{
  "id": "2307.02507",
  "version": "v1",
  "published": "2023-07-05T03:47:28.000Z",
  "updated": "2023-07-05T03:47:28.000Z",
  "title": "STS-CCL: Spatial-Temporal Synchronous Contextual Contrastive Learning for Urban Traffic Forecasting",
  "authors": [
    "Lincan Li",
    "Kaixiang Yang",
    "Fengji Luo",
    "Jichao Bi"
  ],
  "comment": "11pages, 6 figures",
  "categories": [
    "cs.LG",
    "cs.AI"
  ],
  "abstract": "Efficiently capturing the complex spatiotemporal representations from large-scale unlabeled traffic data remains to be a challenging task. In considering of the dilemma, this work employs the advanced contrastive learning and proposes a novel Spatial-Temporal Synchronous Contextual Contrastive Learning (STS-CCL) model. First, we elaborate the basic and strong augmentation methods for spatiotemporal graph data, which not only perturb the data in terms of graph structure and temporal characteristics, but also employ a learning-based dynamic graph view generator for adaptive augmentation. Second, we introduce a Spatial-Temporal Synchronous Contrastive Module (STS-CM) to simultaneously capture the decent spatial-temporal dependencies and realize graph-level contrasting. To further discriminate node individuals in negative filtering, a Semantic Contextual Contrastive method is designed based on semantic features and spatial heterogeneity, achieving node-level contrastive learning along with negative filtering. Finally, we present a hard mutual-view contrastive training scheme and extend the classic contrastive loss to an integrated objective function, yielding better performance. Extensive experiments and evaluations demonstrate that building a predictor upon STS-CCL contrastive learning model gains superior performance than existing traffic forecasting benchmarks. The proposed STS-CCL is highly suitable for large datasets with only a few labeled data and other spatiotemporal tasks with data scarcity issue.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-07-05T03:47:28.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "spatial-temporal synchronous contextual contrastive learning",
      "urban traffic forecasting",
      "model gains superior performance",
      "learning model gains",
      "dynamic graph view generator"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 11,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}