{"id":"https://openalex.org/W7114930322","doi":"https://doi.org/10.1145/3748636.3763227","title":"VisitFrequency-Diffusion: Leveraging Recurrent Visits for Long-Term Individual Trajectory Forecasting","display_name":"VisitFrequency-Diffusion: Leveraging Recurrent Visits for Long-Term Individual Trajectory Forecasting","publication_year":2025,"publication_date":"2025-11-03","ids":{"openalex":"https://openalex.org/W7114930322","doi":"https://doi.org/10.1145/3748636.3763227"},"language":null,"primary_location":{"id":"doi:10.1145/3748636.3763227","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3748636.3763227","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3748636.3763227","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd 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/10.1145/3748636.3763227","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Ziyan Yang","orcid":"https://orcid.org/0009-0009-4088-0667"},"institutions":[{"id":"https://openalex.org/I3125743391","display_name":"China University of Geosciences (Beijing)","ror":"https://ror.org/04q6c7p66","country_code":"CN","type":"education","lineage":["https://openalex.org/I3125743391"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ziyan Yang","raw_affiliation_strings":["School of Information Engineering, China University of Geosciences, Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0009-4088-0667","affiliations":[{"raw_affiliation_string":"School of Information Engineering, China University of Geosciences, Beijing, Beijing, China","institution_ids":["https://openalex.org/I3125743391"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Shuhui Gong","orcid":"https://orcid.org/0000-0003-4073-8002"},"institutions":[{"id":"https://openalex.org/I3125743391","display_name":"China University of Geosciences (Beijing)","ror":"https://ror.org/04q6c7p66","country_code":"CN","type":"education","lineage":["https://openalex.org/I3125743391"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuhui Gong","raw_affiliation_strings":["School of Information Engineering, China University of Geosciences, Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4073-8002","affiliations":[{"raw_affiliation_string":"School of Information Engineering, China University of Geosciences, Beijing, Beijing, China","institution_ids":["https://openalex.org/I3125743391"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xinqi Liu","orcid":"https://orcid.org/0009-0008-3735-4327"},"institutions":[{"id":"https://openalex.org/I3125743391","display_name":"China University of Geosciences (Beijing)","ror":"https://ror.org/04q6c7p66","country_code":"CN","type":"education","lineage":["https://openalex.org/I3125743391"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinqi Liu","raw_affiliation_strings":["School of Information Engineering, China University of Geosciences, Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0008-3735-4327","affiliations":[{"raw_affiliation_string":"School of Information Engineering, China University of Geosciences, Beijing, Beijing, China","institution_ids":["https://openalex.org/I3125743391"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jiahao Lv","orcid":"https://orcid.org/0009-0009-7455-6507"},"institutions":[{"id":"https://openalex.org/I3125743391","display_name":"China University of Geosciences (Beijing)","ror":"https://ror.org/04q6c7p66","country_code":"CN","type":"education","lineage":["https://openalex.org/I3125743391"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiahao Lv","raw_affiliation_strings":["School of Information Engineering, China University of Geosciences, Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0009-7455-6507","affiliations":[{"raw_affiliation_string":"School of Information Engineering, China University of Geosciences, Beijing, Beijing, China","institution_ids":["https://openalex.org/I3125743391"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Changjian Liu","orcid":"https://orcid.org/0009-0008-6235-6981"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changjian Liu","raw_affiliation_strings":["School of Earth and Space Sciences, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0008-6235-6981","affiliations":[{"raw_affiliation_string":"School of Earth and Space Sciences, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jilin Hu","orcid":"https://orcid.org/0000-0002-7739-7769"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jilin Hu","raw_affiliation_strings":["East China Normal University-Shanghai Engineering Research Center of Big Data Management, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-7739-7769","affiliations":[{"raw_affiliation_string":"East China Normal University-Shanghai Engineering Research Center of Big Data Management, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"last","author":{"id":null,"display_name":"Hongbin Pei","orcid":"https://orcid.org/0000-0002-7157-9959"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongbin Pei","raw_affiliation_strings":["School of Cyber Science and Engineering, Xian Jiaotong University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-7157-9959","affiliations":[{"raw_affiliation_string":"School of Cyber Science and Engineering, Xian Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I3125743391"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.55206213,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1190","last_page":"1193"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.5361999869346619,"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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.5361999869346619,"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"}},{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.4458000063896179,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10698","display_name":"Transportation Planning and Optimization","score":0.0032999999821186066,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.8992999792098999},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.6797000169754028},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.61080002784729},{"id":"https://openalex.org/keywords/beijing","display_name":"Beijing","score":0.5449000000953674},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.47440001368522644},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.4189000129699707},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.3939000070095062}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.8992999792098999},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.6797000169754028},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6700000166893005},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.61080002784729},{"id":"https://openalex.org/C2778304055","wikidata":"https://www.wikidata.org/wiki/Q657474","display_name":"Beijing","level":3,"score":0.5449000000953674},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.47440001368522644},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4577000141143799},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.451200008392334},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.4189000129699707},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39500001072883606},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3939000070095062},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.36739999055862427},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.30059999227523804},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.2734000086784363},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2531999945640564}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3748636.3763227","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3748636.3763227","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3748636.3763227","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3748636.3763227","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3748636.3763227","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3748636.3763227","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.41757145524024963,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G1193046859","display_name":null,"funder_award_id":"42401565","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321132","display_name":"China University of Geosciences, Beijing","ror":"https://ror.org/04q6c7p66"},{"id":"https://openalex.org/F4320328899","display_name":"China University of Geosciences","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7114930322.pdf","grobid_xml":"https://content.openalex.org/works/W7114930322.grobid-xml"},"referenced_works_count":5,"referenced_works":["https://openalex.org/W4312305613","https://openalex.org/W4313196348","https://openalex.org/W4362717312","https://openalex.org/W4386076060","https://openalex.org/W4405424311"],"related_works":[],"abstract_inverted_index":{"Individual":[0],"trajectory":[1,60,81,143,150,183],"prediction":[2,61,184],"plays":[3],"a":[4,54,122,127,166,189],"crucial":[5],"role":[6],"in":[7,17,26,130,173],"intelligent":[8],"transportation":[9],"systems.":[10],"While":[11],"existing":[12,131],"methods":[13],"demonstrate":[14],"strong":[15],"performance":[16],"short-term":[18],"forecasting":[19],"(e.g.,":[20],"minute-level":[21],"predictions),":[22],"they":[23],"are":[24],"limited":[25],"modeling":[27,120],"long-term":[28,58],"patterns":[29],"(day-level":[30],"predictions).":[31],"The":[32],"key":[33,64],"challenge":[34],"is":[35],"capturing":[36],"both":[37],"the":[38,114,134],"periodic":[39,178],"regularity":[40],"and":[41,92,110,141,177,193],"stochastic":[42],"variability":[43],"of":[44],"urban":[45],"mobility.":[46],"To":[47],"bridge":[48],"this":[49],"gap,":[50],"we":[51],"propose":[52],"VF-Diffusion,":[53],"novel":[55],"framework":[56,181],"for":[57,163,197],"individual":[59],"with":[62,113,121,175],"three":[63],"innovations:":[65],"(1)":[66],"A":[67,80,94],"direction-sensitive":[68],"diffusion":[69],"model":[70],"that":[71,84,97,153],"generates":[72],"baseline":[73,115],"trajectories":[74],"by":[75,185],"learning":[76],"motion":[77],"trends;":[78],"(2)":[79],"rectification":[82],"module":[83],"refines":[85],"spatial":[86],"displacements":[87],"using":[88],"historical":[89],"median":[90],"coordinates;":[91],"(3)":[93],"frequency-sensitive":[95,123],"mechanism":[96],"identifies":[98],"high-frequency":[99],"visit":[100],"locations,":[101],"predicts":[102],"their":[103],"temporal":[104],"sequences":[105],"via":[106],"an":[107],"ensemble":[108],"model,":[109],"integrates":[111],"them":[112],"trajectory.":[116],"By":[117],"combining":[118],"generative":[119],"mechanism,":[124],"VF-Diffusion":[125],"fills":[126],"critical":[128],"gap":[129],"methods,":[132],"offering":[133],"ability":[135],"to":[136],"predict":[137],"new":[138],"visiting":[139],"areas":[140,174],"improve":[142],"accuracy.":[144],"Extensive":[145],"experiments":[146],"on":[147],"Beijing":[148],"Wi-Fi":[149],"data":[151],"show":[152],"our":[154],"method":[155],"outperforms":[156],"four":[157],"baselines,":[158],"achieving":[159],"about":[160],"90%":[161],"accuracy":[162],"predictions":[164],"within":[165],"1":[167],"km":[168],"threshold.":[169],"It":[170],"particularly":[171],"excels":[172],"frequent":[176],"visits.":[179],"This":[180],"advances":[182],"enabling":[186],"multi-day":[187],"forecasting,":[188],"previously":[190],"underexplored":[191],"capability,":[192],"offers":[194],"practical":[195],"solutions":[196],"enhancing":[198],"smart":[199],"city":[200],"infrastructure.":[201]},"counts_by_year":[],"updated_date":"2026-03-12T06:13:28.667946","created_date":"2025-12-12T00:00:00"}