Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method
Summary
Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method is a scholarly article[1].
Key Facts
Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
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.
APA4ort.xyz Knowledge Graph. (2026). Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method. Retrieved May 24, 2026, from https://4ort.xyz/entity/short-term-origin-destination-demand-prediction-in-urban-rail-transit-systems-a-channel-wise-attentive-split-convolution
