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Predicting citywide crowd flows using deep spatio-temporal residual networks

Research article (Artificial Intelligence, 2018) · cited 515× · AI/ML
academia openalex:W2964319113
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Predicting citywide crowd flows using deep spatio-temporal residual networks

Summary

Predicting citywide crowd flows using deep spatio-temporal residual networks is a scholarly article[1].

Key Facts

  • Predicting citywide crowd flows using deep spatio-temporal residual networks's instance of is recorded as scholarly article[2].

References

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). Predicting citywide crowd flows using deep spatio-temporal residual networks. Retrieved May 24, 2026, from https://4ort.xyz/entity/predicting-citywide-crowd-flows-using-deep-spatio-temporal-residual-networks
MLA “Predicting citywide crowd flows using deep spatio-temporal residual networks.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/predicting-citywide-crowd-flows-using-deep-spatio-temporal-residual-networks.
BibTeX @misc{4ortxyz_predicting-citywide-crowd-flows-using-deep-spatio-temporal-residual-networks_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Predicting citywide crowd flows using deep spatio-temporal residual networks}}, year = {2026}, url = {https://4ort.xyz/entity/predicting-citywide-crowd-flows-using-deep-spatio-temporal-residual-networks}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Predicting citywide crowd flows using deep spatio-temporal residual networks — https://4ort.xyz/entity/predicting-citywide-crowd-flows-using-deep-spatio-temporal-residual-networks (retrieved 2026-05-24)

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