{"id":"https://openalex.org/W3110050334","doi":"https://doi.org/10.1109/tits.2020.3035708","title":"Geographical Information Enhanced Recognition of Traffic Modes and Behavior Patterns","display_name":"Geographical Information Enhanced Recognition of Traffic Modes and Behavior Patterns","publication_year":2020,"publication_date":"2020-11-25","ids":{"openalex":"https://openalex.org/W3110050334","doi":"https://doi.org/10.1109/tits.2020.3035708","mag":"3110050334"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2020.3035708","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2020.3035708","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-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/A5003949288","display_name":"Jiaqin Wang","orcid":"https://orcid.org/0000-0002-4581-0248"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaqin Wang","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Communications (BUPT), Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4581-0248","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Communications (BUPT), Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056511292","display_name":"Shengchu Wang","orcid":"https://orcid.org/0000-0003-0876-8842"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengchu Wang","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Communications (BUPT), Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-0876-8842","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Communications (BUPT), Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":{"value":2045,"currency":"USD","value_usd":2045},"apc_paid":null,"fwci":0.6059,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.81225033,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"23","issue":"4","first_page":"3777","last_page":"3782"},"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":0.9998000264167786,"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":0.9998000264167786,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9980000257492065,"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"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9957000017166138,"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/gnss-applications","display_name":"GNSS applications","score":0.7637762427330017},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6565948128700256},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.5393625497817993},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4902971088886261},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47279971837997437},{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.46252307295799255},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4492097795009613},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4190623164176941},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.26741620898246765},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.16943204402923584},{"id":"https://openalex.org/keywords/geodesy","display_name":"Geodesy","score":0.11338797211647034},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.10681608319282532}],"concepts":[{"id":"https://openalex.org/C14279187","wikidata":"https://www.wikidata.org/wiki/Q5514012","display_name":"GNSS applications","level":3,"score":0.7637762427330017},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6565948128700256},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.5393625497817993},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4902971088886261},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47279971837997437},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.46252307295799255},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4492097795009613},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4190623164176941},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.26741620898246765},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.16943204402923584},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.11338797211647034},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.10681608319282532},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2020.3035708","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2020.3035708","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2747697957","display_name":null,"funder_award_id":"61901049","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1972916106","https://openalex.org/W2020642377","https://openalex.org/W2022749020","https://openalex.org/W2067303450","https://openalex.org/W2119349310","https://openalex.org/W2136317921","https://openalex.org/W2143394441","https://openalex.org/W2148583092","https://openalex.org/W2743685750","https://openalex.org/W2752546456","https://openalex.org/W2781561575","https://openalex.org/W2790475903","https://openalex.org/W2795599376","https://openalex.org/W2807553909","https://openalex.org/W2911168458","https://openalex.org/W2964344294","https://openalex.org/W6637029157","https://openalex.org/W6682042049"],"related_works":["https://openalex.org/W4285503465","https://openalex.org/W3208266890","https://openalex.org/W2099369243","https://openalex.org/W4223656335","https://openalex.org/W3087543527","https://openalex.org/W2345184372","https://openalex.org/W2187500075","https://openalex.org/W2041399278","https://openalex.org/W3099816427","https://openalex.org/W3193301557"],"abstract_inverted_index":{"This":[0],"correspondence":[1],"discusses":[2],"recognition":[3,182],"of":[4,104,180,190],"traffic":[5,19,44,164],"modes":[6,20,45,165],"and":[7,29,46,81,121,146,152,166,192,194],"behavior":[8,32,47,167],"patterns":[9,33,48],"based":[10,95],"on":[11,96],"Global":[12],"Navigation":[13],"Satellite":[14],"System":[15],"(GNSS)":[16],"data.":[17],"The":[18,134,184],"(e.g.,":[21,34,63],"walk,":[22],"car,":[23],"train,":[24],"etc.)":[25,38],"are":[26,39,49,57,93],"firstly":[27],"inferred,":[28],"then":[30,137],"their":[31,55],"left-turn,":[35],"right-turn,":[36],"turn-around,":[37],"further":[40],"identified.":[41],"Because":[42],"both":[43],"strongly":[50],"influenced":[51],"by":[52,59,128],"geographical":[53,60,91,112,125,174],"circumstances,":[54],"recognitions":[56],"enhanced":[58],"layer":[61],"information":[62,126,175],"building,":[64],"road,":[65],"water,":[66],"etc.).":[67],"At":[68],"one":[69],"specific":[70],"GNSS":[71,150],"point,":[72],"its":[73],"surrounding":[74],"area":[75],"is":[76,119,136],"uniformly":[77],"sliced":[78],"as":[79,123,143],"grids,":[80],"the":[82,105,115,129,173,178,188],"probabilities":[83],"for":[84],"grid":[85],"centers":[86,99],"belonging":[87],"to":[88,162],"six":[89],"different":[90,111],"layers":[92],"calculated":[94],"whether":[97],"these":[98],"lie":[100],"inside":[101],"or":[102],"outside":[103],"minimum":[106],"rectangles":[107],"containing":[108],"polygons":[109],"indicating":[110],"objects.":[113],"Finally,":[114],"six-dimensional":[116],"probability":[117],"matrix":[118],"processed":[120],"compressed":[122],"a":[124,156],"vector":[127],"convolutional":[130],"neural":[131],"network":[132,161],"(CNN).":[133],"latter":[135],"combined":[138],"with":[139],"kinematic":[140],"metrics":[141],"such":[142],"velocity,":[144],"acceleration,":[145],"moving":[147],"direction":[148],"from":[149],"data,":[151],"serially":[153],"input":[154],"into":[155],"long":[157],"short-term":[158],"memory":[159],"(LSTM)":[160],"predict":[163],"patterns.":[168],"Experimental":[169],"results":[170],"validate":[171],"that":[172],"does":[176],"enhance":[177],"performances":[179],"two":[181],"tasks.":[183],"CNN+LSTM":[185],"framework":[186],"retains":[187],"powers":[189],"CNN":[191],"LSTM,":[193],"outperforms":[195],"classical":[196],"machine":[197],"learning":[198],"algorithms.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-08-28T12:50:07.497085","created_date":"2025-10-10T00:00:00"}
