Evidence map›Paper›PMID 40550793›Full record

ArticleScientific data2025

Human Mobility Datasets in the Complex Metro System of Shanghai.

Peiyan Sun, Jinming Yang, Zongyuan Huang, Shaoyu Huang, Shengyuan Xu, Weipeng Wang, Wenxuan Guo, Yuting Feng, Xi Zhai, Tao Yang and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Peiyan Sun *MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, China.ORCID http://orcid.org/0009-0003-0016-5366
Jinming Yang *MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, China.
Zongyuan HuangMoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, China.
Shaoyu HuangMoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, China.
Shengyuan XuMoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, China.
Weipeng WangMoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, China.
Wenxuan GuoMoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, China.ORCID http://orcid.org/0000-0001-6336-3819
Yuting FengMoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, China.ORCID http://orcid.org/0009-0006-1002-0111
Xi ZhaiShanghai Transportation Information Center, Shanghai Urban-Rural Construction and Transportation Development Research Institute, Shanghai, 200032, China.
Tao YangShanghai Transportation Information Center, Shanghai Urban-Rural Construction and Transportation Development Research Institute, Shanghai, 200032, China.
Xiaokang YangMoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, China.
Yaohui JinMoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, China. jinyh@sjtu.edu.cn.
Yanyan XuMoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, China. yanyanxu@sjtu.edu.cn.ORCID http://orcid.org/0000-0001-5429-3177

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The growing role of metro systems in urban mobility calls for high-quality metro transit datasets. Derived from over 700 million smart card records, an open-sourced, city-scale metro flow dataset was constructed, covering the period of May-August 2017 and 302 metro stations in Shanghai, China. The in-out flow counts of each station and OD flow between stations were offered at a 10-minute temporal resolution. By leveraging the mobility patterns of each passenger, metro flows were categorized into commuting flows, home-based-other flows, and none-home-based flows, providing a more comprehensive perspective towards urban mobility dynamics. Supplemental metadata, including station attributes, network topology, and meteorological records further support potential applications. This city-scale metro flow dataset could be utilized in advancing research in transportation modeling, spatio-temporal data mining, and urban mobility analysis.

Identifiers

PMID40550793
PMCPMC12185725

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.