494 research outputs found
Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks
Accurate and timely air quality and weather predictions are of great
importance to urban governance and human livelihood. Though many efforts have
been made for air quality or weather prediction, most of them simply employ one
another as feature input, which ignores the inner-connection between two
predictive tasks. On the one hand, the accurate prediction of one task can help
improve another task's performance. On the other hand, geospatially distributed
air quality and weather monitoring stations provide additional hints for
city-wide spatiotemporal dependency modeling. Inspired by the above two
insights, in this paper, we propose the Multi-adversarial spatiotemporal
recurrent Graph Neural Networks (MasterGNN) for joint air quality and weather
predictions. Specifically, we first propose a heterogeneous recurrent graph
neural network to model the spatiotemporal autocorrelation among air quality
and weather monitoring stations. Then, we develop a multi-adversarial graph
learning framework to against observation noise propagation introduced by
spatiotemporal modeling. Moreover, we present an adaptive training strategy by
formulating multi-adversarial learning as a multi-task learning problem.
Finally, extensive experiments on two real-world datasets show that MasterGNN
achieves the best performance compared with seven baselines on both air quality
and weather prediction tasks.Comment: 9 pages, 6 figure
Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities
Traffic prediction plays a crucial role in alleviating traffic congestion
which represents a critical problem globally, resulting in negative
consequences such as lost hours of additional travel time and increased fuel
consumption. Integrating emerging technologies into transportation systems
provides opportunities for improving traffic prediction significantly and
brings about new research problems. In order to lay the foundation for
understanding the open research challenges in traffic prediction, this survey
aims to provide a comprehensive overview of traffic prediction methodologies.
Specifically, we focus on the recent advances and emerging research
opportunities in Artificial Intelligence (AI)-based traffic prediction methods,
due to their recent success and potential in traffic prediction, with an
emphasis on multivariate traffic time series modeling. We first provide a list
and explanation of the various data types and resources used in the literature.
Next, the essential data preprocessing methods within the traffic prediction
context are categorized, and the prediction methods and applications are
subsequently summarized. Lastly, we present primary research challenges in
traffic prediction and discuss some directions for future research.Comment: Published in Transportation Research Part C: Emerging Technologies
(TR_C), Volume 145, 202
Graph Neural Network for Air Quality Prediction: A Case Study in Madrid
Air quality monitoring, modelling and forecasting are considered pressing and challenging
topics for citizens and decision-makers, including the government. The tools used to achieve the above goals
vary depending on the opportunities provided by technological development. Much attention is currently
being paid to machine learning and deep learning methods, which, compared to domain knowledge methods,
often perform better in terms of capturing, computing and processing multidimensional information and
complex dependencies. The technique introduced in this work is an Attention Temporal Graph Convolutional
Network based on a combination of Attention, a Gated Recurrent Unit and a Graph Convolutional Network.
In the framework of the current study, it is initially suggested to use the presented approach in the domain
of air quality prediction. The proposed method was tested using air quality, meteorological and traffic
data obtained from the city of Madrid for the periods January-June 2019 and January-June 2022. The
evaluation metrics, including Root Mean Square Error, Mean Absolute Error and Pearson Correlation
Coefficient, confirmed the proposed model’s advantages compared with the reference models (Temporal
Graph Convolutional Network, Long Short-Term Memory and Gated Recurrent Unit)
Spatiotemporal Graph Neural Networks with Uncertainty Quantification for Traffic Incident Risk Prediction
Predicting traffic incident risks at granular spatiotemporal levels is
challenging. The datasets predominantly feature zero values, indicating no
incidents, with sporadic high-risk values for severe incidents. Notably, a
majority of current models, especially deep learning methods, focus solely on
estimating risk values, overlooking the uncertainties arising from the
inherently unpredictable nature of incidents. To tackle this challenge, we
introduce the Spatiotemporal Zero-Inflated Tweedie Graph Neural Networks
(STZITD-GNNs). Our model merges the reliability of traditional statistical
models with the flexibility of graph neural networks, aiming to precisely
quantify uncertainties associated with road-level traffic incident risks. This
model strategically employs a compound model from the Tweedie family, as a
Poisson distribution to model risk frequency and a Gamma distribution to
account for incident severity. Furthermore, a zero-inflated component helps to
identify the non-incident risk scenarios. As a result, the STZITD-GNNs
effectively capture the dataset's skewed distribution, placing emphasis on
infrequent but impactful severe incidents. Empirical tests using real-world
traffic data from London, UK, demonstrate that our model excels beyond current
benchmarks. The forte of STZITD-GNN resides not only in its accuracy but also
in its adeptness at curtailing uncertainties, delivering robust predictions
over short (7 days) and extended (14 days) timeframes
A Comprehensive Survey on Deep Graph Representation Learning
Graph representation learning aims to effectively encode high-dimensional
sparse graph-structured data into low-dimensional dense vectors, which is a
fundamental task that has been widely studied in a range of fields, including
machine learning and data mining. Classic graph embedding methods follow the
basic idea that the embedding vectors of interconnected nodes in the graph can
still maintain a relatively close distance, thereby preserving the structural
information between the nodes in the graph. However, this is sub-optimal due
to: (i) traditional methods have limited model capacity which limits the
learning performance; (ii) existing techniques typically rely on unsupervised
learning strategies and fail to couple with the latest learning paradigms;
(iii) representation learning and downstream tasks are dependent on each other
which should be jointly enhanced. With the remarkable success of deep learning,
deep graph representation learning has shown great potential and advantages
over shallow (traditional) methods, there exist a large number of deep graph
representation learning techniques have been proposed in the past decade,
especially graph neural networks. In this survey, we conduct a comprehensive
survey on current deep graph representation learning algorithms by proposing a
new taxonomy of existing state-of-the-art literature. Specifically, we
systematically summarize the essential components of graph representation
learning and categorize existing approaches by the ways of graph neural network
architectures and the most recent advanced learning paradigms. Moreover, this
survey also provides the practical and promising applications of deep graph
representation learning. Last but not least, we state new perspectives and
suggest challenging directions which deserve further investigations in the
future
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