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Forecast network-wide traffic states for multiple steps ahead: A deep learning approach considering dynamic non-local spatial correlation and non-stationary temporal dependency
Research article (Transportation Research Part C Emerging Technologies, 2020) · cited 51× · AI/ML
Forecast network-wide traffic states for multiple steps ahead: A deep learning approach considering dynamic non-local spatial correlation and non-stationary temporal dependency
Summary
Forecast network-wide traffic states for multiple steps ahead: A deep learning approach considering dynamic non-local spatial correlation and non-stationary temporal dependency is a scholarly article[1].
Key Facts
Forecast network-wide traffic states for multiple steps ahead: A deep learning approach considering dynamic non-local spatial correlation and non-stationary temporal dependency's instance of is recorded as scholarly article[2].
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APA4ort.xyz Knowledge Graph. (2026). Forecast network-wide traffic states for multiple steps ahead: A deep learning approach considering dynamic non-local spatial correlation and non-stationary temporal dependency. Retrieved May 24, 2026, from https://4ort.xyz/entity/forecast-network-wide-traffic-states-for-multiple-steps-ahead-a-deep-learning-approach-considering-dynamic-non-local-spa
MLA“Forecast network-wide traffic states for multiple steps ahead: A deep learning approach considering dynamic non-local spatial correlation and non-stationary temporal dependency.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/forecast-network-wide-traffic-states-for-multiple-steps-ahead-a-deep-learning-approach-considering-dynamic-non-local-spa.
BibTeX@misc{4ortxyz_forecast-network-wide-traffic-states-for-multiple-steps-ahead-a-deep-learning-approach-considering-dynamic-non-local-spa_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Forecast network-wide traffic states for multiple steps ahead: A deep learning approach considering dynamic non-local spatial correlation and non-stationary temporal dependency}}, year = {2026}, url = {https://4ort.xyz/entity/forecast-network-wide-traffic-states-for-multiple-steps-ahead-a-deep-learning-approach-considering-dynamic-non-local-spa}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Forecast network-wide traffic states for multiple steps ahead: A deep learning approach considering dynamic non-local spatial correlation and non-stationary temporal dependency — https://4ort.xyz/entity/forecast-network-wide-traffic-states-for-multiple-steps-ahead-a-deep-learning-approach-considering-dynamic-non-local-spa (retrieved 2026-05-24)
