Hierarchical Graph Convolution Network for Traffic Forecasting

Guo, Kan; Hu, Yongli; Sun, Yeneng; Qian, Sean; Gao, Junbin; Yin, Baocai

doi:10.1609/aaai.v35i1.16088

Cited by 116 publications

(42 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yu et al [40] proposed STGCN to extract spatio-temporal features with complete convolutional structures. Guo et al [41] established a HGCN model which operates the convolution operation on both micro-and macrotrafc graphs. Zhu et al [42] employed GCN in multigraph to analyze correlations from multiple perspectives.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

A Dynamic Spatio-Temporal Deep Learning Model for Lane-Level Traffic Prediction

Yang

Chen

et al. 2023

Journal of Advanced Transportation

View full text Add to dashboard Cite

Traffic prediction aims to predict the future traffic state by mining features from history traffic information, and it is a crucial component for the intelligent transportation system. However, most existing traffic prediction methods focus on road segment prediction while ignore the fine-grainedlane-level traffic prediction. From observations, we found that different lanes on the same road segment have similar but not identical patterns of variation. Lane-level traffic prediction can provide more accurate prediction results for humans or autonomous driving systems to make appropriate and efficient decisions. In traffic prediction, the mining of spatial features is an important step and graph-based methods are effective methods. While most existing graph-based methods construct a static adjacent matrix, these methods are difficult to respond to spatio-temporal changes in time. In this paper, we propose a deep learning model for lane-level traffic prediction. Specifically, we take advantage of the graph convolutional network (GCN) with a data-driven adjacent matrix for spatial feature modeling and treat different lanes of the same road segment as different nodes. The data-driven adjacent matrix consists of the fundamental distance-based adjacent matrix and the dynamic lane correlation matrix. The temporal features are extracted with the gated recurrent unit (GRU). Then, we adaptively fuse spatial and temporal features with the gating mechanism to get the final spatio-temporal features for lane-level traffic prediction. Extensive experiments on a real-world dataset validate the effectiveness of our model.

show abstract

Section: Deep Learning Methodsmentioning

confidence: 99%

A Dynamic Spatio-Temporal Deep Learning Model for Lane-Level Traffic Prediction

Yang

Chen

et al. 2023

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…Guo et al [ 30 ] proposed a novel attention based spatial–temporal graph convolutional network (ASTGCN) model to solve the traffic flow forecasting problem, which mainly consists of the spatial–temporal attention mechanism and the spatial–temporal convolution. Guo et al [ 31 ] proposed a novel Hierarchical Graph Convolution Networks (HGCN) for traffic forecasting by operating on both the micro- and macro-traffic graphs. Wu et al [ 15 ] proposed a novel graph neural network architecture for spatial–temporal graph modeling by developing a novel adaptive dependency matrix and learning it through node embedding, which can precisely capture the hidden spatial dependency in the data.…”

Section: Related Workmentioning

confidence: 99%

MD-GCN: A Multi-Scale Temporal Dual Graph Convolution Network for Traffic Flow Prediction

Huang

Wang

Lan

et al. 2023

Sensors

View full text Add to dashboard Cite

The spatial–temporal prediction of traffic flow is very important for traffic management and planning. The most difficult challenges of traffic flow prediction are the temporal feature extraction and the spatial correlation extraction of nodes. Due to the complex spatial correlation between different roads and the dynamic trend of time patterns, traditional forecasting methods still have limitations in obtaining spatial–temporal correlation, which makes it difficult to extract more valid information. In order to improve the accuracy of the forecasting, this paper proposes a multi-scale temporal dual graph convolution network for traffic flow prediction (MD-GCN). Firstly, we propose a gated temporal convolution based on a channel attention and inception structure to extract multi-scale temporal dependence. Then, aiming at the complexity of the traffic spatial structure, we develop a dual graph convolution module including the graph sampling and aggregation submodule (GraphSAGE) and the mix-hop propagation graph convolution submodule (MGCN) to extract the local correlation and global correlation between neighbor nodes. Finally, extensive experiments are carried out on several public traffic datasets, and the experimental results show that our proposed algorithm outperforms the existing methods.

show abstract

“…The adaptive graph (Wu et al 2019;Guo et al 2021a;Ye et al 2021;Han et al 2021) is helpful in the short-term prediction (30min), but it is still static over time and fails to capture time-varying spatio-temporal dependencies, and the effect of long-term prediction (60min) is significantly reduced. The attention graph (Zheng et al 2020;Guo et al 2021b) can only change the weight of predefined graphs rather than the structure.…”

Section: Comparison With Baselinesmentioning

confidence: 99%

“…In addition, recent works overemphasize spatio-temporal correlations of bike flow. Generally, these graph-based spatio-temporal prediction models leverage GCN to model spatial correlations, and GRU (Li et al 2018;Liu et al 2020;Bai et al 2020;Ye et al 2021; or CNN (Wu et al 2019;Guo et al 2021a;Fang et al 2021;Han et al 2021) to model temporal correlations separately. Although these methods can achieve satisfactory effects, their ability to model complex nonlinear dynamic spatio-temporal causality is still obviously insufficient.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal neural structural causal models for bike flow prediction

Pan¹,

Yu²,

Liu³

et al. 2023

Preprint

View full text Add to dashboard Cite

As a representative of public transportation, the fundamental issue of managing bike-sharing systems is bike flow prediction. Recent methods overemphasize the spatio-temporal correlations in the data, ignoring the effects of contextual conditions on the transportation system and the inter-regional timevarying causality. In addition, due to the disturbance of incomplete observations in the data, random contextual conditions lead to spurious correlations between data and features, making the prediction of the model ineffective in special scenarios. To overcome this issue, we propose a Spatio-temporal Neural Structure Causal Model(STNSCM) from the perspective of causality. First, we build a causal graph to describe the traffic prediction, and further analyze the causal relationship between the input data, contextual conditions, spatiotemporal states, and prediction results. Second, we propose to apply the frontdoor criterion to eliminate confounding biases in the feature extraction process. Finally, we propose a counterfactual representation reasoning module to extrapolate the spatio-temporal state under the factual scenario to future counterfactual scenarios to improve the prediction performance. Experiments on real-world datasets demonstrate the superior performance of our model, especially its resistance to fluctuations caused by the external environment. The source code and data will be released.

show abstract

Hierarchical Graph Convolution Network for Traffic Forecasting

Cited by 116 publications

References 18 publications

A Dynamic Spatio-Temporal Deep Learning Model for Lane-Level Traffic Prediction

A Dynamic Spatio-Temporal Deep Learning Model for Lane-Level Traffic Prediction

MD-GCN: A Multi-Scale Temporal Dual Graph Convolution Network for Traffic Flow Prediction

Spatio-temporal neural structural causal models for bike flow prediction

Contact Info

Product

Resources

About