ArticleAdvanced engineering informatics2022
A deep spatio-temporal meta-learning model for urban traffic revitalization index prediction in the COVID-19 pandemic.
Article in Advanced engineering informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- Research on road traffic condition prediction of smart city based on spatio-temporal multi-source information fusion.Scientific reports · 2026Article
- A new approach to COVID-19 data mining: A deep spatial-temporal prediction model based on tree structure for traffic revitalization index.Data & knowledge engineering · 2023Article
- Transfer learning based cascaded deep learning network and mask recognition for COVID-19.World wide web · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic is a major global public health problem that has caused hardship to people's normal production and life. Predicting the traffic revitalization index can provide references for city managers to formulate policies related to traffic and epidemic prevention. Previous methods have struggled to capture the complex and diverse dynamic spatio-temporal correlations during the COVID-19 pandemic. Therefore, we propose a deep spatio-temporal meta-learning model for the prediction of traffic revitalization index (DeepMeta-TRI) using external auxiliary information such as COVID-19 data. We conduct extensive experiments on a real-world dataset, and the results validate the predictive performance of DeepMeta-TRI and its effectiveness in addressing underfitting.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.