{"id":"https://openalex.org/W2785433758","doi":"https://doi.org/10.1109/pimrc.2017.8292591","title":"MSTM: A novel map matching approach for low-sampling-rate trajectories","display_name":"MSTM: A novel map matching approach for low-sampling-rate trajectories","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2785433758","doi":"https://doi.org/10.1109/pimrc.2017.8292591","mag":"2785433758"},"language":"en","primary_location":{"id":"doi:10.1109/pimrc.2017.8292591","is_oa":false,"landing_page_url":"https://doi.org/10.1109/pimrc.2017.8292591","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)","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/A5042011491","display_name":"Yupeng Tuo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yupeng Tuo","raw_affiliation_strings":["School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028040417","display_name":"Xiaochun Yun","orcid":"https://orcid.org/0000-0003-2849-1086"},"institutions":[{"id":"https://openalex.org/I4210156404","display_name":"Institute of Information Engineering","ror":"https://ror.org/04r53se39","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210156404"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaochun Yun","raw_affiliation_strings":["Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210156404","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100715816","display_name":"Yongzheng Zhang","orcid":"https://orcid.org/0009-0000-1077-3849"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongzheng Zhang","raw_affiliation_strings":["School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5042011491"],"corresponding_institution_ids":["https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":0.1849,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.49962051,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9997000098228455,"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":0.9997000098228455,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9976999759674072,"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":0.9965000152587891,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/map-matching","display_name":"Map matching","score":0.7768509984016418},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7641390562057495},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.6470108032226562},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.6303830146789551},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5521440505981445},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.5495270490646362},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.526102602481842},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.4928407669067383},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4686305522918701},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4679267406463623},{"id":"https://openalex.org/keywords/space-partitioning","display_name":"Space partitioning","score":0.4429129660129547},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.43393415212631226},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.4232946038246155},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.26151084899902344},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.23570933938026428},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15454638004302979},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08255761861801147}],"concepts":[{"id":"https://openalex.org/C2778559875","wikidata":"https://www.wikidata.org/wiki/Q1892023","display_name":"Map matching","level":3,"score":0.7768509984016418},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7641390562057495},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6470108032226562},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6303830146789551},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5521440505981445},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.5495270490646362},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.526102602481842},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.4928407669067383},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4686305522918701},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4679267406463623},{"id":"https://openalex.org/C13670688","wikidata":"https://www.wikidata.org/wiki/Q3500548","display_name":"Space partitioning","level":2,"score":0.4429129660129547},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.43393415212631226},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.4232946038246155},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.26151084899902344},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.23570933938026428},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15454638004302979},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08255761861801147},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/pimrc.2017.8292591","is_oa":false,"landing_page_url":"https://doi.org/10.1109/pimrc.2017.8292591","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.7200000286102295}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1519770485","https://openalex.org/W1544411027","https://openalex.org/W1606909344","https://openalex.org/W1626398438","https://openalex.org/W1966416504","https://openalex.org/W2049353513","https://openalex.org/W2049626361","https://openalex.org/W2094283130","https://openalex.org/W2135822894","https://openalex.org/W2140251882","https://openalex.org/W2147880780","https://openalex.org/W2150177647","https://openalex.org/W2166771065","https://openalex.org/W2170387980","https://openalex.org/W2172041433","https://openalex.org/W2172133238","https://openalex.org/W2254590392","https://openalex.org/W2548733599","https://openalex.org/W2612776813","https://openalex.org/W6630891747","https://openalex.org/W6632530909","https://openalex.org/W6636500457","https://openalex.org/W6685357131"],"related_works":["https://openalex.org/W2370431274","https://openalex.org/W3010912586","https://openalex.org/W2187159411","https://openalex.org/W3008857645","https://openalex.org/W3037605386","https://openalex.org/W2045922748","https://openalex.org/W2073947114","https://openalex.org/W2061980994","https://openalex.org/W2090617251","https://openalex.org/W4389304954"],"abstract_inverted_index":{"Map":[0],"matching":[1,158],"is":[2,20],"an":[3],"important":[4],"technique":[5],"that":[6,65],"matches":[7],"user":[8],"trajectories":[9,81],"to":[10,53,72,86],"the":[11,69,80,87,105,111,124,129,132,136],"real":[12],"road":[13,73],"networks":[14],"on":[15,140],"a":[16,62,93],"digital":[17],"map.":[18],"It":[19],"crucial":[21],"and":[22,26,41,99,113,127,153],"has":[23],"been":[24],"more":[25,27],"widely":[28],"used":[29],"in":[30,123,155],"various":[31],"fields,":[32],"such":[33],"as":[34,135,160,162],"route":[35],"plan,":[36],"traffic":[37],"forecast,":[38],"location-based":[39],"services":[40],"so":[42],"on.":[43],"However,":[44],"most":[45],"existing":[46],"algorithms":[47],"are":[48],"less":[49],"effective":[50],"when":[51],"applied":[52],"low-sampling-rate":[54,70],"trajectories.":[55],"In":[56,75],"this":[57],"paper,":[58],"we":[59,77,91,118],"propose":[60],"MSTM,":[61,76],"novel":[63],"approach":[64],"can":[66],"effectively":[67],"match":[68],"trajectory":[71,83],"networks.":[74],"first":[78],"partition":[79],"into":[82],"segments":[84],"according":[85],"stay":[88],"points.":[89],"Then":[90],"construct":[92],"map-searching":[94,125],"tree":[95,126],"by":[96,109],"conditional":[97],"extend":[98],"prune":[100],"operations,":[101],"which":[102],"contains":[103],"all":[104],"candidate":[106],"paths.":[107],"Finally,":[108],"considering":[110],"spatial":[112],"temporal":[114],"information":[115],"of":[116,157],"trajectories,":[117],"evaluate":[119],"each":[120],"branch":[121],"path":[122],"choose":[128],"one":[130],"with":[131,146],"highest":[133],"score":[134],"result.":[137],"Extensive":[138],"experiments":[139],"real-world":[141],"datasets":[142],"show":[143],"that,":[144],"compared":[145],"two":[147],"classic":[148],"approaches,":[149],"MSTM":[150],"outperforms":[151],"ST-Matching":[152],"IVMM":[154],"terms":[156],"accuracy":[159],"well":[161],"efficiency.":[163]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}