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Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting
Weiqi Chen, Ling Chen, Yu Xie, Wei Cao, Yusong Gao, Xiaojie Feng
TL;DR
Traffic forecasting must capture complicated spatial-temporal dependencies, yet existing graph methods mainly use fixed weighted graphs that overlook interacting edges and varied neighborhood ranges. MRA-BGCN models node and edge correlations with bicomponent graph convolution and uses multi-range attention to learn the importance of different ranges. On METR-LA and PEMS-BAY, it achieves state-of-the-art performance.
Problem
Existing traffic forecasting approaches using fixed weighted graphs overlook complex edge interactions and multiple neighborhood ranges.
Method
MRA-BGCN builds node-wise and edge-wise graphs, applies bicomponent graph convolution to both, and uses multi-range attention to learn range importance.
Results
MRA-BGCN achieves state-of-the-art performance on the METR-LA and PEMS-BAY traffic datasets.
Takeaways & Limitations
The model provides a traffic-forecasting approach that jointly represents node and edge correlations while integrating information across neighborhood ranges.
Abstract
from arXiv · showhide
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.
Introduction
MRA-BGCN addresses overlooked edge interactions and multiple neighborhood ranges in traffic forecasting by modeling both explicitly and learning their relative importance. Experiments on METR-LA and PEMS-BAY report state-of-the-art results.
- Motivation: Fixed weighted graphs neglect complex interactions among pair-wise traffic correlations, whose relationships vary with traffic conditions.Existing approaches primarily model node interactions using road-distance graphs while representing edge correlations as fixed adjacency scalars.
- Motivation: Different neighborhood ranges capture distinct traffic properties, from local dependencies to broader regional patterns, and their importance varies by situation.The paper gives traffic accidents as an example where nearest neighbors deserve greater attention than all k-hop neighbors equally.
- Approach: MRA-BGCN explicitly models correlations of both nodes and edges through bicomponent graph convolution.Its node-wise graph uses road network distance, while its edge-wise graph represents stream connectivity and competitive relationships.
- Approach: Multi-range attention aggregates information from different neighborhood ranges and learns their importance automatically.This mechanism is introduced within the bicomponent graph convolution framework.
- Results: MRA-BGCN achieves state-of-the-art results on the METR-LA and PEMS-BAY traffic datasets.The introduction describes extensive experiments on two real-world traffic datasets.
Related Works
Traffic forecasting research progressed from shallow models and regular-grid CNNs toward graph-based methods for irregular road networks. Later approaches added learned hidden dependencies, but edge interaction and multi-range information remained insufficiently modeled.
- Earlier Approaches: Early traffic forecasting methods used shallow machine learning for single or few observation nodes, limiting nonlinear and spatial-dependency modeling.Examples include linear regression, Kalman filtering, and ARIMA-based approaches.
- Deep Learning Approaches: CNN- and RNN-based approaches improved deep spatial-temporal modeling, but CNNs do not naturally represent irregular non-Euclidean road networks.CNNs are restricted to regular grid structures such as images and videos.
- Graph-Based Approaches: Graph-based methods model road-network correlations, including bidirectional diffusion and combined spatial-temporal graph convolutions.DCRNN uses diffusion convolution for inflow and outflow relationships, while ST-GCN combines graph convolution with one-dimensional convolution.
- Graph-Based Approaches: Self-adaptive adjacency matrices capture hidden spatial dependencies from node-embedding similarity but lack domain-knowledge guidance and may suffer from overfitting.The passage presents this as a limitation of data-driven hidden-dependency learning.
Preliminaries
The forecasting problem represents correlated traffic sensors and their observations as a weighted directed graph with graph signals. Graph convolutions aggregate neighboring information, while stacked layers expand the receptive range.
- Problem Definition: Traffic forecasting uses a weighted directed graph whose nodes are correlated sensors and whose adjacency matrix represents node proximities such as road distance.The observed traffic at each time is represented as a graph signal with N nodes and P features.
- Problem Definition: The task is to forecast T future graph signals from T′ historical graph signals and the graph structure.The historical and future signals are represented over node, feature, and time dimensions.
- Graph Convolutional Networks: Graph convolutional networks learn from non-Euclidean graph structures and include spectral-based and spatial-based approaches.Spatial methods aggregate representations from a node and its neighbors, whereas spectral methods operate in the graph Fourier domain.
- Graph Convolution: The paper’s graph convolution applies a learnable transformation to an input signal and normalized adjacency with self-connections, followed by nonlinear activation.A single layer aggregates one-hop neighbors, and stacking layers expands the receptive neighborhood range.
Methodology
MRA-BGCN models traffic structure through interacting node-wise and edge-wise graphs, then combines multiple neighborhood ranges with learned attention. It is integrated with BGCGRU and sequence-to-sequence forecasting to capture temporal dependencies.
- Bicomponent Graph Convolution: MRA-BGCN uses node-wise and edge-wise graph convolution layers to explicitly model interactions among both nodes and edges.The edge-wise graph represents edge interactions, while the bicomponent module combines node and edge representations.
- Bicomponent Graph Convolution: The edge-wise graph represents stream connectivity and competitive relationships between road links.Stream connectivity links upstream and downstream edges; competitive relationships connect edges associated with shared traffic-resource competition.
- Bicomponent Graph Convolution: The incidence matrix transfers information between connected nodes and edges during bicomponent graph convolution.MZ aggregates edge representations at nodes, while M^T X aggregates node representations at edges.
- Multi-Range Attention: Multi-range attention aggregates node representations from different neighborhood ranges and learns their relative importance.A shared linear transformation and learned neighborhood-range context embedding produce normalized attention coefficients.
- Bicomponent Graph Convolutional RNN: The model replaces GRU fully connected layers with MRA-BGCN and stacks BGCGRU layers within a sequence-to-sequence architecture.This design combines the proposed spatial module with recurrent processing for multiple-step-ahead traffic forecasting.
Experiments
The experiments evaluate MRA-BGCN against classical, recurrent, spatial-temporal, and adaptive-graph baselines on METR-LA and PEMS-BAY across multiple forecasting horizons. MRA-BGCN performs best across horizons, with larger gains on METR-LA and evidence supporting both edge-wise modeling and multi-range attention.
- Experimental setup: The task forecasts traffic speed one hour ahead from the preceding hour, using 12 input and output time steps.The experiments report results for 15-minute, 30-minute, and 1-hour horizons selected from the 12 forecasting horizons.
- Overall results: MRA-BGCN achieves the best performance for all forecasting horizons and outperforms traditional and fixed-graph GCN-based methods.The compared traditional methods are HA, ARIMAkal, and FC-LSTM; the fixed-graph GCN methods include DCRNN and ST-GCN.
- Overall results: MRA-BGCN yields small improvement over Graph WaveNet on PEMS-BAY but large improvement on METR-LA, with superiority increasing at longer horizons.The paper relates this pattern to more complicated dependencies in METR-LA and greater uncertainty in long-term forecasting.
- Ablation studies: Removing edge correlations produces the largest testing error, whereas MRA-BGCN achieves the lowest testing error among the edge-wise graph variants.The edge-wise graph represents stream connectivity and competitive relationship.
- Ablation studies: The multi-range attention mechanism improves the use of information from different neighborhood ranges by distinguishing their importance.The comparison includes BGCN, which uses one range, and MR-BGCN, which concatenates representations from multiple layers.
Conclusions and Future Work
MRA-BGCN models node and edge correlations with bicomponent graph convolution, encodes two edge-interaction patterns, and integrates multiple neighborhood ranges. On two traffic datasets, it achieves state-of-the-art performance, with future work extending the model to other forecasting tasks and richer dependencies.
- Conclusions and Future Work: Bicomponent graph convolution explicitly models correlations of both nodes and edges.The approach treats edges as interacting entities rather than only using node correlations.
- Conclusions and Future Work: Edge-wise graph construction encodes stream connectivity and competitive relationship.
- Conclusions and Future Work: Multi-range attention leverages information from multiple neighborhood ranges to generate integrated representations.
- Conclusions and Future Work: The model achieves state-of-the-art performance on two traffic datasets.
- Conclusions and Future Work: Future work will apply the model to other spatial-temporal forecasting tasks and model more complex dependencies using additional factors.Examples include traffic accidents and surrounding points of interest.