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Although existing solutions effectively capture general spatio-temporal features, they often overlook co-movement patterns among moving objects, which are crucial for applications such as traffic simulation, ride-sharing, and crowd modeling. Moreover, most approaches rely on road network representations, limiting generalization and failing to preserve fine-grained mobility trends. To tackle these challenges, we propose CA-Gen, a Co-movement Aware trajectory generation framework based on Generative Adversarial Networks (GANs). Instead of employing road vertex mapping, we introduce a hot grid-cell based trajectory representation to enhance robustness and generalization. To better simulate real-world co-movement patterns, we design a way-point guided search algorithm based on frequent subsequence mining. Extensive experiments on real-world datasets show that CA-Gen significantly outperforms existing SOTA methods, generating realistic trajectories that retain both individual mobility characteristics and co-movement trends, providing a privacy-preserving and high-fidelity solution for mobility analysis.<\/jats:p>","DOI":"10.1007\/s10707-025-00556-w","type":"journal-article","created":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T06:23:04Z","timestamp":1757917384000},"page":"1093-1119","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Co-movement aware trajectory generation via waypoint-guided generative adversarial networks"],"prefix":"10.1007","volume":"29","author":[{"given":"Ziwen","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lisi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nan","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoshiharu","family":"Ishikawa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,15]]},"reference":[{"key":"556_CR1","doi-asserted-by":"crossref","unstructured":"Rao X, Wang H, Zhang L, Li J, Shang S, Han P (2022) FOGS: first-order gradient supervision with learning-based graph for traffic flow forecasting. 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