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Traditional machine learning techniques approach the targets independently, while deep learning strategies may use joint learning with shared representations, both neglecting inter-target causal relationships and potentially compromising the models\u2019 generalization capabilities. Our novel\n            <jats:italic>CausalTrans<\/jats:italic>\n            model introduces a framework to define and leverage the temporal causal interplay between supply and demand, incorporating both temporal and spatial causality into the forecasting process. Additionally, we enhance computational efficiency by introducing an innovative fast attention mechanism that reduces the time complexity from quadratic to linear without sacrificing performance. Our comprehensive experiments show that\n            <jats:italic>CausalTrans<\/jats:italic>\n            significantly surpasses contemporary forecasting methods, achieving up to a 15% reduction in error, thus setting a new benchmark in the field.\n          <\/jats:p>","DOI":"10.1145\/3643848","type":"journal-article","created":{"date-parts":[[2024,2,3]],"date-time":"2024-02-03T11:45:24Z","timestamp":1706960724000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Causal Probabilistic Spatio-Temporal Fusion Transformers in Two-Sided Ride-Hailing Markets"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8644-9893","authenticated-orcid":false,"given":"Shixiang","family":"Wan","sequence":"first","affiliation":[{"name":"AI Lab, DiDi Chuxing, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3567-2194","authenticated-orcid":false,"given":"Shikai","family":"Luo","sequence":"additional","affiliation":[{"name":"ByteDance, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6781-2690","authenticated-orcid":false,"given":"Hongtu","family":"Zhu","sequence":"additional","affiliation":[{"name":"University of North Carolina at Chapel Hill, Chapel Hill, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,10,4]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Alexander Alexandrov Konstantinos Benidis Michael Bohlke-Schneider Valentin Flunkert Jan Gasthaus Tim Januschowski Danielle C. 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