A Graph and Attentive Multi-Path Convolutional Network for Traffic Prediction

Abstract

Traffic prediction is an important and yet highly challenging problem due to the complexity and constantly changing nature of traffic systems. To address the challenges, we propose a graph and attentive multi-path convolutional network (GAMCN) model to predict traffic conditions such as traffic speed across a given road network into the future. Our model focuses on the spatial and temporal factors that impact traffic conditions. To model the spatial factors, we propose a variant of the graph convolutional network (GCN) named LPGCN to embed road network graph vertices into a latent space, where vertices with correlated traffic conditions are close to each other. To model the temporal factors, we use a multi-path convolutional neural network (CNN) to learn the joint impact of different combinations of past traffic conditions on the future traffic conditions. Such a joint impact is further modulated by an attention generated from an embedding of the prediction time, which encodes the periodic patterns of traffic conditions. We evaluate our model on real-world road networks and traffic data. The experimental results show that our model outperforms state-of-art traffic prediction models by up to 18.9% in terms of prediction errors and 23.4% in terms of prediction efficiency.

Traffic Flow Prediction Research Based on an Interactive Dynamic Spatial–Temporal Graph Convolutional Probabilistic Sparse Attention Mechanism (IDG-PSAtt)

“…(9) STGCN [2]: This network employs graph convolutional layers and convolutional sequence layers, there are 2 spatiotemporal cells and the hidden dimension is set to 64. (10) ASTGCN [8]: This model employs an attention mechanism to capture spatiotemporal dynamic correlations, there are 2 spatiotemporal cells and the hidden dimension is set to 64.…”

“…Several of the above studies employed serial or parallel structures to extract dynamic spatial-temporal features; however, these structures can weaken the captured spatial-temporal correlations and even amplify some irrelevant information, resulting in poor traffic flow prediction results. Therefore, the ASTGCN [8] employs a spatial attention mechanism and a temporal attention mechanism to further enhance the prediction performance of the model. The ASTGNN [9] adapts dynamic graph convolutions to extract spatial features and learns the temporal dependencies of traffic flows through an attention mechanism.…”

TrafFormer: A Transformer Model for Predicting Long-term Traffic

“…• Graph and Attentive Multi-path Convolutional Network (GAMCN) [42]. The authors proposed a novel LPGCN module that captures the spatial correlation between traffic conditions in close and far-away locations alike, and a temporal correlation modeling with multi-path temporal convolution.…”

Dynamic Graph Convolutional Network-Based Prediction of the Urban Grid-Level Taxi Demand–Supply Imbalance Using GPS Trajectories

“…A significant number of deep-learning prediction models have been developed based on the GCN (Graph Convolutional Network) model [26], capable of capturing the spatiotemporal characteristics of traffic flow. For instance, several models have arisen within this context, including T-GCN [27], GAMCN [28], DDP-GCN [29], KST-GCN [30], and TPP-GCN [31]. These models generally take into account only the fixed correlations between vertices while executing spatial convolution operations, such as adjacency relationships [27], distance [30], and attribute correlations between vertices [31].…”