A General Traffic Flow Prediction Approach Based on Spatial-Temporal Graph Attention

Tang, Cong; Sun, Jingru; Sun, Yichuang; Peng, Mu; Gan, Nianfei

doi:10.1109/access.2020.3018452

Cited by 35 publications

(16 citation statements)

References 32 publications

(29 reference statements)

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“…The attention-based learning methods use the attention mechanism to learn the pairwise inter-variable dependencies. For traffic flow forecasting, Tang et al [31] used a graph attention module to learn graph structure. Zheng et al [33] used spatial attention mechanism to learn the correlation of traffic flow at different nodes.…”

Section: B Graph Learning For Mtsmentioning

confidence: 99%

Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting

Chen¹,

Chen²,

Shang³

et al. 2022

Preprint

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Multivariate time series (MTS) forecasting plays an important role in the automation and optimization of intelligent applications. It is a challenging task, as we need to consider both complex intra-variable dependencies and inter-variable dependencies. Existing works only learn temporal patterns with the help of single inter-variable dependencies. However, there are multi-scale temporal patterns in many real-world MTS. Single inter-variable dependencies make the model prefer to learn one type of prominent and shared temporal patterns. In this paper, we propose a multi-scale adaptive graph neural network (MAGNN) to address the above issue. MAGNN exploits a multiscale pyramid network to preserve the underlying temporal dependencies at different time scales. Since the inter-variable dependencies may be different under distinct time scales, an adaptive graph learning module is designed to infer the scalespecific inter-variable dependencies without pre-defined priors. Given the multi-scale feature representations and scale-specific inter-variable dependencies, a multi-scale temporal graph neural network is introduced to jointly model intra-variable dependencies and inter-variable dependencies. After that, we develop a scale-wise fusion module to effectively promote the collaboration across different time scales, and automatically capture the importance of contributed temporal patterns. Experiments on four real-world datasets demonstrate that MAGNN outperforms the state-of-the-art methods across various settings.

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Section: B Graph Learning For Mtsmentioning

confidence: 99%

Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting

Chen¹,

Chen²,

Shang³

et al. 2022

Preprint

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show abstract

“…As is shown by ( Figure 1 ), the sensors deployed on the traffic network which collected traffic data at fixed time intervals form a non-Euclidean topological graph naturally ( 34 ). We use nodes to represent the locations of traffic sensors, and the road segments connecting traffic sensors are treated as edges in graph.…”

Section: Problem Descriptionmentioning

confidence: 99%

A Deep Learning Framework About Traffic Flow Forecasting for Urban Traffic Emission Monitoring System

Yao

Feng

et al. 2022

Front. Public Health

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As urban traffic pollution continues to increase, there is an urgent need to build traffic emission monitoring and forecasting system for the urban traffic construction. The traffic emission monitoring and forecasting system's core is the prediction of traffic emission's evolution. And the traffic flow prediction on the urban road network contributes greatly to the prediction of traffic emission's evolution. Due to the complex non-Euclidean topological structure of traffic networks and dynamic heterogeneous spatial-temporal correlations of traffic conditions, it is difficult to obtain satisfactory prediction results with less computation cost. To figure these issues out, a novel deep learning traffic flow forecasting framework is proposed in this paper, termed as Ensemble Attention based Graph Time Convolutional Networks (EAGTCN). More specifically, each component of our model contains two major blocks: (1) the global spatial patterns are captured by the spatial blocks which are fused by the Graph Convolution Network (GCN) and spatial ensemble attention layer; (2) the temporal patterns are captured by the temporal blocks which are composed by the Time Convolution Net (TCN) and temporal ensemble attention layers. Experiments on two real-world datasets demonstrate that our model obtains more accurate prediction results than the state-of-the-art baselines at less computation expense especially in the long-term prediction situation.

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“…Flows, Crowds and Density. Some existing works study the estimation of flows [33,34], crowds [6,37], or dense regions [15,16] outdoors. However, they all fall short in indoor spaces mainly for two reasons.…”

Section: Related Workmentioning

confidence: 99%

Towards crowd-aware indoor path planning

Liu

et al. 2021

Proc. VLDB Endow.

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Indoor venues accommodate many people who collectively form crowds. Such crowds in turn influence people's routing choices, e.g., people may prefer to avoid crowded rooms when walking from A to B. This paper studies two types of crowd-aware indoor path planning queries. The Indoor Crowd-Aware Fastest Path Query (FPQ) finds a path with the shortest travel time in the presence of crowds, whereas the Indoor Least Crowded Path Query (LCPQ) finds a path encountering the least objects en route. To process the queries, we design a unified framework with three major components. First, an indoor crowd model organizes indoor topology and captures object flows between rooms. Second, a time-evolving population estimator derives room populations for a future timestamp to support crowd-aware routing cost computations in query processing. Third, two exact and two approximate query processing algorithms process each type of query. All algorithms are based on graph traversal over the indoor crowd model and use the same search framework with different strategies of updating the populations during the search process. All proposals are evaluated experimentally on synthetic and real data. The experimental results demonstrate the efficiency and scalability of our framework and query processing algorithms.

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A General Traffic Flow Prediction Approach Based on Spatial-Temporal Graph Attention

Cited by 35 publications

References 32 publications

Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting

Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting

A Deep Learning Framework About Traffic Flow Forecasting for Urban Traffic Emission Monitoring System

Towards crowd-aware indoor path planning

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