{"id":"https://openalex.org/W4396736145","doi":"https://doi.org/10.1145/3589334.3645644","title":"More Than Routing: Joint GPS and Route Modeling for Refine Trajectory Representation Learning","display_name":"More Than Routing: Joint GPS and Route Modeling for Refine Trajectory Representation Learning","publication_year":2024,"publication_date":"2024-05-08","ids":{"openalex":"https://openalex.org/W4396736145","doi":"https://doi.org/10.1145/3589334.3645644"},"language":"en","primary_location":{"id":"doi:10.1145/3589334.3645644","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3589334.3645644","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2024","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071182342","display_name":"Zhipeng Ma","orcid":"https://orcid.org/0009-0008-1485-0766"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhipeng Ma","raw_affiliation_strings":["Southwest Jiaotong University &amp; Tsinghua University, Institute for AI Industry Research (AIR), Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University &amp; Tsinghua University, Institute for AI Industry Research (AIR), Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064863219","display_name":"Zheyan Tu","orcid":"https://orcid.org/0000-0003-0839-4262"},"institutions":[{"id":"https://openalex.org/I5023651","display_name":"McGill University","ror":"https://ror.org/01pxwe438","country_code":"CA","type":"education","lineage":["https://openalex.org/I5023651"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Zheyan Tu","raw_affiliation_strings":["McGill University &amp; Tsinghua University, Institute for AI Industry Research (AIR), Montreal, Canada"],"affiliations":[{"raw_affiliation_string":"McGill University &amp; Tsinghua University, Institute for AI Industry Research (AIR), Montreal, Canada","institution_ids":["https://openalex.org/I5023651"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088496927","display_name":"Xinhai Chen","orcid":"https://orcid.org/0009-0001-9924-2397"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinhai Chen","raw_affiliation_strings":["Southwest Jiaotong University &amp; Tsinghua University, Institute for AI Industry Research (AIR), Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University &amp; Tsinghua University, Institute for AI Industry Research (AIR), Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100456391","display_name":"Yan Zhang","orcid":"https://orcid.org/0000-0003-2142-5094"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Zhang","raw_affiliation_strings":["Tsinghua University, Institute for AI Industry Research (AIR), Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Institute for AI Industry Research (AIR), Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076144487","display_name":"Deguo Xia","orcid":"https://orcid.org/0000-0003-3366-2230"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Deguo Xia","raw_affiliation_strings":["Baidu Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Baidu Inc., Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011913905","display_name":"Guyue Zhou","orcid":"https://orcid.org/0000-0002-3894-9858"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guyue Zhou","raw_affiliation_strings":["Tsinghua University, Institute for AI Industry Research (AIR), Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Institute for AI Industry Research (AIR), Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103028914","display_name":"Yilun Chen","orcid":"https://orcid.org/0000-0003-0618-3621"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yilun Chen","raw_affiliation_strings":["Tsinghua University, Institute for AI Industry Research (AIR), Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Institute for AI Industry Research (AIR), Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016698883","display_name":"Yu Zheng","orcid":"https://orcid.org/0000-0003-2537-4685"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Zheng","raw_affiliation_strings":["JD iCity, JD Technology &amp; JD Intelligent Cities Research, Beijing, China"],"affiliations":[{"raw_affiliation_string":"JD iCity, JD Technology &amp; JD Intelligent Cities Research, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089973290","display_name":"Jiangtao Gong","orcid":"https://orcid.org/0000-0002-4310-1894"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiangtao Gong","raw_affiliation_strings":["Tsinghua University, Institute for AI Industry Research (AIR), Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Institute for AI Industry Research (AIR), Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5071182342"],"corresponding_institution_ids":["https://openalex.org/I4800084"],"apc_list":null,"apc_paid":null,"fwci":6.4894,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.97451251,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"3064","last_page":"3075"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9932000041007996,"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":0.9932000041007996,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9904000163078308,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9864000082015991,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.797369658946991},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7908620834350586},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.6587586402893066},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5505478978157043},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.47632700204849243},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4720892608165741},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4574216902256012},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4434354305267334},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3399237096309662}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.797369658946991},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7908620834350586},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.6587586402893066},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5505478978157043},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.47632700204849243},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4720892608165741},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4574216902256012},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4434354305267334},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3399237096309662},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3589334.3645644","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3589334.3645644","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2024","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1989491491","https://openalex.org/W2151573792","https://openalex.org/W2549766072","https://openalex.org/W2624735749","https://openalex.org/W2741460999","https://openalex.org/W2767923185","https://openalex.org/W2768256553","https://openalex.org/W2788997482","https://openalex.org/W2795016801","https://openalex.org/W2809128166","https://openalex.org/W2952493731","https://openalex.org/W2962756421","https://openalex.org/W3003858448","https://openalex.org/W3012808657","https://openalex.org/W3013563398","https://openalex.org/W3080501557","https://openalex.org/W3173572290","https://openalex.org/W3210395416","https://openalex.org/W3212024868","https://openalex.org/W3213458765","https://openalex.org/W4200476967","https://openalex.org/W4225832925","https://openalex.org/W4290943511","https://openalex.org/W4290943894","https://openalex.org/W4290945861","https://openalex.org/W4295990491","https://openalex.org/W4306316985","https://openalex.org/W4385270681","https://openalex.org/W4385567963","https://openalex.org/W4385568314","https://openalex.org/W4385767662"],"related_works":["https://openalex.org/W3162200841","https://openalex.org/W2586280620","https://openalex.org/W2805505483","https://openalex.org/W2334071950","https://openalex.org/W2384744344","https://openalex.org/W4233932308","https://openalex.org/W1799694159","https://openalex.org/W2393169196","https://openalex.org/W2366610330","https://openalex.org/W4242143973"],"abstract_inverted_index":{"Trajectory":[0],"representation":[1,59,63,73,185,188],"learning":[2,74],"plays":[3],"a":[4,71,102,139],"pivotal":[5],"role":[6],"in":[7,26,32,53,181],"supporting":[8],"various":[9],"downstream":[10],"tasks,":[11],"such":[12],"as":[13,97],"travel":[14],"time":[15],"estimation,":[16],"trajectory":[17,22,62,93,96,187],"classification":[18],"and":[19,81,94,106,126,186],"Top-k":[20],"similar":[21],"search.":[23],"Traditional":[24],"methods":[25,40,180],"order":[27],"to":[28,36,41,120,152],"filter":[29],"the":[30,43,49,54,58,98,154,158,161],"noise":[31],"GPS":[33,55,92,124],"trajectories":[34,125,128],"tend":[35],"focus":[37],"on":[38,85,164],"routing-based":[39],"simplify":[42],"trajectories.":[44],"However,":[45],"these":[46,133],"approaches":[47],"ignore":[48],"motion":[50],"details":[51],"contained":[52],"data,":[56],"limiting":[57],"capability":[60],"of":[61,101,123,160],"learning.":[64],"To":[65],"fill":[66],"this":[67],"gap,":[68],"we":[69,114,147],"propose":[70],"novel":[72],"framework":[75],"that":[76,175],"is":[77,193],"Jointly":[78],"G":[79],"PS":[80],"Route":[82],"Modeling":[83],"based":[84],"self-supervised":[86,150],"technology,":[87],"namely":[88],"JGRM.":[89],"We":[90,156],"consider":[91],"route":[95,127],"two":[99,116,134,165],"modals":[100],"single":[103],"movement":[104],"observation":[105],"fuse":[107],"information":[108,111,144],"through":[109,168],"inter-modal":[110,143],"interaction.":[112,145],"Specifically,":[113],"develop":[115],"encoders,":[117],"each":[118],"tailored":[119],"capture":[121],"representations":[122,131],"respectively.":[129],"The":[130,171],"from":[132],"modalities":[135],"are":[136],"fed":[137],"into":[138],"shared":[140],"transformer":[141],"for":[142],"Eventually,":[146],"design":[148],"three":[149],"tasks":[151],"train":[153],"model.":[155],"validate":[157],"effectiveness":[159],"proposed":[162],"method":[163],"real-world":[166],"datasets":[167],"extensive":[169],"experiments.":[170],"experimental":[172],"results":[173],"show":[174],"JGRM":[176],"significantly":[177],"outperforms":[178],"existing":[179],"both":[182],"road":[183],"segment":[184],"tasks.":[189],"Our":[190],"source":[191],"code":[192],"available":[194],"at":[195],"Github":[196],"https://github.com/mamazi0131/JGRM.":[197]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
