{"id":"https://openalex.org/W4281642483","doi":"https://doi.org/10.5194/agile-giss-3-22-2022","title":"Traffic Regulation Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach","display_name":"Traffic Regulation Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach","publication_year":2022,"publication_date":"2022-06-10","ids":{"openalex":"https://openalex.org/W4281642483","doi":"https://doi.org/10.5194/agile-giss-3-22-2022"},"language":"en","primary_location":{"id":"doi:10.5194/agile-giss-3-22-2022","is_oa":true,"landing_page_url":"https://doi.org/10.5194/agile-giss-3-22-2022","pdf_url":"https://agile-giss.copernicus.org/articles/3/22/2022/agile-giss-3-22-2022.pdf","source":{"id":"https://openalex.org/S4210203054","display_name":"AGILE GIScience Series","issn_l":"2700-8150","issn":["2700-8150"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"AGILE: GIScience Series","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://agile-giss.copernicus.org/articles/3/22/2022/agile-giss-3-22-2022.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064989034","display_name":"Stefania Zourlidou","orcid":"https://orcid.org/0000-0003-1759-7379"},"institutions":[{"id":"https://openalex.org/I114112103","display_name":"Leibniz University Hannover","ror":"https://ror.org/0304hq317","country_code":"DE","type":"education","lineage":["https://openalex.org/I114112103"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Stefania Zourlidou","raw_affiliation_strings":["Institute of Cartography and Geoinformatics, Leibniz University, Hannover, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Cartography and Geoinformatics, Leibniz University, Hannover, Germany","institution_ids":["https://openalex.org/I114112103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079335135","display_name":"Jens Golze","orcid":"https://orcid.org/0000-0001-7355-351X"},"institutions":[{"id":"https://openalex.org/I114112103","display_name":"Leibniz University Hannover","ror":"https://ror.org/0304hq317","country_code":"DE","type":"education","lineage":["https://openalex.org/I114112103"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jens Golze","raw_affiliation_strings":["Institute of Cartography and Geoinformatics, Leibniz University, Hannover, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Cartography and Geoinformatics, Leibniz University, Hannover, Germany","institution_ids":["https://openalex.org/I114112103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020817045","display_name":"Monika Sester","orcid":"https://orcid.org/0000-0002-6656-8809"},"institutions":[{"id":"https://openalex.org/I114112103","display_name":"Leibniz University Hannover","ror":"https://ror.org/0304hq317","country_code":"DE","type":"education","lineage":["https://openalex.org/I114112103"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Monika Sester","raw_affiliation_strings":["Institute of Cartography and Geoinformatics, Leibniz University, Hannover, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Cartography and Geoinformatics, Leibniz University, Hannover, Germany","institution_ids":["https://openalex.org/I114112103"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5064989034"],"corresponding_institution_ids":["https://openalex.org/I114112103"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06320078,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"3","issue":null,"first_page":"1","last_page":"19"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9987999796867371,"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"}},"topics":[{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9987999796867371,"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":0.9979000091552734,"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":0.979200005531311,"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/global-positioning-system","display_name":"Global Positioning System","score":0.7170042991638184},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7121219038963318},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6059707403182983},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.5777910947799683},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.5296183228492737},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.4583356976509094},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38818126916885376},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3829997181892395},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3821520209312439},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.25358039140701294},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.17704632878303528},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.15157300233840942},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.11332014203071594},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.08462861180305481}],"concepts":[{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.7170042991638184},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7121219038963318},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6059707403182983},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.5777910947799683},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.5296183228492737},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.4583356976509094},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38818126916885376},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3829997181892395},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3821520209312439},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.25358039140701294},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.17704632878303528},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.15157300233840942},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.11332014203071594},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.08462861180305481}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5194/agile-giss-3-22-2022","is_oa":true,"landing_page_url":"https://doi.org/10.5194/agile-giss-3-22-2022","pdf_url":"https://agile-giss.copernicus.org/articles/3/22/2022/agile-giss-3-22-2022.pdf","source":{"id":"https://openalex.org/S4210203054","display_name":"AGILE GIScience Series","issn_l":"2700-8150","issn":["2700-8150"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"AGILE: GIScience Series","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.5194/agile-giss-3-22-2022","is_oa":true,"landing_page_url":"https://doi.org/10.5194/agile-giss-3-22-2022","pdf_url":"https://agile-giss.copernicus.org/articles/3/22/2022/agile-giss-3-22-2022.pdf","source":{"id":"https://openalex.org/S4210203054","display_name":"AGILE GIScience Series","issn_l":"2700-8150","issn":["2700-8150"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"AGILE: GIScience Series","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3006041050","display_name":null,"funder_award_id":"GRK1931","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G3564768260","display_name":null,"funder_award_id":"9/GRK","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G5106512922","display_name":null,"funder_award_id":"Deutsche Forschungsgemeinschaft (DFG","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G6024419964","display_name":null,"funder_award_id":"Deutsche Forschungsgemeinschaft (DFG)","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G6052429835","display_name":null,"funder_award_id":"(DFG)","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G6549727561","display_name":null,"funder_award_id":"227198829","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G6937053344","display_name":null,"funder_award_id":"227198829/GRK1931","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"}],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4281642483.pdf","grobid_xml":"https://content.openalex.org/works/W4281642483.grobid-xml"},"referenced_works_count":18,"referenced_works":["https://openalex.org/W2019858433","https://openalex.org/W2033969525","https://openalex.org/W2073061255","https://openalex.org/W2124029430","https://openalex.org/W2809783195","https://openalex.org/W2889853576","https://openalex.org/W2972403824","https://openalex.org/W2982675960","https://openalex.org/W3026789654","https://openalex.org/W3097495256","https://openalex.org/W3117909419","https://openalex.org/W3118927687","https://openalex.org/W3138657285","https://openalex.org/W3203984526","https://openalex.org/W3205531670","https://openalex.org/W3211927032","https://openalex.org/W6600204758","https://openalex.org/W6964397296"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W4313488044","https://openalex.org/W2348178913","https://openalex.org/W2806221860","https://openalex.org/W4384274202","https://openalex.org/W4367597884","https://openalex.org/W2999796123","https://openalex.org/W4319869621","https://openalex.org/W3173853490","https://openalex.org/W2164267950"],"abstract_inverted_index":{"Abstract.":[0],"This":[1],"article":[2],"presents":[3],"a":[4,73,152],"method":[5,108],"for":[6,199],"traffic":[7,47,52,175],"control":[8],"recognition":[9],"at":[10,48,60,72,87],"junctions":[11],"(traffic":[12],"lights,":[13],"stop,":[14],"priority":[15],"and":[16,54,68,127,136,157,183,193],"right":[17],"of":[18,76,111,116,154],"way":[19,44],"rule)":[20],"using":[21,139],"crowd-sensed":[22],"GPS":[23],"data":[24,148],"(vehicle":[25],"trajectories),":[26],"as":[27,29],"well":[28],"features":[30,143,159,172],"extracted":[31],"from":[32,122,149],"OpenStreetMap.":[33],"Traffic":[34],"regulators":[35,99],"are":[36,100,205],"not":[37],"mapped":[38],"in":[39,181,185],"most":[40],"maps,":[41],"although":[42],"the":[43,51,56,62,65,69,77,106,155,166,188,196],"they":[45],"regulate":[46],"intersections":[49],"affects":[50],"flow":[53],"therefore":[55],"vehicle":[57],"idle":[58],"time":[59,71],"intersections,":[61,88],"fuel":[63],"consumption,":[64],"CO2":[66],"emissions,":[67],"arrival":[70],"destination.":[74],"Because":[75],"controlled":[78],"interaction":[79],"that":[80,165],"road":[81],"users":[82],"have":[83],"with":[84,170,177],"each":[85],"other":[86,189],"driving":[89],"safety":[90],"or":[91,145,151],"assistance":[92],"applications":[93],"can":[94,173],"be":[95],"enabled":[96],"if":[97],"intersection":[98],"mapped.":[101],"In":[102],"order":[103],"to":[104],"verify":[105],"proposed":[107],"two":[109,123],"sets":[110],"trajectories":[112],"were":[113,132],"used,":[114],"one":[115],"which":[117],"is":[118],"an":[119],"open":[120],"dataset,":[121],"different":[124],"cities,":[125],"Hannover":[126],"Chicago.":[128],"Two":[129],"classification":[130,169],"methods":[131],"tested,":[133],"random":[134],"forest":[135],"gradient":[137,167],"boosting,":[138],"exclusively":[140],"either":[141],"dynamic":[142,156],"(trajectories),":[144],"static":[146,158],"(only":[147],"OSM)":[150],"combination":[153],"(hybrid":[160],"model).":[161],"The":[162],"results":[163],"show":[164],"boosting":[168],"hybrid":[171],"predict":[174],"regulations":[176],"high":[178],"accuracy":[179],"(93%":[180],"Chicago":[182],"94%":[184],"Hannover),":[186],"outperforming":[187],"detection":[190],"models":[191],"(static":[192],"dynamic).":[194],"At":[195],"end":[197],"directions":[198],"further":[200],"research":[201],"on":[202],"this":[203],"topic":[204],"proposed.":[206]},"counts_by_year":[],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
