91 research outputs found
Spatiotemporal Propagation Learning for Network-Wide Flight Delay Prediction
Demystifying the delay propagation mechanisms among multiple airports is
fundamental to precise and interpretable delay prediction, which is crucial
during decision-making for all aviation industry stakeholders. The principal
challenge lies in effectively leveraging the spatiotemporal dependencies and
exogenous factors related to the delay propagation. However, previous works
only consider limited spatiotemporal patterns with few factors. To promote more
comprehensive propagation modeling for delay prediction, we propose
SpatioTemporal Propagation Network (STPN), a space-time separable graph
convolutional network, which is novel in spatiotemporal dependency capturing.
From the aspect of spatial relation modeling, we propose a multi-graph
convolution model considering both geographic proximity and airline schedule.
From the aspect of temporal dependency capturing, we propose a multi-head
self-attentional mechanism that can be learned end-to-end and explicitly reason
multiple kinds of temporal dependency of delay time series. We show that the
joint spatial and temporal learning models yield a sum of the Kronecker
product, which factors the spatiotemporal dependence into the sum of several
spatial and temporal adjacency matrices. By this means, STPN allows cross-talk
of spatial and temporal factors for modeling delay propagation. Furthermore, a
squeeze and excitation module is added to each layer of STPN to boost
meaningful spatiotemporal features. To this end, we apply STPN to multi-step
ahead arrival and departure delay prediction in large-scale airport networks.
To validate the effectiveness of our model, we experiment with two real-world
delay datasets, including U.S and China flight delays; and we show that STPN
outperforms state-of-the-art methods. In addition, counterfactuals produced by
STPN show that it learns explainable delay propagation patterns.Comment: 14 pages,8 figure
Inductive Graph Neural Networks for Spatiotemporal Kriging
Time series forecasting and spatiotemporal kriging are the two most important
tasks in spatiotemporal data analysis. Recent research on graph neural networks
has made substantial progress in time series forecasting, while little
attention has been paid to the kriging problem -- recovering signals for
unsampled locations/sensors. Most existing scalable kriging methods (e.g.,
matrix/tensor completion) are transductive, and thus full retraining is
required when we have a new sensor to interpolate. In this paper, we develop an
Inductive Graph Neural Network Kriging (IGNNK) model to recover data for
unsampled sensors on a network/graph structure. To generalize the effect of
distance and reachability, we generate random subgraphs as samples and
reconstruct the corresponding adjacency matrix for each sample. By
reconstructing all signals on each sample subgraph, IGNNK can effectively learn
the spatial message passing mechanism. Empirical results on several real-world
spatiotemporal datasets demonstrate the effectiveness of our model. In
addition, we also find that the learned model can be successfully transferred
to the same type of kriging tasks on an unseen dataset. Our results show that:
1) GNN is an efficient and effective tool for spatial kriging; 2) inductive
GNNs can be trained using dynamic adjacency matrices; 3) a trained model can be
transferred to new graph structures and 4) IGNNK can be used to generate
virtual sensors.Comment: AAAI 202
March in Chat: Interactive Prompting for Remote Embodied Referring Expression
Many Vision-and-Language Navigation (VLN) tasks have been proposed in recent
years, from room-based to object-based and indoor to outdoor. The REVERIE
(Remote Embodied Referring Expression) is interesting since it only provides
high-level instructions to the agent, which are closer to human commands in
practice. Nevertheless, this poses more challenges than other VLN tasks since
it requires agents to infer a navigation plan only based on a short
instruction. Large Language Models (LLMs) show great potential in robot action
planning by providing proper prompts. Still, this strategy has not been
explored under the REVERIE settings. There are several new challenges. For
example, the LLM should be environment-aware so that the navigation plan can be
adjusted based on the current visual observation. Moreover, the LLM planned
actions should be adaptable to the much larger and more complex REVERIE
environment. This paper proposes a March-in-Chat (MiC) model that can talk to
the LLM on the fly and plan dynamically based on a newly proposed
Room-and-Object Aware Scene Perceiver (ROASP). Our MiC model outperforms the
previous state-of-the-art by large margins by SPL and RGSPL metrics on the
REVERIE benchmark.Comment: Accepted by ICCV 202
Spatial-temporal traffic modeling with a fusion graph reconstructed by tensor decomposition
Accurate spatial-temporal traffic flow forecasting is essential for helping
traffic managers to take control measures and drivers to choose the optimal
travel routes. Recently, graph convolutional networks (GCNs) have been widely
used in traffic flow prediction owing to their powerful ability to capture
spatial-temporal dependencies. The design of the spatial-temporal graph
adjacency matrix is a key to the success of GCNs, and it is still an open
question. This paper proposes reconstructing the binary adjacency matrix via
tensor decomposition, and a traffic flow forecasting method is proposed. First,
we reformulate the spatial-temporal fusion graph adjacency matrix into a
three-way adjacency tensor. Then, we reconstructed the adjacency tensor via
Tucker decomposition, wherein more informative and global spatial-temporal
dependencies are encoded. Finally, a Spatial-temporal Synchronous Graph
Convolutional module for localized spatial-temporal correlations learning and a
Dilated Convolution module for global correlations learning are assembled to
aggregate and learn the comprehensive spatial-temporal dependencies of the road
network. Experimental results on four open-access datasets demonstrate that the
proposed model outperforms state-of-the-art approaches in terms of the
prediction performance and computational cost.Comment: 11 pages, 8 figure
Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization Approach
Time series analysis is a fundamental task in various application domains,
and deep learning approaches have demonstrated remarkable performance in this
area. However, many real-world time series data exhibit significant periodic or
quasi-periodic dynamics that are often not adequately captured by existing deep
learning-based solutions. This results in an incomplete representation of the
underlying dynamic behaviors of interest. To address this gap, we propose an
unsupervised method called Floss that automatically regularizes learned
representations in the frequency domain. The Floss method first automatically
detects major periodicities from the time series. It then employs periodic
shift and spectral density similarity measures to learn meaningful
representations with periodic consistency. In addition, Floss can be easily
incorporated into both supervised, semi-supervised, and unsupervised learning
frameworks. We conduct extensive experiments on common time series
classification, forecasting, and anomaly detection tasks to demonstrate the
effectiveness of Floss. We incorporate Floss into several representative deep
learning solutions to justify our design choices and demonstrate that it is
capable of automatically discovering periodic dynamics and improving
state-of-the-art deep learning models.Comment: 12 page
Explainable and Safe Reinforcement Learning for Autonomous Air Mobility
Increasing traffic demands, higher levels of automation, and communication
enhancements provide novel design opportunities for future air traffic
controllers (ATCs). This article presents a novel deep reinforcement learning
(DRL) controller to aid conflict resolution for autonomous free flight.
Although DRL has achieved important advancements in this field, the existing
works pay little attention to the explainability and safety issues related to
DRL controllers, particularly the safety under adversarial attacks. To address
those two issues, we design a fully explainable DRL framework wherein we: 1)
decompose the coupled Q value learning model into a safety-awareness and
efficiency (reach the target) one; and 2) use information from surrounding
intruders as inputs, eliminating the needs of central controllers. In our
simulated experiments, we show that by decoupling the safety-awareness and
efficiency, we can exceed performance on free flight control tasks while
dramatically improving explainability on practical. In addition, the safety Q
learning module provides rich information about the safety situation of
environments. To study the safety under adversarial attacks, we additionally
propose an adversarial attack strategy that can impose both safety-oriented and
efficiency-oriented attacks. The adversarial aims to minimize safety/efficiency
by only attacking the agent at a few time steps. In the experiments, our attack
strategy increases as many collisions as the uniform attack (i.e., attacking at
every time step) by only attacking the agent four times less often, which
provide insights into the capabilities and restrictions of the DRL in future
ATC designs. The source code is publicly available at
https://github.com/WLeiiiii/Gym-ATC-Attack-Project
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