422 research outputs found
Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation
Metro origin-destination prediction is a crucial yet challenging time-series
analysis task in intelligent transportation systems, which aims to accurately
forecast two specific types of cross-station ridership, i.e.,
Origin-Destination (OD) one and Destination-Origin (DO) one. However, complete
OD matrices of previous time intervals can not be obtained immediately in
online metro systems, and conventional methods only used limited information to
forecast the future OD and DO ridership separately. In this work, we proposed a
novel neural network module termed Heterogeneous Information Aggregation
Machine (HIAM), which fully exploits heterogeneous information of historical
data (e.g., incomplete OD matrices, unfinished order vectors, and DO matrices)
to jointly learn the evolutionary patterns of OD and DO ridership.
Specifically, an OD modeling branch estimates the potential destinations of
unfinished orders explicitly to complement the information of incomplete OD
matrices, while a DO modeling branch takes DO matrices as input to capture the
spatial-temporal distribution of DO ridership. Moreover, a Dual Information
Transformer is introduced to propagate the mutual information among OD features
and DO features for modeling the OD-DO causality and correlation. Based on the
proposed HIAM, we develop a unified Seq2Seq network to forecast the future OD
and DO ridership simultaneously. Extensive experiments conducted on two
large-scale benchmarks demonstrate the effectiveness of our method for online
metro origin-destination prediction
Triformer:Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Version
A variety of real-world applications rely on far future information to make
decisions, thus calling for efficient and accurate long sequence multivariate
time series forecasting. While recent attention-based forecasting models show
strong abilities in capturing long-term dependencies, they still suffer from
two key limitations. First, canonical self attention has a quadratic complexity
w.r.t. the input time series length, thus falling short in efficiency. Second,
different variables' time series often have distinct temporal dynamics, which
existing studies fail to capture, as they use the same model parameter space,
e.g., projection matrices, for all variables' time series, thus falling short
in accuracy. To ensure high efficiency and accuracy, we propose Triformer, a
triangular, variable-specific attention. (i) Linear complexity: we introduce a
novel patch attention with linear complexity. When stacking multiple layers of
the patch attentions, a triangular structure is proposed such that the layer
sizes shrink exponentially, thus maintaining linear complexity. (ii)
Variable-specific parameters: we propose a light-weight method to enable
distinct sets of model parameters for different variables' time series to
enhance accuracy without compromising efficiency and memory usage. Strong
empirical evidence on four datasets from multiple domains justifies our design
choices, and it demonstrates that Triformer outperforms state-of-the-art
methods w.r.t. both accuracy and efficiency. This is an extended version of
"Triformer: Triangular, Variable-Specific Attentions for Long Sequence
Multivariate Time Series Forecasting", to appear in IJCAI 2022 [Cirstea et al.,
2022a], including additional experimental results
Spatio-Temporal Dual Graph Neural Networks for Travel Time Estimation
Travel time estimation is one of the core tasks for the development of
intelligent transportation systems. Most previous works model the road segments
or intersections separately by learning their spatio-temporal characteristics
to estimate travel time. However, due to the continuous alternations of the
road segments and intersections in a path, the dynamic features are supposed to
be coupled and interactive. Therefore, modeling one of them limits further
improvement in accuracy of estimating travel time. To address the above
problems, a novel graph-based deep learning framework for travel time
estimation is proposed in this paper, namely Spatio-Temporal Dual Graph Neural
Networks (STDGNN). Specifically, we first establish the node-wise and edge-wise
graphs to respectively characterize the adjacency relations of intersections
and that of road segments. In order to extract the joint spatio-temporal
correlations of the intersections and road segments, we adopt the
spatio-temporal dual graph learning approach that incorporates multiple
spatial-temporal dual graph learning modules with multi-scale network
architectures for capturing multi-level spatial-temporal information from the
dual graph. Finally, we employ the multi-task learning approach to estimate the
travel time of a given whole route, each road segment and intersection
simultaneously. We conduct extensive experiments to evaluate our proposed model
on three real-world trajectory datasets, and the experimental results show that
STDGNN significantly outperforms several state-of-art baselines
Inductive Graph Transformer for Delivery Time Estimation
Providing accurate estimated time of package delivery on users' purchasing
pages for e-commerce platforms is of great importance to their purchasing
decisions and post-purchase experiences. Although this problem shares some
common issues with the conventional estimated time of arrival (ETA), it is more
challenging with the following aspects: 1) Inductive inference. Models are
required to predict ETA for orders with unseen retailers and addresses; 2)
High-order interaction of order semantic information. Apart from the
spatio-temporal features, the estimated time also varies greatly with other
factors, such as the packaging efficiency of retailers, as well as the
high-order interaction of these factors. In this paper, we propose an inductive
graph transformer (IGT) that leverages raw feature information and structural
graph data to estimate package delivery time. Different from previous graph
transformer architectures, IGT adopts a decoupled pipeline and trains
transformer as a regression function that can capture the multiplex information
from both raw feature and dense embeddings encoded by a graph neural network
(GNN). In addition, we further simplify the GNN structure by removing its
non-linear activation and the learnable linear transformation matrix. The
reduced parameter search space and linear information propagation in the
simplified GNN enable the IGT to be applied in large-scale industrial
scenarios. Experiments on real-world logistics datasets show that our proposed
model can significantly outperform the state-of-the-art methods on estimation
of delivery time. The source code is available at:
https://github.com/enoche/IGT-WSDM23.Comment: 9 pages, accepted to WSDM 202
Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full Version
Traffic time series forecasting is challenging due to complex spatio-temporal
dynamics time series from different locations often have distinct patterns; and
for the same time series, patterns may vary across time, where, for example,
there exist certain periods across a day showing stronger temporal
correlations. Although recent forecasting models, in particular deep learning
based models, show promising results, they suffer from being spatio-temporal
agnostic. Such spatio-temporal agnostic models employ a shared parameter space
irrespective of the time series locations and the time periods and they assume
that the temporal patterns are similar across locations and do not evolve
across time, which may not always hold, thus leading to sub-optimal results. In
this work, we propose a framework that aims at turning spatio-temporal agnostic
models to spatio-temporal aware models. To do so, we encode time series from
different locations into stochastic variables, from which we generate
location-specific and time-varying model parameters to better capture the
spatio-temporal dynamics. We show how to integrate the framework with canonical
attentions to enable spatio-temporal aware attentions. Next, to compensate for
the additional overhead introduced by the spatio-temporal aware model parameter
generation process, we propose a novel window attention scheme, which helps
reduce the complexity from quadratic to linear, making spatio-temporal aware
attentions also have competitive efficiency. We show strong empirical evidence
on four traffic time series datasets, where the proposed spatio-temporal aware
attentions outperform state-of-the-art methods in term of accuracy and
efficiency. This is an extended version of "Towards Spatio-Temporal Aware
Traffic Time Series Forecasting", to appear in ICDE 2022 [1], including
additional experimental results.Comment: Accepted at ICDE 202
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