-specific heuristics with comparable
runtimes, with an increase in profits by up to 3x. Finally, we highlight
promising zero-shot transfer capabilities of our learned policy on tasks such
as inter-city generalization and service area expansion, thus showing the
utility, scalability, and flexibility of our framework.Comment: 9 page
Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes
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 supply, historical transport data typically
represents a biased, or censored, version of the true underlying demand
pattern. Without explicitly accounting for this inherent distinction,
predictive models of demand would necessarily represent a biased version of
true demand, thus less effectively predicting the needs of service users. To
counter this problem, we propose a general method for censorship-aware demand
modeling, for which we devise a censored likelihood function. We apply this
method to the task of shared mobility demand prediction by incorporating the
censored likelihood within a Gaussian Process model, which can flexibly
approximate arbitrary functional forms. Experiments on artificial and
real-world datasets show how taking into account the limiting effect of supply
on demand is essential in the process of obtaining an unbiased predictive model
of user demand behavior.Comment: 21 pages, 10 figure
Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis
Multiple Sclerosis (MS) progresses at an unpredictable rate, but predictions on the disease course in each patient would be extremely useful to tailor therapy to the individual needs. We explore different machine learning (ML) approaches to predict whether a patient will shift from the initial Relapsing-Remitting (RR) to the Secondary Progressive (SP) form of the disease, using only “real world” data available in clinical routine. The clinical records of 1624 outpatients (207 in the SP phase) attending the MS service of Sant'Andrea hospital, Rome, Italy, were used. Predictions at 180, 360 or 720 days from the last visit were obtained considering either the data of the last available visit (Visit-Oriented setting), comparing four classical ML methods (Random Forest, Support Vector Machine, K-Nearest Neighbours and AdaBoost) or the whole clinical history of each patient (History-Oriented setting), using a Recurrent Neural Network model, specifically designed for historical data. Missing values were handled by removing either all clinical records presenting at least one missing parameter (Feature-saving approach) or the 3 clinical parameters which contained missing values (Record-saving approach). The performances of the classifiers were rated using common indicators, such as Recall (or Sensitivity) and Precision (or Positive predictive value). In the visit-oriented setting, the Record-saving approach yielded Recall values from 70% to 100%, but low Precision (5% to 10%), which however increased to 50% when considering only predictions for which the model returned a probability above a given “confidence threshold”. For the History-oriented setting, both indicators increased as prediction time lengthened, reaching values of 67% (Recall) and 42% (Precision) at 720 days. We show how “real world” data can be effectively used to forecast the evolution of MS, leading to high Recall values and propose innovative approaches to improve Precision towards clinically useful values