{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T22:55:48Z","timestamp":1780095348391,"version":"3.54.0"},"reference-count":39,"publisher":"Institution of Engineering and Technology (IET)","issue":"2","license":[{"start":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T00:00:00Z","timestamp":1770249600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T00:00:00Z","timestamp":1770249600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["CAAI Trans on Intel Tech"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Integrating healthcare systems with intelligent transportation networks represents a critical frontier in modern urban infrastructure, where efficient resource allocation and timely service delivery can significantly impact patient outcomes. However, current approaches often fail to capture the complex interplay between healthcare facility accessibility and transportation dynamics, particularly during emergencies. Additionally, the temporal dependencies in healthcare service delivery follow strict sequential patterns that significantly influence both routine operations and emergency response effectiveness. To address these challenges, we propose a multi\u2010scale spatio\u2010temporal transformer network for healthcare and transportation (MST\u2010HT) that leverages generative AI capabilities. Our model employs multiple specialised transformer networks to model different spatial scales, capturing hidden dependencies while using graph convolutional networks to learn static infrastructure features. The architecture incorporates healthcare district patterns, emergency response corridors and facility distributions through a novel gating mechanism that adaptively combines features based on their predictive importance. The model maintains awareness of critical service delivery patterns by embedding healthcare\u2010specific temporal position information while optimising resource allocation. Experiments on real\u2010world datasets demonstrate MST\u2010HT's superior performance, achieving a 15.7% reduction in emergency response times and a 23.4% improvement in resource allocation efficiency compared to state\u2010of\u2010the\u2010art baselines.<\/jats:p>","DOI":"10.1049\/cit2.70107","type":"journal-article","created":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T02:46:13Z","timestamp":1770345973000},"page":"578-591","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi\u2010Scale Spatio\u2010Temporal Transformer Network for Intelligent Healthcare and Transportation Systems: A Generative AI Approach"],"prefix":"10.1049","volume":"11","author":[{"given":"Huamao","family":"Jiang","sequence":"first","affiliation":[{"name":"The First Affiliated Hospital of Jinzhou Medical University  Jinzhou 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