71,312 research outputs found
Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction
Accurate traffic prediction is a challenging task in intelligent
transportation systems because of the complex spatio-temporal dependencies in
transportation networks. Many existing works utilize sophisticated temporal
modeling approaches to incorporate with graph convolution networks (GCNs) for
capturing short-term and long-term spatio-temporal dependencies. However, these
separated modules with complicated designs could restrict effectiveness and
efficiency of spatio-temporal representation learning. Furthermore, most
previous works adopt the fixed graph construction methods to characterize the
global spatio-temporal relations, which limits the learning capability of the
model for different time periods and even different data scenarios. To overcome
these limitations, we propose an automated dilated spatio-temporal synchronous
graph network, named Auto-DSTSGN for traffic prediction. Specifically, we
design an automated dilated spatio-temporal synchronous graph (Auto-DSTSG)
module to capture the short-term and long-term spatio-temporal correlations by
stacking deeper layers with dilation factors in an increasing order. Further,
we propose a graph structure search approach to automatically construct the
spatio-temporal synchronous graph that can adapt to different data scenarios.
Extensive experiments on four real-world datasets demonstrate that our model
can achieve about 10% improvements compared with the state-of-art methods.
Source codes are available at https://github.com/jinguangyin/Auto-DSTSGN
Multi-Spatio-temporal Fusion Graph Recurrent Network for Traffic forecasting
Traffic forecasting is essential for the traffic construction of smart cities
in the new era. However, traffic data's complex spatial and temporal
dependencies make traffic forecasting extremely challenging. Most existing
traffic forecasting methods rely on the predefined adjacency matrix to model
the Spatio-temporal dependencies. Nevertheless, the road traffic state is
highly real-time, so the adjacency matrix should change dynamically with time.
This article presents a new Multi-Spatio-temporal Fusion Graph Recurrent
Network (MSTFGRN) to address the issues above. The network proposes a
data-driven weighted adjacency matrix generation method to compensate for
real-time spatial dependencies not reflected by the predefined adjacency
matrix. It also efficiently learns hidden Spatio-temporal dependencies by
performing a new two-way Spatio-temporal fusion operation on parallel
Spatio-temporal relations at different moments. Finally, global Spatio-temporal
dependencies are captured simultaneously by integrating a global attention
mechanism into the Spatio-temporal fusion module. Extensive trials on four
large-scale, real-world traffic datasets demonstrate that our method achieves
state-of-the-art performance compared to alternative baselines
The right place at the right time: assisting spatio-temporal planning in construction
21st - 24th October 2003 This paper describes research carried out for requirements capture in the development of a computer-based decision support tool (VIRCON) for space-time scheduling and visualisation of construction tasks. The focus was on pre-tender work and involved interviews with construction planners. Both space-time scheduling and visualisation of tasks are largely informal/intuitive processes for planners. They form an important part of the planner\'s risk identification function. Planners tend to opt for a robust spatio-temporal schedule rather than an optimal one. They require decision support tools that are quick and easy to use rather than highly sophisticated. The research highlights the extent to which construction planning is a communicative and co-operative activity in addition to a complex problem-solving one. Questions arise about the cost to the client of non-involvement by the construction planner at the design stage, the costs of short pre-tender periods, inadequate design data and sub-optimal construction periods specified in tender documents
Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting
With the acceleration of urbanization, traffic forecasting has become an
essential role in smart city construction. In the context of spatio-temporal
prediction, the key lies in how to model the dependencies of sensors. However,
existing works basically only consider the micro relationships between sensors,
where the sensors are treated equally, and their macroscopic dependencies are
neglected. In this paper, we argue to rethink the sensor's dependency modeling
from two hierarchies: regional and global perspectives. Particularly, we merge
original sensors with high intra-region correlation as a region node to
preserve the inter-region dependency. Then, we generate representative and
common spatio-temporal patterns as global nodes to reflect a global dependency
between sensors and provide auxiliary information for spatio-temporal
dependency learning. In pursuit of the generality and reality of node
representations, we incorporate a Meta GCN to calibrate the regional and global
nodes in the physical data space. Furthermore, we devise the cross-hierarchy
graph convolution to propagate information from different hierarchies. In a
nutshell, we propose a Hierarchical Information Enhanced Spatio-Temporal
prediction method, HIEST, to create and utilize the regional dependency and
common spatio-temporal patterns. Extensive experiments have verified the
leading performance of our HIEST against state-of-the-art baselines. We
publicize the code to ease reproducibility.Comment: 9 pages, accepted by CIKM'2
Generalised additive multiscale wavelet models constructed using particle swarm optimisation and mutual information for spatio-temporal evolutionary system representation
A new class of generalised additive multiscale wavelet models (GAMWMs) is introduced for high dimensional spatio-temporal evolutionary (STE) system identification. A novel two-stage hybrid learning scheme is developed for constructing such an additive wavelet model. In the first stage, a new orthogonal projection pursuit (OPP) method, implemented using a particle swarm optimisation(PSO) algorithm, is proposed for successively augmenting an initial coarse wavelet model, where relevant parameters of the associated wavelets are optimised using a particle swarm optimiser. The resultant network model, obtained in the first stage, may however be a redundant model. In the second stage, a forward orthogonal regression (FOR) algorithm, implemented using a mutual information method, is then applied to refine and improve the initially constructed wavelet model. The proposed two-stage hybrid method can generally produce a parsimonious wavelet model, where a ranked list of wavelet functions, according to the capability of each wavelet to represent the total variance in the desired system output signal is produced. The proposed new modelling framework is applied to real observed images, relative to a chemical reaction exhibiting a spatio-temporal evolutionary behaviour, and the associated identification results show that the new modelling framework is applicable and effective for handling high dimensional identification problems of spatio-temporal evolution sytems
MPSTAN: Metapopulation-based Spatio-Temporal Attention Network for Epidemic Forecasting
Accurate epidemic forecasting plays a vital role for governments in
developing effective prevention measures for suppressing epidemics. Most of the
present spatio-temporal models cannot provide a general framework for stable,
and accurate forecasting of epidemics with diverse evolution trends.
Incorporating epidemiological domain knowledge ranging from single-patch to
multi-patch into neural networks is expected to improve forecasting accuracy.
However, relying solely on single-patch knowledge neglects inter-patch
interactions, while constructing multi-patch knowledge is challenging without
population mobility data. To address the aforementioned problems, we propose a
novel hybrid model called Metapopulation-based Spatio-Temporal Attention
Network (MPSTAN). This model aims to improve the accuracy of epidemic
forecasting by incorporating multi-patch epidemiological knowledge into a
spatio-temporal model and adaptively defining inter-patch interactions.
Moreover, we incorporate inter-patch epidemiological knowledge into both the
model construction and loss function to help the model learn epidemic
transmission dynamics. Extensive experiments conducted on two representative
datasets with different epidemiological evolution trends demonstrate that our
proposed model outperforms the baselines and provides more accurate and stable
short- and long-term forecasting. We confirm the effectiveness of domain
knowledge in the learning model and investigate the impact of different ways of
integrating domain knowledge on forecasting. We observe that using domain
knowledge in both model construction and loss functions leads to more efficient
forecasting, and selecting appropriate domain knowledge can improve accuracy
further
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