2,518 research outputs found
Spatial-Temporal Hypergraph Neural Network for Traffic Forecasting
Traffic forecasting, which benefits from mobile Internet development and
position technologies, plays a critical role in Intelligent Transportation
Systems. It helps to implement rich and varied transportation applications and
bring convenient transportation services to people based on collected traffic
data. Most existing methods usually leverage graph-based deep learning networks
to model the complex road network for traffic forecasting shallowly. Despite
their effectiveness, these methods are generally limited in fully capturing
high-order spatial dependencies caused by road network topology and high-order
temporal dependencies caused by traffic dynamics. To tackle the above issues,
we focus on the essence of traffic system and propose STHODE: Spatio-Temporal
Hypergraph Neural Ordinary Differential Equation Network, which combines road
network topology and traffic dynamics to capture high-order spatio-temporal
dependencies in traffic data. Technically, STHODE consists of a spatial module
and a temporal module. On the one hand, we construct a spatial hypergraph and
leverage an adaptive MixHop hypergraph ODE network to capture high-order
spatial dependencies. On the other hand, we utilize a temporal hypergraph and
employ a hyperedge evolving ODE network to capture high-order temporal
dependencies. Finally, we aggregate the outputs of stacked STHODE layers to
mutually enhance the prediction performance. Extensive experiments conducted on
four real-world traffic datasets demonstrate the superior performance of our
proposed model compared to various baselines
Spatio-Temporal Meta Contrastive Learning
Spatio-temporal prediction is crucial in numerous real-world applications,
including traffic forecasting and crime prediction, which aim to improve public
transportation and safety management. Many state-of-the-art models demonstrate
the strong capability of spatio-temporal graph neural networks (STGNN) to
capture complex spatio-temporal correlations. However, despite their
effectiveness, existing approaches do not adequately address several key
challenges. Data quality issues, such as data scarcity and sparsity, lead to
data noise and a lack of supervised signals, which significantly limit the
performance of STGNN. Although recent STGNN models with contrastive learning
aim to address these challenges, most of them use pre-defined augmentation
strategies that heavily depend on manual design and cannot be customized for
different Spatio-Temporal Graph (STG) scenarios. To tackle these challenges, we
propose a new spatio-temporal contrastive learning (CL4ST) framework to encode
robust and generalizable STG representations via the STG augmentation paradigm.
Specifically, we design the meta view generator to automatically construct node
and edge augmentation views for each disentangled spatial and temporal graph in
a data-driven manner. The meta view generator employs meta networks with
parameterized generative model to customize the augmentations for each input.
This personalizes the augmentation strategies for every STG and endows the
learning framework with spatio-temporal-aware information. Additionally, we
integrate a unified spatio-temporal graph attention network with the proposed
meta view generator and two-branch graph contrastive learning paradigms.
Extensive experiments demonstrate that our CL4ST significantly improves
performance over various state-of-the-art baselines in traffic and crime
prediction.Comment: 32nd ACM International Conference on Information and Knowledge
Management (CIKM' 23
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
Graph deep learning model for network-based predictive hotspot mapping of sparse spatio-temporal events
The predictive hotspot mapping of sparse spatio-temporal events (e.g., crime and traffic accidents) aims to forecast areas or locations with higher average risk of event occurrence, which is important to offer insight for preventative strategies. Although a network-based structure can better capture the micro-level variation of spatio-temporal events, existing deep learning methods of sparse events forecasting are either based on area or grid units due to the data sparsity in both space and time, and the complex network topology. To overcome these challenges, this paper develops the first deep learning (DL) model for network-based predictive mapping of sparse spatio-temporal events. Leveraging a graph-based representation of the network-structured data, a gated localised diffusion network (GLDNet) is introduced, which integrating a gated network to model the temporal propagation and a novel localised diffusion network to model the spatial propagation confined by the network topology. To deal with the sparsity issue, we reformulate the research problem as an imbalance regression task and employ a weighted loss function to train the DL model. The framework is validated on a crime forecasting case of South Chicago, USA, which outperforms the state-of-the-art benchmark by 12% and 25% in terms of the mean hit rate at 10% and 20% coverage level, respectively
SMAP: A Novel Heterogeneous Information Framework for Scenario-based Optimal Model Assignment
The increasing maturity of big data applications has led to a proliferation
of models targeting the same objectives within the same scenarios and datasets.
However, selecting the most suitable model that considers model's features
while taking specific requirements and constraints into account still poses a
significant challenge. Existing methods have focused on worker-task assignments
based on crowdsourcing, they neglect the scenario-dataset-model assignment
problem. To address this challenge, a new problem named the Scenario-based
Optimal Model Assignment (SOMA) problem is introduced and a novel framework
entitled Scenario and Model Associative percepts (SMAP) is developed. SMAP is a
heterogeneous information framework that can integrate various types of
information to intelligently select a suitable dataset and allocate the optimal
model for a specific scenario. To comprehensively evaluate models, a new score
function that utilizes multi-head attention mechanisms is proposed. Moreover, a
novel memory mechanism named the mnemonic center is developed to store the
matched heterogeneous information and prevent duplicate matching. Six popular
traffic scenarios are selected as study cases and extensive experiments are
conducted on a dataset to verify the effectiveness and efficiency of SMAP and
the score function
Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting
Traffic forecasting is of great importance to transportation management and
public safety, and very challenging due to the complicated spatial-temporal
dependency and essential uncertainty brought about by the road network and
traffic conditions. Latest studies mainly focus on modeling the spatial
dependency by utilizing graph convolutional networks (GCNs) throughout a fixed
weighted graph. However, edges, i.e., the correlations between pair-wise nodes,
are much more complicated and interact with each other. In this paper, we
propose the Multi-Range Attentive Bicomponent GCN (MRA-BGCN), a novel deep
learning model for traffic forecasting. We first build the node-wise graph
according to the road network distance and the edge-wise graph according to
various edge interaction patterns. Then, we implement the interactions of both
nodes and edges using bicomponent graph convolution. The multi-range attention
mechanism is introduced to aggregate information in different neighborhood
ranges and automatically learn the importance of different ranges. Extensive
experiments on two real-world road network traffic datasets, METR-LA and
PEMS-BAY, show that our MRA-BGCN achieves the state-of-the-art results.Comment: Accepted by AAAI 202
A novel wind power prediction model using graph attention networks and bi-directional deep learning long and short term memory
Today, integrating wind energy forecasting is an important area of research due to the erratic nature of wind. To achieve this goal, we propose a new model of wind speed prediction based on graph attention networks (GAT), we added a new attention mechanism and a learnable adjacency matrix to the GAT structure to obtain attention scores for each weather variable. The results of the GAT-based model are merged with the bi-directional deep learning long and short-term memory (BiLSTM) layer to take advantage of the geographic and temporal properties of historical weather data. The experiments and analyzes are carried out using precise meteorological data collected from wind farms in the Moroccan city of Tetouan. We show that the proposed model can learn complex input-output correlations of meteorological data more efficiently than previous wind speed prediction algorithms. Due to the resulting attention weights, the model also provides more information about the main weather factors for the evaluated forecast work
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