{"id":"https://openalex.org/W4285066205","doi":"https://doi.org/10.1109/tvt.2022.3185249","title":"Deep Learning Enabled Fine-Grained Path Planning for Connected Vehicular Networks","display_name":"Deep Learning Enabled Fine-Grained Path Planning for Connected Vehicular Networks","publication_year":2022,"publication_date":"2022-06-22","ids":{"openalex":"https://openalex.org/W4285066205","doi":"https://doi.org/10.1109/tvt.2022.3185249"},"language":"en","primary_location":{"id":"doi:10.1109/tvt.2022.3185249","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2022.3185249","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"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 Vehicular Technology","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/A5030714813","display_name":"Hanlin Wu","orcid":"https://orcid.org/0000-0002-9640-9315"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanlin Wu","raw_affiliation_strings":["School of Electronic Science and Engineering, Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-9640-9315","affiliations":[{"raw_affiliation_string":"School of Electronic Science and Engineering, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043286765","display_name":"Haibo Zhou","orcid":"https://orcid.org/0000-0002-6549-8917"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haibo Zhou","raw_affiliation_strings":["School of Electronic Science and Engineering, Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-6549-8917","affiliations":[{"raw_affiliation_string":"School of Electronic Science and Engineering, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076527812","display_name":"Jiwei Zhao","orcid":"https://orcid.org/0000-0002-5806-992X"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiwei Zhao","raw_affiliation_strings":["School of Electronic Science and Engineering, Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-5806-992X","affiliations":[{"raw_affiliation_string":"School of Electronic Science and Engineering, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073847062","display_name":"Yunting Xu","orcid":"https://orcid.org/0000-0001-6341-4698"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunting Xu","raw_affiliation_strings":["School of Electronic Science and Engineering, Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-6341-4698","affiliations":[{"raw_affiliation_string":"School of Electronic Science and Engineering, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071419774","display_name":"Bo Qian","orcid":"https://orcid.org/0000-0002-6964-220X"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Qian","raw_affiliation_strings":["School of Electronic Science and Engineering, Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-6964-220X","affiliations":[{"raw_affiliation_string":"School of Electronic Science and Engineering, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100773343","display_name":"Xuemin Shen","orcid":"https://orcid.org/0000-0002-4140-287X"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Xuemin Shen","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada"],"raw_orcid":"https://orcid.org/0000-0002-4140-287X","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6501,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.81375024,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"71","issue":"10","first_page":"10303","last_page":"10315"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":1.0,"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":1.0,"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/T10524","display_name":"Traffic control and management","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/computer-science","display_name":"Computer science","score":0.7444092631340027},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5846593379974365},{"id":"https://openalex.org/keywords/beijing","display_name":"Beijing","score":0.5733320116996765},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.5370064973831177},{"id":"https://openalex.org/keywords/floating-car-data","display_name":"Floating car data","score":0.530188262462616},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.5102143287658691},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4951724112033844},{"id":"https://openalex.org/keywords/traffic-congestion-reconstruction-with-kerners-three-phase-theory","display_name":"Traffic congestion reconstruction with Kerner's three-phase theory","score":0.47075632214546204},{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.45986223220825195},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4122660160064697},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3982766270637512},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3613482713699341},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.3125962018966675},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.29150038957595825},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.24155491590499878},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14010605216026306},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07951933145523071}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7444092631340027},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5846593379974365},{"id":"https://openalex.org/C2778304055","wikidata":"https://www.wikidata.org/wiki/Q657474","display_name":"Beijing","level":3,"score":0.5733320116996765},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.5370064973831177},{"id":"https://openalex.org/C64093975","wikidata":"https://www.wikidata.org/wiki/Q356677","display_name":"Floating car data","level":3,"score":0.530188262462616},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.5102143287658691},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4951724112033844},{"id":"https://openalex.org/C25492975","wikidata":"https://www.wikidata.org/wiki/Q960570","display_name":"Traffic congestion reconstruction with Kerner's three-phase theory","level":3,"score":0.47075632214546204},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.45986223220825195},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4122660160064697},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3982766270637512},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3613482713699341},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.3125962018966675},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29150038957595825},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.24155491590499878},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14010605216026306},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07951933145523071},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C191935318","wikidata":"https://www.wikidata.org/wiki/Q148","display_name":"China","level":2,"score":0.0},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","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/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tvt.2022.3185249","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2022.3185249","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"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 Vehicular Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8299999833106995,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G8781480831","display_name":"\u4e92\u534f\u65b9\u5dee\u672a\u77e5\u60c5\u5f62\u4e0b\u7684\u5206\u5e03\u5f0f\u878d\u5408\u7b56\u7565\u53ca\u6c42\u89e3\u65b9\u6cd5\u7814\u7a76","funder_award_id":"61871221","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"},{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W800432607","https://openalex.org/W1875626450","https://openalex.org/W1895577753","https://openalex.org/W1969483458","https://openalex.org/W1971993044","https://openalex.org/W1977153226","https://openalex.org/W1989648672","https://openalex.org/W2007322170","https://openalex.org/W2025745283","https://openalex.org/W2092923999","https://openalex.org/W2112796928","https://openalex.org/W2115459592","https://openalex.org/W2135410644","https://openalex.org/W2138589386","https://openalex.org/W2169528473","https://openalex.org/W2194775991","https://openalex.org/W2265339577","https://openalex.org/W2269942773","https://openalex.org/W2293708710","https://openalex.org/W2401468202","https://openalex.org/W2415153062","https://openalex.org/W2418228449","https://openalex.org/W2509469073","https://openalex.org/W2528639018","https://openalex.org/W2557171754","https://openalex.org/W2579495707","https://openalex.org/W2605945764","https://openalex.org/W2793054085","https://openalex.org/W2808871417","https://openalex.org/W2918797707","https://openalex.org/W2945622688","https://openalex.org/W2977191322","https://openalex.org/W2993809815","https://openalex.org/W3001774639","https://openalex.org/W3014197885","https://openalex.org/W3017027405","https://openalex.org/W6729823683"],"related_works":["https://openalex.org/W2972320057","https://openalex.org/W4386289889","https://openalex.org/W3117279048","https://openalex.org/W2945875309","https://openalex.org/W2898775471","https://openalex.org/W4391811515","https://openalex.org/W4389949262","https://openalex.org/W2565115916","https://openalex.org/W1669406372","https://openalex.org/W4286209918"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"to":[3,54,147],"alleviate":[4],"the":[5,33,56,98,122,133,138,149,154,167,179],"ever-increasing":[6],"traffic":[7,19,49,65,71,99,124,156,175,190],"congestion":[8,176],"in":[9,126],"urban":[10,174],"areas":[11],"by":[12,94,120],"accommodating":[13],"higher":[14],"road":[15,79],"traffic,":[16],"we":[17,87,131],"develop":[18],"prediction":[20,50,66,72,100,157],"framework":[21,67,158],"together":[22],"with":[23,80],"path":[24,91,109,113,160],"planning":[25,92,161],"method":[26,93],"for":[27,188],"connected":[28],"vehicular":[29,60],"networks.":[30],"First,":[31],"through":[32],"employment":[34],"of":[35,59,73,97,104,108,140,153,172],"convolutional":[36],"neural":[37],"network":[38],"(CNN)":[39],"and":[40,84,111,142,151,159],"residual":[41],"unit":[42],"(RN),":[43],"deep":[44],"learning":[45],"(DL)":[46],"based":[47,177],"fine-grained":[48,64],"algorithm":[51],"is":[52,170],"designed":[53],"obtain":[55],"spatial-temporal":[57],"characteristics":[58],"traffic.":[61],"The":[62,102],"regionally":[63],"can":[68,184],"realize":[69],"real-time":[70],"future":[74],"changing":[75],"trends":[76],"at":[77],"each":[78],"a":[81,89],"high":[82],"accuracy":[83,103],"reliability.":[85],"Second,":[86],"propose":[88],"gridded":[90,128],"making":[95],"use":[96],"information.":[101],"selected":[105],"path,":[106],"complexity":[107],"calculation,":[110],"adaptive":[112],"adjustment":[114],"are":[115],"jointly":[116],"taken":[117],"into":[118],"consideration":[119],"achieving":[121],"refined":[123],"regulation":[125],"different":[127],"section.":[129],"Finally,":[130],"utilize":[132],"actual":[134],"vehicle":[135],"data":[136],"from":[137],"city":[139],"Beijing":[141],"digital":[143],"map":[144],"on":[145,178],"OpenStreetMap":[146],"validate":[148],"effectiveness":[150],"reliability":[152],"proposed":[155,168],"method.":[162],"Simulation":[163],"results":[164],"demonstrate":[165],"that":[166],"approach":[169],"capable":[171],"relieving":[173],"existing":[180],"roadway":[181],"systems,":[182],"which":[183],"provide":[185],"methodological":[186],"guidance":[187],"data-intensive":[189],"management.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}