{"id":"https://openalex.org/W7124165298","doi":"https://doi.org/10.48550/arxiv.2601.08482","title":"DiffMM: Efficient Method for Accurate Noisy and Sparse Trajectory Map Matching via One Step Diffusion","display_name":"DiffMM: Efficient Method for Accurate Noisy and Sparse Trajectory Map Matching via One Step Diffusion","publication_year":2026,"publication_date":"2026-01-13","ids":{"openalex":"https://openalex.org/W7124165298","doi":"https://doi.org/10.48550/arxiv.2601.08482"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.08482","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.08482","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":null,"license_id":null,"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.2601.08482","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123001265","display_name":"Chenxu Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Chenxu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054708051","display_name":"Bin Yang","orcid":"https://orcid.org/0000-0001-7819-2290"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Sean Bin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5111855573","display_name":"Jilin Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Jilin","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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.8531000018119812,"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":0.8531000018119812,"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":0.06030000001192093,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.04610000178217888,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"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/map-matching","display_name":"Map matching","score":0.8378999829292297},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.6743999719619751},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.6218000054359436},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.46320000290870667},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.4399999976158142},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.3889000117778778},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.3741999864578247},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.3741999864578247}],"concepts":[{"id":"https://openalex.org/C2778559875","wikidata":"https://www.wikidata.org/wiki/Q1892023","display_name":"Map matching","level":3,"score":0.8378999829292297},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7225000262260437},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6743999719619751},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6218000054359436},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4934000074863434},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.46320000290870667},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.4399999976158142},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4163999855518341},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.3889000117778778},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.3741999864578247},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.3741999864578247},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.37059998512268066},{"id":"https://openalex.org/C188048851","wikidata":"https://www.wikidata.org/wiki/Q2298569","display_name":"Road map","level":2,"score":0.36910000443458557},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.34299999475479126},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33980000019073486},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.3375999927520752},{"id":"https://openalex.org/C55128770","wikidata":"https://www.wikidata.org/wiki/Q5275440","display_name":"Diffusion map","level":4,"score":0.31450000405311584},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2921000123023987},{"id":"https://openalex.org/C123853557","wikidata":"https://www.wikidata.org/wiki/Q7098946","display_name":"Optimal matching","level":3,"score":0.2856000065803528},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2667999863624573},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C61455927","wikidata":"https://www.wikidata.org/wiki/Q1030529","display_name":"Blossom algorithm","level":3,"score":0.26660001277923584},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.08482","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.08482","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2601.08482","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.08482","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":null,"license_id":null,"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":0.7315260171890259,"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":{"Map":[0],"matching":[1,24,62,69,116,152],"for":[2,9,22,162],"sparse":[3,163],"trajectories":[4,164],"is":[5],"a":[6,72,79,97,108,118],"fundamental":[7],"problem":[8],"many":[10],"trajectory-based":[11],"applications,":[12],"e.g.,":[13],"traffic":[14,17],"scheduling":[15],"and":[16,90,129,159,165],"flow":[18],"analysis.":[19],"Existing":[20],"methods":[21,38,153],"map":[23,61,115,151],"are":[25],"generally":[26],"based":[27],"on":[28,140],"Hidden":[29],"Markov":[30],"Model":[31],"(HMM)":[32],"or":[33,47],"encoder-decoder":[34],"framework.":[35],"However,":[36],"these":[37,54],"continue":[39],"to":[40,113],"face":[41],"significant":[42],"challenges":[43],"when":[44],"handling":[45],"noisy":[46],"sparsely":[48],"sampled":[49],"GPS":[50],"trajectories.":[51],"To":[52],"address":[53],"limitations,":[55],"we":[56,106],"propose":[57,107],"DiffMM,":[58],"an":[59,102],"encoder-diffusion-based":[60],"framework":[63],"that":[64,84,145],"produces":[65],"effective":[66],"yet":[67],"efficient":[68],"results":[70],"through":[71,101,117],"one-step":[73],"diffusion":[74,111],"process.":[75],"We":[76,136],"first":[77],"introduce":[78],"road":[80,94,131,167],"segment-aware":[81],"trajectory":[82,89,128,142],"encoder":[83],"jointly":[85],"embeds":[86],"the":[87,123,127],"input":[88],"its":[91],"surrounding":[92],"candidate":[93,130],"segments":[95,132],"into":[96],"shared":[98],"latent":[99],"space":[100],"attention":[103],"mechanism.":[104],"Next,":[105],"one":[109],"step":[110],"method":[112],"realize":[114],"shortcut":[119],"model":[120],"by":[121],"leveraging":[122],"joint":[124],"embedding":[125],"of":[126,156],"as":[133],"conditioning":[134],"context.":[135],"conduct":[137],"extensive":[138],"experiments":[139],"large-scale":[141],"datasets,":[143],"demonstrating":[144],"our":[146],"approach":[147],"consistently":[148],"outperforms":[149],"state-of-the-art":[150],"in":[154],"terms":[155],"both":[157],"accuracy":[158],"efficiency,":[160],"particularly":[161],"complex":[166],"network":[168],"topologies.":[169]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-01-15T00:00:00"}