Graph Convolutional Network With Pattern-Spatial Interactive and Regional Awareness for Traffic Forecasting

• URL: http://arxiv.org/abs/2509.00515v1
• Date: Sat, 30 Aug 2025 14:39:02 GMT
• Title: Graph Convolutional Network With Pattern-Spatial Interactive and Regional Awareness for Traffic Forecasting
• Authors: Xinyu Ji, Chengcheng Yan, Jibiao Yuan, Fiefie Zhao,
• Abstract summary: We propose a pattern-spatial interactive fusion framework composed of pattern and spatial modules.<n>In the spatial module, we designed a graph convolutional network based on message-passing.<n>The network is designed to leverage a regional characteristics bank to reconstruct data-driven message-passing.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traffic forecasting is significant for urban traffic management, intelligent route planning, and real-time flow monitoring. Recent advances in spatial-temporal models have markedly improved the modeling of intricate spatial-temporal correlations for traffic forecasting. Unfortunately, most previous studies have encountered challenges in effectively modeling spatial-temporal correlations across various perceptual perspectives, which have neglected the interactive fusion between traffic patterns and spatial correlations. Additionally, constrained by spatial heterogeneity, most studies fail to consider distinct regional heterogeneity during message-passing. To overcome these limitations, we propose a Pattern-Spatial Interactive and Regional Awareness Graph Convolutional Network (PSIRAGCN) for traffic forecasting. Specifically, we propose a pattern-spatial interactive fusion framework composed of pattern and spatial modules. This framework aims to capture patterns and spatial correlations by adopting a perception perspective from the global to the local level and facilitating mutual utilization with positive feedback. In the spatial module, we designed a graph convolutional network based on message-passing. The network is designed to leverage a regional characteristics bank to reconstruct data-driven message-passing with regional awareness. Reconstructed message passing can reveal the regional heterogeneity between nodes in the traffic network. Extensive experiments on three real-world traffic datasets demonstrate that PSIRAGCN outperforms the State-of-the-art baseline while balancing computational costs.

Related papers

• Wireless Traffic Prediction with Large Language Model [54.07581399989292]
  TIDES is a novel framework that captures spatial-temporal correlations for wireless traffic prediction.<n> TIDES achieves efficient adaptation to domain-specific patterns without incurring excessive training overhead.<n>Our results indicate that integrating spatial awareness into LLM-based predictors is the key to unlocking scalable and intelligent network management in future 6G systems.
  arXiv  Detail & Related papers  (2025-12-19T04:47:40Z)
• Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies [55.2480439325792]
  This paper introduces the Signal-Enhanced Graph Convolutional Network Long Short Term Memory (SGCN-LSTM) model for predicting traffic speeds across road networks. Experiments on the PEMS-BAY road network traffic dataset demonstrate the SGCN-LSTM model's effectiveness.
  arXiv  Detail & Related papers  (2024-11-01T00:37:00Z)
• Fusion Matrix Prompt Enhanced Self-Attention Spatial-Temporal Interactive Traffic Forecasting Framework [2.9490249935740573]
  We propose a Fusion Matrix Prompt Enhanced Self-Attention Spatial-Temporal Interactive Traffic Forecasting Framework (FMPESTF) FMPESTF is composed of spatial and temporal modules for down-sampling traffic data. We introduce attention mechanism in time modeling, and design hierarchical spatial-temporal interactive learning to help the model adapt to various traffic scenarios.
  arXiv  Detail & Related papers  (2024-10-12T03:47:27Z)
• Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting [70.66710698485745]
  We propose an Adaptive Hierarchical SpatioTemporal Network (AHSTN) to promote traffic forecasting. AHSTN exploits the spatial hierarchy and modeling multi-scale spatial correlations. Experiments on two real-world datasets show that AHSTN achieves better performance over several strong baselines.
  arXiv  Detail & Related papers  (2023-06-15T14:50:27Z)
• PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction [78.05103666987655]
  spatial-temporal Graph Neural Network (GNN) models have emerged as one of the most promising methods to solve this problem. We propose a novel propagation delay-aware dynamic long-range transFormer, namely PDFormer, for accurate traffic flow prediction. Our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency.
  arXiv  Detail & Related papers  (2023-01-19T08:42:40Z)
• Adaptive Graph Spatial-Temporal Transformer Network for Traffic Flow Forecasting [6.867331860819595]
  Traffic forecasting can be highly challenging due to complex spatial-temporal correlations and non-linear traffic patterns. Existing works mostly model such spatial-temporal dependencies by considering spatial correlations and temporal correlations separately. We propose to directly model the cross-spatial-temporal correlations on the spatial-temporal graph using local multi-head self-attentions.
  arXiv  Detail & Related papers  (2022-07-09T19:21:00Z)
• DMGCRN: Dynamic Multi-Graph Convolution Recurrent Network for Traffic Forecasting [7.232141271583618]
  We propose a novel dynamic multi-graph convolution recurrent network (DMG) to tackle above issues. We use the distance-based graph to capture spatial information from nodes are close in distance. We also construct a novel latent graph which encoded the structure correlations among roads to capture spatial information from nodes are similar in structure.
  arXiv  Detail & Related papers  (2021-12-04T06:51:55Z)
• Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting [11.495992519252585]
  We argue that such correlations are universal and play a pivotal role in traffic flow. We propose a new spacetime interval learning framework that constructs a local-spacetime context of a traffic sensor. The proposed STNN model can be applied on any unseen traffic networks.
  arXiv  Detail & Related papers  (2021-09-11T09:04:35Z)
• Spatio-temporal Modeling for Large-scale Vehicular Networks Using Graph Convolutional Networks [110.80088437391379]
  A graph-based framework called SMART is proposed to model and keep track of the statistics of vehicle-to-temporal (V2I) communication latency across a large geographical area. We develop a graph reconstruction-based approach using a graph convolutional network integrated with a deep Q-networks algorithm. Our results show that the proposed method can significantly improve both the accuracy and efficiency for modeling and the latency performance of large vehicular networks.
  arXiv  Detail & Related papers  (2021-03-13T06:56:29Z)
• Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting [35.072979313851235]
  spatial-temporal data forecasting of traffic flow is a challenging task because of complicated spatial dependencies and dynamical trends of temporal pattern between different roads. Existing frameworks typically utilize given spatial adjacency graph and sophisticated mechanisms for modeling spatial and temporal correlations. This paper proposes Spatial-Temporal Fusion Graph Neural Networks (STFGNN) for traffic flow forecasting.
  arXiv  Detail & Related papers  (2020-12-15T14:03:17Z)
• Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting [88.5550074808201]
  We propose Geographic and Long term Temporal Graph Convolutional Recurrent Neural Network (GLT-GCRNN) for traffic forecasting. In this work, we propose a novel framework for traffic forecasting that learns the rich interactions between roads sharing similar geographic or longterm temporal patterns.
  arXiv  Detail & Related papers  (2020-04-23T03:50:46Z)
• Spatial-Temporal Transformer Networks for Traffic Flow Forecasting [74.76852538940746]
  We propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) to improve the accuracy of long-term traffic forecasting. Specifically, we present a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies. The proposed model enables fast and scalable training over a long range spatial-temporal dependencies.
  arXiv  Detail & Related papers  (2020-01-09T10:21:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.