Urban Traffic Speed Dataset of Guangzhou, China: papers linking to it

Zenodo · Dataset · 2018 · doi.org/10.5281/zenodo.1205229

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to Urban Traffic Speed Dataset of Guangzhou, China (https://doi.org/10.5281/zenodo.1205229), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

3 papers in the tracked journals link to the Zenodo dataset Urban Traffic Speed Dataset of Guangzhou, China in their Crossref records, the first in 2021; most of them (1) in 2024.
By category: AI (3); the journals with the most are Applied Intelligence (1), Information Fusion (1) and Machine Learning (1).

By year#

20212024

Papers linking to Urban Traffic Speed Dataset of Guangzhou, China by publication year
Year	Papers
2021	1
2023	1
2024	1

Journals#

Journals whose papers link to Urban Traffic Speed Dataset of Guangzhou, China most
Journal	Papers
Applied Intelligence	1
Information Fusion	1
Machine Learning	1

Papers linking to Urban Traffic Speed Dataset of Guangzhou, China, most cited first
Published	Title	Journal	Authors	Citations	Link
2023-11-04	ADCT-Net: Adaptive traffic forecasting neural network via dual-graphic cross-fused transformer	Information Fusion	Jianlei Kong, Xiaomeng Fan, Min Zuo et al.	96	cites it
2021-04-28	Misalignment problem in matrix decomposition with missing values	Machine Learning	Sofia Fernandes, Mário Antunes, Diogo Gomes et al.	6	cites it
2024-11-23	ST-NAMN: a spatial-temporal nonlinear auto-regressive multichannel neural network for traffic prediction	Applied Intelligence	Jiankai Zuo, Yaying Zhang	2	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). Urban Traffic Speed Dataset of Guangzhou, China: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/zenodo-1205229

@misc{scholar4ai-code-zenodo-1205229,
  author = {{Scholar4AI}},
  title = {Urban Traffic Speed Dataset of Guangzhou, China: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/zenodo-1205229}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

Free to reuse for non-commercial purposes with attribution (CC BY-NC 4.0).