{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T09:06:36Z","timestamp":1782378396562,"version":"3.54.5"},"reference-count":69,"publisher":"Association for Computing Machinery (ACM)","issue":"2","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Spatial Algorithms Syst."],"published-print":{"date-parts":[[2026,6,30]]},"abstract":"<jats:p>\n                    Advances in tracking technologies have spurred the rapid growth of large-scale trajectory data. Building a compact collection of pathlets, referred to as a\n                    <jats:italic toggle=\"yes\">trajectory pathlet dictionary<\/jats:italic>\n                    , is essential for supporting mobility-related applications. Existing methods typically adopt a top-down approach, generating numerous candidate pathlets and selecting a subset, leading to high memory usage and redundant storage from overlapping pathlets. To overcome these limitations, we propose a bottom-up strategy that incrementally merges basic pathlets to build the dictionary, reducing memory requirements by up to 24,000 times compared to baseline methods. The approach begins with unit-length pathlets and iteratively merges them while optimizing utility, which is defined using newly introduced metrics of\n                    <jats:italic toggle=\"yes\">trajectory loss<\/jats:italic>\n                    and\n                    <jats:italic toggle=\"yes\">representability<\/jats:italic>\n                    . We develop a deep reinforcement learning framework,\n                    <jats:sc>PathletRL<\/jats:sc>\n                    , which utilizes Deep Q-Networks (\n                    <jats:sc>DQN<\/jats:sc>\n                    ) to approximate the utility function, resulting in a compact and efficient pathlet dictionary. Experiments on both synthetic and real-world datasets demonstrate that our method outperforms state-of-the-art techniques, reducing the size of the constructed dictionary by up to 65.8%. Additionally, our results show that only half of the dictionary pathlets are needed to reconstruct 85% of the original trajectory data. Building on\n                    <jats:sc>PathletRL<\/jats:sc>\n                    , we introduce\n                    <jats:sc>PathletRL++<\/jats:sc>\n                    , which extends the original model by incorporating a richer state representation and an improved reward function to optimize decision-making during pathlet merging. These enhancements enable the agent to gain a more nuanced understanding of the environment, leading to higher-quality pathlet dictionaries.\n                    <jats:sc>PathletRL++<\/jats:sc>\n                    achieves even greater dictionary size reduction, surpassing the performance of\n                    <jats:sc>PathletRL<\/jats:sc>\n                    , while maintaining high trajectory representability.\n                  <\/jats:p>","DOI":"10.1145\/3801963","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T20:56:49Z","timestamp":1773349009000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["PathletRL++: Optimizing Trajectory Pathlet Extraction and Dictionary Formation via Reinforcement Learning"],"prefix":"10.1145","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9430-1407","authenticated-orcid":false,"given":"Gian","family":"Alix","sequence":"first","affiliation":[{"name":"Eecs, York University","place":["Toronto, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2277-8038","authenticated-orcid":false,"given":"Arian","family":"Haghparast","sequence":"additional","affiliation":[{"name":"Eecs, York University","place":["Toronto, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0138-2541","authenticated-orcid":false,"given":"Manos","family":"Papagelis","sequence":"additional","affiliation":[{"name":"Eecs, York University","place":["Toronto, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,25]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"11","volume-title":"Proc. 36th Int. 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