{"id":"https://openalex.org/W7135092781","doi":"https://doi.org/10.1145/3801963","title":"PathletRL++: Optimizing Trajectory Pathlet Extraction and Dictionary Formation via Reinforcement Learning","display_name":"PathletRL++: Optimizing Trajectory Pathlet Extraction and Dictionary Formation via Reinforcement Learning","publication_year":2026,"publication_date":"2026-03-12","ids":{"openalex":"https://openalex.org/W7135092781","doi":"https://doi.org/10.1145/3801963"},"language":"en","primary_location":{"id":"doi:10.1145/3801963","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3801963","pdf_url":null,"source":{"id":"https://openalex.org/S2503711797","display_name":"ACM Transactions on Spatial Algorithms and Systems","issn_l":"2374-0353","issn":["2374-0353","2374-0361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Spatial Algorithms and Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1145/3801963","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075974377","display_name":"Gian Alix","orcid":"https://orcid.org/0000-0002-9430-1407"},"institutions":[{"id":"https://openalex.org/I4210127512","display_name":"IEC University","ror":"https://ror.org/03kzpe032","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210127512"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Gian Alix","raw_affiliation_strings":["Eecs, York University"],"raw_orcid":"https://orcid.org/0000-0002-9430-1407","affiliations":[{"raw_affiliation_string":"Eecs, York University","institution_ids":["https://openalex.org/I4210127512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115023839","display_name":"Arian Haghparast","orcid":null},"institutions":[{"id":"https://openalex.org/I4210127512","display_name":"IEC University","ror":"https://ror.org/03kzpe032","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210127512"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Arian Haghparast","raw_affiliation_strings":["Eecs, York University"],"raw_orcid":"https://orcid.org/0009-0001-2277-8038","affiliations":[{"raw_affiliation_string":"Eecs, York University","institution_ids":["https://openalex.org/I4210127512"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008923918","display_name":"Manos Papagelis","orcid":"https://orcid.org/0000-0003-0138-2541"},"institutions":[{"id":"https://openalex.org/I4210127512","display_name":"IEC University","ror":"https://ror.org/03kzpe032","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210127512"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Manos Papagelis","raw_affiliation_strings":["Eecs, York University"],"raw_orcid":"https://orcid.org/0000-0003-0138-2541","affiliations":[{"raw_affiliation_string":"Eecs, York University","institution_ids":["https://openalex.org/I4210127512"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210127512"],"apc_list":null,"apc_paid":null,"fwci":6.3527,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.95140634,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":"12","issue":"2","first_page":"1","last_page":"37"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.4350000023841858,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.4350000023841858,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.09160000085830688,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.0908999964594841,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.734000027179718},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.6980000138282776},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5382999777793884},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.47909998893737793},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4244999885559082},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.39239999651908875},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.3824000060558319}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8458999991416931},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.734000027179718},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6980000138282776},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5934000015258789},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5382999777793884},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.47909998893737793},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4244999885559082},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.39239999651908875},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.3824000060558319},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36480000615119934},{"id":"https://openalex.org/C2988886741","wikidata":"https://www.wikidata.org/wiki/Q25304494","display_name":"Dictionary learning","level":3,"score":0.351500004529953},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3111000061035156},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.29809999465942383},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.29739999771118164},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C173246807","wikidata":"https://www.wikidata.org/wiki/Q7833062","display_name":"Trajectory optimization","level":3,"score":0.26899999380111694},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.25360000133514404},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.2531000077724457}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3801963","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3801963","pdf_url":null,"source":{"id":"https://openalex.org/S2503711797","display_name":"ACM Transactions on Spatial Algorithms and Systems","issn_l":"2374-0353","issn":["2374-0353","2374-0361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Spatial Algorithms and Systems","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1145/3801963","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3801963","pdf_url":null,"source":{"id":"https://openalex.org/S2503711797","display_name":"ACM Transactions on Spatial Algorithms and Systems","issn_l":"2374-0353","issn":["2374-0353","2374-0361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Spatial Algorithms and Systems","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1966416504","https://openalex.org/W1985027321","https://openalex.org/W2047348678","https://openalex.org/W2060846151","https://openalex.org/W2135822894","https://openalex.org/W2166771065","https://openalex.org/W2943793359","https://openalex.org/W2965539152","https://openalex.org/W2982321152","https://openalex.org/W3007866289","https://openalex.org/W3130891974","https://openalex.org/W3155872716","https://openalex.org/W4214915569","https://openalex.org/W4287266177","https://openalex.org/W4307472837","https://openalex.org/W4385283024","https://openalex.org/W4401025000","https://openalex.org/W4409336685","https://openalex.org/W4417283694"],"related_works":[],"abstract_inverted_index":{"Advances":[0],"in":[1,137],"tracking":[2],"technologies":[3],"have":[4],"spurred":[5],"the":[6,75,133,159,162,177,186,200,223,232,247],"rapid":[7],"growth":[8],"of":[9,17,109,161,176,185,231,249],"large-scale":[10],"trajectory":[11,23,110,188,255],"data.":[12,189],"Building":[13,190],"a":[14,22,37,46,65,117,138,205,227],"compact":[15,139],"collection":[16],"pathlets,":[18],"referred":[19],"to":[20,49,73,82,86,131,167,182,214,225,235],"as":[21],"pathlet":[24,142,218,237],"dictionary":[25,164,178,243],",":[26,123,193,197,251],"is":[27,103],"essential":[28],"for":[29],"supporting":[30],"mobility-related":[31],"applications.":[32],"Existing":[33],"methods":[34],"typically":[35],"adopt":[36],"top-down":[38],"approach,":[39],"generating":[40],"numerous":[41],"candidate":[42],"pathlets":[43,72,94,179],"and":[44,53,95,112,140,148,209],"selecting":[45],"subset,":[47],"leading":[48,234],"high":[50,254],"memory":[51,78],"usage":[52],"redundant":[54],"storage":[55],"from":[56],"overlapping":[57],"pathlets.":[58],"To":[59],"overcome":[60],"these":[61],"limitations,":[62],"we":[63,194],"propose":[64],"bottom-up":[66],"strategy":[67],"that":[68,152,173],"incrementally":[69],"merges":[70,97],"basic":[71],"build":[74],"dictionary,":[76],"reducing":[77,158],"requirements":[79],"by":[80,165,203],"up":[81,166],"24,000":[83],"times":[84],"compared":[85],"baseline":[87],"methods.":[88],"The":[89],"approach":[90],"begins":[91],"with":[92],"unit-length":[93],"iteratively":[96],"them":[98],"while":[99,252],"optimizing":[100],"utility,":[101],"which":[102,124,198],"defined":[104],"using":[105],"newly":[106],"introduced":[107],"metrics":[108],"loss":[111],"representability":[113],".":[114],"We":[115],"develop":[116],"deep":[118],"reinforcement":[119],"learning":[120],"framework,":[121],"PathletRL":[122,192,250],"utilizes":[125],"Deep":[126],"Q-Networks":[127],"(":[128],"DQN":[129],")":[130],"approximate":[132],"utility":[134],"function,":[135],"resulting":[136],"efficient":[141],"dictionary.":[143],"Experiments":[144],"on":[145,191],"both":[146],"synthetic":[147],"real-world":[149],"datasets":[150],"demonstrate":[151],"our":[153,170],"method":[154],"outperforms":[155],"state-of-the-art":[156],"techniques,":[157],"size":[160,244],"constructed":[163],"65.8%.":[168],"Additionally,":[169],"results":[171],"show":[172],"only":[174],"half":[175],"are":[180],"needed":[181],"reconstruct":[183],"85%":[184],"original":[187,201],"introduce":[195],"PathletRL++":[196,239],"extends":[199],"model":[202],"incorporating":[204],"richer":[206],"state":[207],"representation":[208],"an":[210],"improved":[211],"reward":[212],"function":[213],"optimize":[215],"decision-making":[216],"during":[217],"merging.":[219],"These":[220],"enhancements":[221],"enable":[222],"agent":[224],"gain":[226],"more":[228],"nuanced":[229],"understanding":[230],"environment,":[233],"higher-quality":[236],"dictionaries.":[238],"achieves":[240],"even":[241],"greater":[242],"reduction,":[245],"surpassing":[246],"performance":[248],"maintaining":[253],"representability.":[256]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2026-03-13T00:00:00"}
