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The proposed model comprises four components: (1) a temporal feature extraction module is devised to extract time information within network-wide historical OD data; (2) a spatial correlation learning module is introduced to address the data malconformation and data dimensionality problems, which provides an interpretable spatial correlation quantization method; (3) an input control-gated mechanism is originally proposed to solve the data lag problem, which combines the processed available OD flow and real-time inflow\/outflow; (4) a fusion module combines historical spatial\u2013temporal features with real-time information to achieve accurate OD flow prediction. We also further discuss the interpretability of the model in detail. The ST-LSTM model is evaluated by sufficient experiments on two large-scale actual subway datasets from Nanjing and Beijing, and the experimental results demonstrate that it can better learn the spatial\u2013temporal correlations and exceed the rest benchmarking methods.<\/jats:p>","DOI":"10.1007\/s40747-024-01391-6","type":"journal-article","created":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T02:08:35Z","timestamp":1711937315000},"page":"4675-4696","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Short-term origin\u2013destination flow prediction for urban rail network: a deep learning method based on multi-source big data"],"prefix":"10.1007","volume":"10","author":[{"given":"Hongmeng","family":"Cui","sequence":"first","affiliation":[]},{"given":"Bingfeng","family":"Si","sequence":"additional","affiliation":[]},{"given":"Jiayuan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Ben","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Weiting","family":"Pan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,1]]},"reference":[{"key":"1391_CR1","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.future.2021.03.003","volume":"121","author":"L Bai","year":"2021","unstructured":"Bai L, Yao L, Wang X, Li C, Zhang X (2021) Deep spatial\u2013temporal sequence modeling for multi-step passenger demand prediction. 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