{"id":"https://openalex.org/W4390100413","doi":"https://doi.org/10.1145/3589132.3625622","title":"PathletRL: Trajectory Pathlet Dictionary Construction using Reinforcement Learning","display_name":"PathletRL: Trajectory Pathlet Dictionary Construction using Reinforcement Learning","publication_year":2023,"publication_date":"2023-11-13","ids":{"openalex":"https://openalex.org/W4390100413","doi":"https://doi.org/10.1145/3589132.3625622"},"language":"en","primary_location":{"id":"doi:10.1145/3589132.3625622","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3589132.3625622","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3589132.3625622","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3589132.3625622","any_repository_has_fulltext":null},"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/I192455969","display_name":"York University","ror":"https://ror.org/05fq50484","country_code":"CA","type":"education","lineage":["https://openalex.org/I192455969"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Gian Alix","raw_affiliation_strings":["York University, Toronto, Ontario, Canada"],"affiliations":[{"raw_affiliation_string":"York University, Toronto, Ontario, Canada","institution_ids":["https://openalex.org/I192455969"]}]},{"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/I192455969","display_name":"York University","ror":"https://ror.org/05fq50484","country_code":"CA","type":"education","lineage":["https://openalex.org/I192455969"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Manos Papagelis","raw_affiliation_strings":["York University, Toronto, Ontario, Canada"],"affiliations":[{"raw_affiliation_string":"York University, Toronto, Ontario, Canada","institution_ids":["https://openalex.org/I192455969"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5075974377"],"corresponding_institution_ids":["https://openalex.org/I192455969"],"apc_list":null,"apc_paid":null,"fwci":0.2038,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.4886385,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9995999932289124,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9936000108718872,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8295970559120178},{"id":"https://openalex.org/keywords/merge","display_name":"Merge (version control)","score":0.7288922667503357},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7174758911132812},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.699309766292572},{"id":"https://openalex.org/keywords/dictionary-learning","display_name":"Dictionary learning","score":0.5555063486099243},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5049172043800354},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4986586570739746},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.47304147481918335},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.4131115674972534},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.1007404625415802}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8295970559120178},{"id":"https://openalex.org/C197129107","wikidata":"https://www.wikidata.org/wiki/Q1921621","display_name":"Merge (version control)","level":2,"score":0.7288922667503357},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7174758911132812},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.699309766292572},{"id":"https://openalex.org/C2988886741","wikidata":"https://www.wikidata.org/wiki/Q25304494","display_name":"Dictionary learning","level":3,"score":0.5555063486099243},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5049172043800354},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4986586570739746},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.47304147481918335},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.4131115674972534},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.1007404625415802},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3589132.3625622","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3589132.3625622","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3589132.3625622","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3589132.3625622","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3589132.3625622","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3589132.3625622","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.5099999904632568,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4390100413.pdf","grobid_xml":"https://content.openalex.org/works/W4390100413.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W169931978","https://openalex.org/W1981398125","https://openalex.org/W2047348678","https://openalex.org/W2126194848","https://openalex.org/W2515868912","https://openalex.org/W2768915865","https://openalex.org/W2902907165","https://openalex.org/W2943793359","https://openalex.org/W2945989849","https://openalex.org/W2965539152","https://openalex.org/W2982321152","https://openalex.org/W3007866289","https://openalex.org/W3092935656","https://openalex.org/W3130891974","https://openalex.org/W3152249529","https://openalex.org/W3155872716","https://openalex.org/W4213457653","https://openalex.org/W4243304115","https://openalex.org/W4385283024","https://openalex.org/W6797922541"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W2146343568","https://openalex.org/W98480971","https://openalex.org/W2150291671","https://openalex.org/W2013643406","https://openalex.org/W2027972911","https://openalex.org/W2157978810","https://openalex.org/W2597809628","https://openalex.org/W3046370962"],"abstract_inverted_index":{"Sophisticated":[0],"location":[1],"and":[2,49,60,82,97,151,169,202],"tracking":[3],"technologies":[4],"have":[5],"led":[6],"to":[7,88,99,103,140,147,185,217,220,238],"the":[8,84,90,104,187,196,209,212,231,234,242],"generation":[9],"of":[10,13,23,33,79,130,136,166,211,230,241],"vast":[11],"amounts":[12],"trajectory":[14,38,53,167,244],"data.":[15,204,245],"Of":[16],"interest":[17],"is":[18,95,146,160,176,224,236],"constructing":[19,66,120],"a":[20,30,37,67,71,76,116,121],"small":[21],"set":[22,78],"basic":[24],"building":[25],"blocks":[26],"that":[27,106,124,179,192,227],"can":[28,43,108,207],"represent":[29],"wide":[31],"range":[32],"trajectories,":[34],"known":[35],"as":[36,52],"pathlet":[39,68,122],"dictionary.":[40,91],"This":[41],"dictionary":[42,69,123,214,235],"be":[44],"useful":[45],"in":[46,233],"various":[47],"tasks":[48],"applications,":[50],"such":[51],"compression,":[54],"travel":[55],"time":[56],"estimation,":[57],"route":[58],"planning,":[59],"navigation":[61],"services.":[62],"Existing":[63],"methods":[64,132],"for":[65,119],"use":[70],"top-down":[72],"approach,":[73],"which":[74],"generates":[75],"large":[77],"candidate":[80],"pathlets":[81,107,150,232],"selects":[83],"most":[85],"popular":[86],"ones":[87],"form":[89],"However,":[92],"this":[93],"approach":[94,118],"memory-intensive":[96],"leads":[98],"redundant":[100],"storage":[101,128],"due":[102],"assumption":[105],"overlap.":[109],"To":[110],"address":[111],"these":[112],"limitations,":[113],"we":[114],"propose":[115],"bottom-up":[117],"significantly":[125],"reduces":[126],"memory":[127],"needs":[129],"baseline":[131],"by":[133,215],"multiple":[134],"orders":[135],"magnitude":[137],"(by":[138],"up":[139,216],"~24K\u00d7":[141],"better).":[142],"The":[143,158],"key":[144],"idea":[145],"initialize":[148],"unit-length":[149],"iteratively":[152],"merge":[153],"them,":[154],"while":[155],"maximizing":[156],"utility.":[157],"utility":[159,188],"defined":[161],"using":[162],"newly":[163],"introduced":[164],"metrics":[165],"loss":[168],"representability.":[170],"A":[171],"deep":[172],"reinforcement":[173],"learning":[174],"method":[175,194,206],"proposed,":[177],"PathletRL,":[178],"uses":[180],"Deep":[181],"Q":[182],"Networks":[183],"(Dqn)":[184],"approximate":[186],"function.":[189],"Experiments":[190],"show":[191],"our":[193],"outperforms":[195],"current":[197],"state-of-the-art,":[198],"both":[199],"on":[200],"synthetic":[201],"real-world":[203],"Our":[205],"reduce":[208],"size":[210],"constructed":[213],"65.8%":[218],"compared":[219],"other":[221],"methods.":[222],"It":[223],"also":[225],"shown":[226],"only":[228],"half":[229],"needed":[237],"reconstruct":[239],"85%":[240],"original":[243]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}