{"id":"https://openalex.org/W7140102749","doi":"https://doi.org/10.48550/arxiv.2603.19474","title":"TRACE: Trajectory Recovery with State Propagation Diffusion for Urban Mobility","display_name":"TRACE: Trajectory Recovery with State Propagation Diffusion for Urban Mobility","publication_year":2026,"publication_date":"2026-03-19","ids":{"openalex":"https://openalex.org/W7140102749","doi":"https://doi.org/10.48550/arxiv.2603.19474"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.19474","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19474","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.19474","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130358921","display_name":"Jinming Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jinming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130398420","display_name":"Hai Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Hai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130402974","display_name":"Hongkai Wen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen, Hongkai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130357195","display_name":"Geyong Min","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Min, Geyong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130369776","display_name":"Man Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Man","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.7556999921798706,"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"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.7556999921798706,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.08500000089406967,"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/T11106","display_name":"Data Management and Algorithms","score":0.05469999834895134,"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/trajectory","display_name":"Trajectory","score":0.7689999938011169},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7206000089645386},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5945000052452087},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.5613999962806702},{"id":"https://openalex.org/keywords/trace","display_name":"TRACE (psycholinguistics)","score":0.46230000257492065},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.43230000138282776},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.34950000047683716},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.34619998931884766}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7689999938011169},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7206000089645386},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6721000075340271},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5945000052452087},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.5613999962806702},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.46230000257492065},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.43230000138282776},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4300999939441681},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39899998903274536},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3828999996185303},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.34950000047683716},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.34619998931884766},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.3418000042438507},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3391000032424927},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30079999566078186},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.2937000095844269},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.27079999446868896},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2655999958515167},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.2547999918460846},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.19474","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19474","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.19474","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19474","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5141471028327942,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"High-quality":[0],"GPS":[1],"trajectories":[2,32,44,80],"are":[3,33],"essential":[4,51],"for":[5,70,153],"location-based":[6],"web":[7],"services":[8],"and":[9,16,24,36,47,57,78,83,114,155,162],"smart":[10],"city":[11],"applications,":[12],"including":[13],"navigation,":[14],"ride-sharing":[15],"delivery.":[17],"However,":[18],"due":[19],"to":[20,121],"low":[21],"sampling":[22],"rates":[23],"limited":[25],"infrastructure":[26],"coverage":[27],"during":[28,107],"data":[29],"collection,":[30],"real-world":[31,132],"often":[34],"sparse":[35,82],"feature":[37],"unevenly":[38],"distributed":[39],"location":[40,157],"points.":[41],"Recovering":[42],"these":[43],"into":[45],"dense":[46,77],"continuous":[48,79],"forms":[49],"is":[50,169],"but":[52],"challenging,":[53],"given":[54],"their":[55],"complex":[56],"irregular":[58],"spatio-temporal":[59],"patterns.":[60],"In":[61],"this":[62],"paper,":[63],"we":[64,91],"introduce":[65],"a":[66,93,101],"novel":[67,102],"diffusion":[68],"model":[69],"trajectory":[71,126],"recovery":[72],"named":[73],"TRACE,":[74,90],"which":[75,99],"reconstruct":[76,123],"from":[81,118],"incomplete":[84],"inputs.":[85],"At":[86],"the":[87,108,138,151,160],"core":[88],"of":[89,164],"propose":[92],"State":[94],"Propagation":[95],"Diffusion":[96],"Model":[97],"(SPDM),":[98],"integrates":[100],"memory":[103],"mechanism,":[104],"so":[105],"that":[106,135],"denoising":[109],"process,":[110],"TRACE":[111,136],"can":[112],"retain":[113],"leverage":[115],"intermediate":[116],"results":[117],"previous":[119],"steps":[120],"effectively":[122],"those":[124],"hard-to-recover":[125],"segments.":[127],"Extensive":[128],"experiments":[129],"on":[130],"multiple":[131],"datasets":[133],"show":[134],"outperforms":[137],"state-of-the-art,":[139],"offering":[140],"$>$26\\%":[141],"accuracy":[142],"improvement":[143],"without":[144],"significant":[145],"inference":[146],"overhead.":[147],"Our":[148],"work":[149],"strengthens":[150],"foundation":[152],"mobile":[154],"web-connected":[156],"services,":[158],"advancing":[159],"quality":[161],"fairness":[163],"data-driven":[165],"urban":[166],"applications.":[167],"Code":[168],"available":[170],"at:":[171],"https://github.com/JinmingWang/TRACE":[172]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-24T00:00:00"}