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Additionally, we introduce a Weighted Position Loss function to emphasize accurate prediction in later time steps, ensuring that cumulative errors are minimized. Our approach outperforms existing methods in terms of both prediction accuracy and computational efficiency, achieving significantly lower Average Displacement Error and Final Displacement Error across multiple benchmarks, including the ETH\/UCY dataset and SDD dataset. Moreover, ARP-STGCN consistently demonstrates faster inference times compared to state-of-the-art methods, making it suitable for real-time applications. Code is available at\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/fantot\/ARP-STGCN\/\" ext-link-type=\"uri\">https:\/\/github.com\/fantot\/ARP-STGCN\/<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1007\/s40747-025-02068-4","type":"journal-article","created":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T10:23:35Z","timestamp":1760091815000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["ARP-STGCN: a fast attraction\u2013repulsion-potential based spatio-temporal graph convolutional network with imputation for pedestrian trajectory prediction"],"prefix":"10.1007","volume":"11","author":[{"given":"Bin","family":"Fang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0709-1714","authenticated-orcid":false,"given":"Fangtao","family":"Qin","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,10]]},"reference":[{"key":"2068_CR1","doi-asserted-by":"crossref","unstructured":"Xu Y, Bazarjani A, Chi H-G, Choi C, Fu Y (2023) Uncovering the missing pattern: unified framework towards trajectory imputation and prediction. 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