{"id":"https://openalex.org/W4412877039","doi":"https://doi.org/10.1145/3711896.3736961","title":"Fine-Grained Traffic Inference from Road to Lane via Spatio-Temporal Graph Node Generation","display_name":"Fine-Grained Traffic Inference from Road to Lane via Spatio-Temporal Graph Node Generation","publication_year":2025,"publication_date":"2025-08-03","ids":{"openalex":"https://openalex.org/W4412877039","doi":"https://doi.org/10.1145/3711896.3736961"},"language":"en","primary_location":{"id":"doi:10.1145/3711896.3736961","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3736961","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3736961","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3736961","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100706220","display_name":"Shuhao Li","orcid":"https://orcid.org/0009-0008-5175-7667"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuhao Li","raw_affiliation_strings":["Fudan University, Shanghai, China and Shanghai Key Laboratory of Data Science, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0008-5175-7667","affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China and Shanghai Key Laboratory of Data Science, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101865874","display_name":"Weidong Yang","orcid":"https://orcid.org/0000-0002-6473-9272"},"institutions":[{"id":"https://openalex.org/I4210110560","display_name":"Zhuhai Fudan Innovation Research Institute","ror":"https://ror.org/01tapk317","country_code":"CN","type":"facility","lineage":["https://openalex.org/I24943067","https://openalex.org/I4210110560"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weidong Yang","raw_affiliation_strings":["Fudan University, Shanghai, China and Zhuhai Fudan Innovation Research Institute, Zhuhai, China"],"raw_orcid":"https://orcid.org/0000-0002-6473-9272","affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China and Zhuhai Fudan Innovation Research Institute, Zhuhai, China","institution_ids":["https://openalex.org/I4210110560"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055065511","display_name":"Yue Cui","orcid":"https://orcid.org/0000-0002-1656-5407"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Yue Cui","raw_affiliation_strings":["The Hong Kong University of Science and Technology, Hong Kong SAR, China"],"raw_orcid":"https://orcid.org/0000-0002-1656-5407","affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology, Hong Kong SAR, China","institution_ids":["https://openalex.org/I200769079"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xiaoxing Liu","orcid":"https://orcid.org/0009-0006-6496-0803"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoxing Liu","raw_affiliation_strings":["GZHU-SCHB Intelligent Transportation Joint Lab, Guangzhou University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0009-0006-6496-0803","affiliations":[{"raw_affiliation_string":"GZHU-SCHB Intelligent Transportation Joint Lab, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070489274","display_name":"Lingkai Meng","orcid":"https://orcid.org/0009-0002-7961-9131"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingkai Meng","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0002-7961-9131","affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039580875","display_name":"Lipeng Ma","orcid":"https://orcid.org/0000-0001-5974-5988"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lipeng Ma","raw_affiliation_strings":["Fudan University, Shanghai, China and Shanghai Key Laboratory of Data Science, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-5974-5988","affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China and Shanghai Key Laboratory of Data Science, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100403537","display_name":"Fan Zhang","orcid":"https://orcid.org/0000-0003-0548-0130"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Zhang","raw_affiliation_strings":["GZHU-SCHB Intelligent Transportation Joint Lab, Guangzhou University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-0548-0130","affiliations":[{"raw_affiliation_string":"GZHU-SCHB Intelligent Transportation Joint Lab, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7368,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.7241303,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1529","last_page":"1540"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9997000098228455,"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":0.9997000098228455,"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/T10799","display_name":"Data Visualization and Analytics","score":0.9878000020980835,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9746000170707703,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7513127326965332},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6925771832466125},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.502507209777832},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4650510549545288},{"id":"https://openalex.org/keywords/road-traffic","display_name":"Road traffic","score":0.42051222920417786},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.28031790256500244},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.23830202221870422},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.18480011820793152},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08955883979797363}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7513127326965332},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6925771832466125},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.502507209777832},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4650510549545288},{"id":"https://openalex.org/C2985695025","wikidata":"https://www.wikidata.org/wiki/Q4323994","display_name":"Road traffic","level":2,"score":0.42051222920417786},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28031790256500244},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.23830202221870422},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.18480011820793152},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08955883979797363},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3711896.3736961","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3736961","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3736961","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2507.19089","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.19089","pdf_url":"https://arxiv.org/pdf/2507.19089","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3711896.3736961","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3736961","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3736961","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.5699999928474426}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321851","display_name":"Fudan University","ror":"https://ror.org/013q1eq08"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412877039.pdf","grobid_xml":"https://content.openalex.org/works/W4412877039.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W569478347","https://openalex.org/W1600175910","https://openalex.org/W2903871660","https://openalex.org/W2912407321","https://openalex.org/W2996847713","https://openalex.org/W2997848713","https://openalex.org/W3034388290","https://openalex.org/W3036527662","https://openalex.org/W3069141980","https://openalex.org/W3099414221","https://openalex.org/W3102632991","https://openalex.org/W3112830724","https://openalex.org/W3135728061","https://openalex.org/W3161675142","https://openalex.org/W3170140111","https://openalex.org/W3177318507","https://openalex.org/W3216356365","https://openalex.org/W4214809422","https://openalex.org/W4283739673","https://openalex.org/W4285357695","https://openalex.org/W4292973033","https://openalex.org/W4382203079","https://openalex.org/W4382240063","https://openalex.org/W4382449675","https://openalex.org/W4383747990","https://openalex.org/W4390100390","https://openalex.org/W4395075604","https://openalex.org/W4411088760","https://openalex.org/W6600297362","https://openalex.org/W6809521257"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Fine-grained":[0,58],"traffic":[1,18,24,71,86,142],"management":[2],"and":[3,17,40,44,81,119,131,161,188],"prediction":[4],"are":[5,190],"fundamental":[6],"to":[7,35,66,124,137,156,172],"key":[8],"applications":[9],"such":[10],"as":[11,92],"autonomous":[12],"driving,":[13],"lane":[14,141],"change":[15],"guidance,":[16],"signal":[19],"control.":[20],"However,":[21],"obtaining":[22],"lane-level":[23,70],"data":[25,136],"has":[26],"become":[27],"a":[28,78,105],"critical":[29],"bottleneck":[30],"for":[31,84],"data-driven":[32],"models":[33,152],"due":[34],"limitations":[36],"in":[37,180],"the":[38,47,57,93,97,109,115,120,127,154,158,174,177,182],"types":[39],"number":[41],"of":[42,49,96,134,176],"sensors":[43],"issues":[45],"with":[46,153],"accuracy":[48],"tracking":[50],"algorithms.":[51],"To":[52],"address":[53],"this,":[54],"we":[55,148],"propose":[56],"Road":[59],"Traffic":[60],"Inference":[61],"(FRTI)":[62],"task,":[63],"which":[64],"aims":[65],"generate":[67],"more":[68,79],"detailed":[69],"information":[72],"using":[73],"limited":[74,128],"road":[75,135,170],"data,":[76],"providing":[77],"energy-efficient":[80],"cost-effective":[82],"solution":[83],"precise":[85],"management.":[87],"This":[88,112],"task":[89,160],"is":[90],"abstracted":[91],"first":[94],"scene":[95],"spatio-temporal":[98,129],"graph":[99],"node":[100],"generation":[101],"problem.":[102],"We":[103],"designed":[104,149],"two-stage":[106],"framework-RoadDiff-to":[107],"solve":[108,157],"FRTI":[110,159,183],"task.":[111,184],"framework":[113],"leverages":[114],"Road-Lane":[116],"Correlation":[117],"Autoencoder-Decoder":[118],"Lane":[121],"Diffusion":[122],"Module":[123],"fully":[125],"utilize":[126],"dependencies":[130],"distribution":[132],"relationships":[133],"accurately":[138],"infer":[139],"fine-grained":[140],"states.":[143],"Based":[144],"on":[145,165],"existing":[146],"research,":[147],"several":[150],"baseline":[151],"potential":[155],"conducted":[162],"extensive":[163],"experiments":[164],"six":[166],"datasets":[167,187],"representing":[168],"different":[169],"conditions":[171],"validate":[173],"effectiveness":[175],"RoadDiff":[178],"model":[179],"addressing":[181],"The":[185],"relevant":[186],"code":[189],"available":[191],"at":[192],"https://github.com/ShuhaoLii/RoadDiff.":[193]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}