ST-CSL/CITATION.cff at main · CodeZx6/ST-CSL · GitHub

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cff-version: 1.2.0
message: If you use this code, please cite the paper in preferred-citation.
type: software
title: 'ST-FCL: code for "Spatio-temporal fusion and contrastive learning for urban flow prediction"'
authors:
- family-names: Zhang
  given-names: Xu
- family-names: Gong
  given-names: Yongshun
- family-names: Zhang
  given-names: Chengqi
- family-names: Wu
  given-names: Xiaoming
- family-names: Guo
  given-names: Ying
- family-names: Lu
  given-names: Wenpeng
- family-names: Zhao
  given-names: Long
- family-names: Dong
  given-names: Xiangjun
repository-code: https://github.com/CodeZx6/ST-CSL
url: https://codezx6.github.io/papers/st-csl.html
abstract: ST-FCL predicts grid-level urban inflow and outflow by fusing temporal and spatial views, learned through contrastive
  pretraining, with an external-factor view. On the full TaxiBJ dataset it reaches RMSE 14.71, against 15.41 for the best
  baseline, ATFM.
keywords:
- urban flow prediction
- crowd flow prediction
- contrastive learning
- spatio-temporal fusion
- multi-view representation fusion
- multi-view influencing factors
- spatio-temporal systems
- traffic prediction
preferred-citation:
  type: article
  title: Spatio-temporal fusion and contrastive learning for urban flow prediction
  authors:
  - family-names: Zhang
    given-names: Xu
  - family-names: Gong
    given-names: Yongshun
  - family-names: Zhang
    given-names: Chengqi
  - family-names: Wu
    given-names: Xiaoming
  - family-names: Guo
    given-names: Ying
  - family-names: Lu
    given-names: Wenpeng
  - family-names: Zhao
    given-names: Long
  - family-names: Dong
    given-names: Xiangjun
  year: 2023
  doi: 10.1016/j.knosys.2023.111104
  url: https://doi.org/10.1016/j.knosys.2023.111104
  journal: Knowledge-Based Systems
  volume: '282'
  start: '111104'
license: MIT