Home › Entities › academia › Convolutional Neural Network-Based Bidirectional Gated Recurrent Unit–Additive Attention Mechanism Hybrid Deep Neural Networks for Short-Term Traffic Flow Prediction

Convolutional Neural Network-Based Bidirectional Gated Recurrent Unit–Additive Attention Mechanism Hybrid Deep Neural Networks for Short-Term Traffic Flow Prediction

Research article (Sustainability, 2024) · cited 30× · AI/ML
academia openalex:W4392239991
Press Enter · cited answer in seconds

Convolutional Neural Network-Based Bidirectional Gated Recurrent Unit–Additive Attention Mechanism Hybrid Deep Neural Networks for Short-Term Traffic Flow Prediction

Summary

Convolutional Neural Network-Based Bidirectional Gated Recurrent Unit–Additive Attention Mechanism Hybrid Deep Neural Networks for Short-Term Traffic Flow Prediction is a scholarly article[1].

Key Facts

  • Convolutional Neural Network-Based Bidirectional Gated Recurrent Unit–Additive Attention Mechanism Hybrid Deep Neural Networks for Short-Term Traffic Flow Prediction's instance of is recorded as scholarly article[2].

References

Programmatic citations — every numbered marker resolves to a verifiable graph row below.

Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

📑 Cite this page

Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.

APA 4ort.xyz Knowledge Graph. (2026). Convolutional Neural Network-Based Bidirectional Gated Recurrent Unit–Additive Attention Mechanism Hybrid Deep Neural Networks for Short-Term Traffic Flow Prediction. Retrieved May 24, 2026, from https://4ort.xyz/entity/convolutional-neural-network-based-bidirectional-gated-recurrent-unitadditive-attention-mechanism-hybrid-deep-neural-net
MLA “Convolutional Neural Network-Based Bidirectional Gated Recurrent Unit–Additive Attention Mechanism Hybrid Deep Neural Networks for Short-Term Traffic Flow Prediction.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/convolutional-neural-network-based-bidirectional-gated-recurrent-unitadditive-attention-mechanism-hybrid-deep-neural-net.
BibTeX @misc{4ortxyz_convolutional-neural-network-based-bidirectional-gated-recurrent-unitadditive-attention-mechanism-hybrid-deep-neural-net_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Convolutional Neural Network-Based Bidirectional Gated Recurrent Unit–Additive Attention Mechanism Hybrid Deep Neural Networks for Short-Term Traffic Flow Prediction}}, year = {2026}, url = {https://4ort.xyz/entity/convolutional-neural-network-based-bidirectional-gated-recurrent-unitadditive-attention-mechanism-hybrid-deep-neural-net}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Convolutional Neural Network-Based Bidirectional Gated Recurrent Unit–Additive Attention Mechanism Hybrid Deep Neural Networks for Short-Term Traffic Flow Prediction — https://4ort.xyz/entity/convolutional-neural-network-based-bidirectional-gated-recurrent-unitadditive-attention-mechanism-hybrid-deep-neural-net (retrieved 2026-05-24)

Canonical URL: https://4ort.xyz/entity/convolutional-neural-network-based-bidirectional-gated-recurrent-unitadditive-attention-mechanism-hybrid-deep-neural-net · Last refreshed: