NodeTrans: A Graph Transfer Learning Approach for Traffic Prediction

• URL: http://arxiv.org/abs/2207.01301v1
• Date: Mon, 4 Jul 2022 10:06:20 GMT
• Title: NodeTrans: A Graph Transfer Learning Approach for Traffic Prediction
• Authors: Xueyan Yin, Feifan Li, Yanming Shen, Heng Qi, and Baocai Yin
• Abstract summary: We propose a novel transfer learning approach to solve the traffic prediction with few data. First, a spatial-temporal graph neural network is proposed, which can capture the node-specific spatial-temporal traffic patterns of different road networks.
• Score: 33.299309349152146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, deep learning methods have made great progress in traffic prediction, but their performance depends on a large amount of historical data. In reality, we may face the data scarcity issue. In this case, deep learning models fail to obtain satisfactory performance. Transfer learning is a promising approach to solve the data scarcity issue. However, existing transfer learning approaches in traffic prediction are mainly based on regular grid data, which is not suitable for the inherent graph data in the traffic network. Moreover, existing graph-based models can only capture shared traffic patterns in the road network, and how to learn node-specific patterns is also a challenge. In this paper, we propose a novel transfer learning approach to solve the traffic prediction with few data, which can transfer the knowledge learned from a data-rich source domain to a data-scarce target domain. First, a spatial-temporal graph neural network is proposed, which can capture the node-specific spatial-temporal traffic patterns of different road networks. Then, to improve the robustness of transfer, we design a pattern-based transfer strategy, where we leverage a clustering-based mechanism to distill common spatial-temporal patterns in the source domain, and use these knowledge to further improve the prediction performance of the target domain. Experiments on real-world datasets verify the effectiveness of our approach.

Related papers

• Deep Learning-driven Mobile Traffic Measurement Collection and Analysis [0.43512163406552007]
  In this thesis, we harness the powerful hierarchical feature learning abilities of Deep Learning (DL) techniques in both spatial and temporal domains. We develop solutions for precise city-scale mobile traffic analysis and forecasting.
  arXiv  Detail & Related papers  (2024-10-14T06:53:45Z)
• Gradient Transformation: Towards Efficient and Model-Agnostic Unlearning for Dynamic Graph Neural Networks [66.70786325911124]
  Graph unlearning has emerged as an essential tool for safeguarding user privacy and mitigating the negative impacts of undesirable data. With the increasing prevalence of DGNNs, it becomes imperative to investigate the implementation of dynamic graph unlearning. We propose an effective, efficient, model-agnostic, and post-processing method to implement DGNN unlearning.
  arXiv  Detail & Related papers  (2024-05-23T10:26:18Z)
• BjTT: A Large-scale Multimodal Dataset for Traffic Prediction [49.93028461584377]
  Traditional traffic prediction methods rely on historical traffic data to predict traffic trends. In this work, we explore how generative models combined with text describing the traffic system can be applied for traffic generation. We propose ChatTraffic, the first diffusion model for text-to-traffic generation.
  arXiv  Detail & Related papers  (2024-03-08T04:19:56Z)
• Learning State-Augmented Policies for Information Routing in Communication Networks [92.59624401684083]
  We develop a novel State Augmentation (SA) strategy to maximize the aggregate information at source nodes using graph neural network (GNN) architectures. We leverage an unsupervised learning procedure to convert the output of the GNN architecture to optimal information routing strategies. In the experiments, we perform the evaluation on real-time network topologies to validate our algorithms.
  arXiv  Detail & Related papers  (2023-09-30T04:34:25Z)
• Dynamic Causal Graph Convolutional Network for Traffic Prediction [19.759695727682935]
  We propose an approach for predicting traffic that embeds time-varying dynamic network to capture finetemporal patterns of traffic data. We then use graph convolutional networks to generate traffic forecasts. Our experimental results on a real traffic dataset demonstrate the superior prediction performance of the proposed method.
  arXiv  Detail & Related papers  (2023-06-12T10:46:31Z)
• Semantic-Fused Multi-Granularity Cross-City Traffic Prediction [17.020546413647708]
  We propose a Semantic-Fused Multi-Granularity Transfer Learning model to achieve knowledge transfer across cities with fused semantics at different granularities. In detail, we design a semantic fusion module to fuse various semantics while conserving static spatial dependencies. We conduct extensive experiments on six real-world datasets to verify the effectiveness of our STL model.
  arXiv  Detail & Related papers  (2023-02-23T04:26:34Z)
• 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)
• Road Network Guided Fine-Grained Urban Traffic Flow Inference [108.64631590347352]
  Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem. We propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that exploits the prior knowledge of road networks. Our method can generate high-quality fine-grained traffic flow maps.
  arXiv  Detail & Related papers  (2021-09-29T07:51:49Z)
• TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning [10.205873494981633]
  We propose a Streaming Traffic Flow Forecasting Framework, TrafficStream, based on Graph Neural Networks (GNNs) and Continual Learning (CL) A JS-divergence-based algorithm is proposed to mine new traffic patterns. We construct a streaming traffic dataset to verify the efficiency and effectiveness of our model.
  arXiv  Detail & Related papers  (2021-06-11T09:42:37Z)
• Learning dynamic and hierarchical traffic spatiotemporal features with Transformer [4.506591024152763]
  This paper proposes a novel model, Traffic Transformer, for spatial-temporal graph modeling and long-term traffic forecasting. Transformer is the most popular framework in Natural Language Processing (NLP) analyzing the attention weight matrixes can find the influential part of road networks, allowing us to learn the traffic networks better.
  arXiv  Detail & Related papers  (2021-04-12T02:29:58Z)
• 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)

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.