{"id":"https://openalex.org/W3208149186","doi":"https://doi.org/10.1109/itsc48978.2021.9565040","title":"Modeling spatio-temporal interactions for vehicle trajectory prediction based on graph representation learning","display_name":"Modeling spatio-temporal interactions for vehicle trajectory prediction based on graph representation learning","publication_year":2021,"publication_date":"2021-09-19","ids":{"openalex":"https://openalex.org/W3208149186","doi":"https://doi.org/10.1109/itsc48978.2021.9565040","mag":"3208149186"},"language":"en","primary_location":{"id":"doi:10.1109/itsc48978.2021.9565040","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc48978.2021.9565040","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Intelligent Transportation Systems Conference (ITSC)","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/A5076859384","display_name":"Ziyan Gao","orcid":"https://orcid.org/0000-0001-9948-7960"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ziyan Gao","raw_affiliation_strings":["National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chendu, China"],"affiliations":[{"raw_affiliation_string":"National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chendu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080893470","display_name":"Zhanbo Sun","orcid":"https://orcid.org/0000-0001-9617-7676"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhanbo Sun","raw_affiliation_strings":["National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chendu, China"],"affiliations":[{"raw_affiliation_string":"National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chendu, China","institution_ids":["https://openalex.org/I4800084"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5076859384"],"corresponding_institution_ids":["https://openalex.org/I4800084"],"apc_list":null,"apc_paid":null,"fwci":0.5499,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.66169779,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1334","last_page":"1339"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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":0.9948999881744385,"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/T10370","display_name":"Traffic and Road Safety","score":0.9926999807357788,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/interpretability","display_name":"Interpretability","score":0.8593637943267822},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7571964859962463},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6136386394500732},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6115990281105042},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5626081228256226},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5505452752113342},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5221767425537109},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5022072792053223},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.46914568543434143},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4623682498931885},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3595927357673645},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2217077612876892},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10900893807411194}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8593637943267822},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7571964859962463},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6136386394500732},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6115990281105042},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5626081228256226},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5505452752113342},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5221767425537109},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5022072792053223},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.46914568543434143},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4623682498931885},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3595927357673645},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2217077612876892},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10900893807411194},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc48978.2021.9565040","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc48978.2021.9565040","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Intelligent Transportation Systems Conference (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.7599999904632568,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1967371255","https://openalex.org/W2012849708","https://openalex.org/W2019550475","https://openalex.org/W2055556996","https://openalex.org/W2084835622","https://openalex.org/W2123236823","https://openalex.org/W2153219011","https://openalex.org/W2153323685","https://openalex.org/W2766836212","https://openalex.org/W2774231091","https://openalex.org/W2775492430","https://openalex.org/W2803184913","https://openalex.org/W2912477994","https://openalex.org/W2963914175","https://openalex.org/W2996988148","https://openalex.org/W3006096899","https://openalex.org/W3105115779"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W2888392564","https://openalex.org/W4310278675","https://openalex.org/W4388422664","https://openalex.org/W4390569940","https://openalex.org/W4361193272","https://openalex.org/W2963326959","https://openalex.org/W4388685194","https://openalex.org/W4312407344","https://openalex.org/W2894289927"],"abstract_inverted_index":{"Accurately":[0],"predicting":[1],"vehicle":[2],"trajectories":[3],"is":[4,36,50],"essential":[5],"for":[6,52],"safe":[7],"and":[8,44,88,101,119,126],"efficient":[9],"operations":[10],"of":[11,104],"autonomous":[12],"driving":[13],"cars.":[14],"In":[15,107],"this":[16,62],"paper,":[17],"we":[18,146],"propose":[19],"a":[20,29,45],"long":[21],"short-term":[22],"memory":[23],"(LSTM)":[24],"encoder-decoder":[25],"model":[26,73],"along":[27],"with":[28,40],"graph":[30,46],"representation":[31,47,63],"learning":[32,48,54,64],"module,":[33],"where":[34],"LSTM":[35],"adopted":[37],"to":[38,81,124,157],"deal":[39],"temporal":[41],"sequence":[42],"features":[43],"module":[49,65],"used":[51,123],"precisely":[53],"the":[55,71,76,82,97,102,105,108,111,142,148],"spatial":[56],"interactions":[57],"between":[58],"vehicles.":[59],"By":[60],"leveraging":[61],"based":[66],"on":[67],"an":[68],"attention":[69,77],"aggregator,":[70],"developed":[72],"can":[74],"learn":[75],"paid":[78],"by":[79,154],"drivers":[80],"vehicles":[83],"in":[84],"their":[85],"local":[86],"neighborhood":[87],"aggregate":[89],"forward":[90],"traffic":[91],"flow":[92],"information,":[93],"thereby":[94],"significantly":[95],"improving":[96],"long-term":[98],"prediction":[99],"accuracy":[100],"interpretability":[103],"model.":[106,129],"conducted":[109],"experiments,":[110],"publicly":[112],"available":[113],"Next":[114],"Generation":[115],"Simulation":[116],"(NGSIM)":[117],"US-101":[118],"I\u201380":[120],"datasets":[121],"are":[122],"train":[125],"evaluate":[127],"our":[128,134],"The":[130],"results":[131],"indicate":[132],"that":[133],"spatial-temporal":[135],"framework":[136],"achieves":[137],"noticeably":[138],"better":[139],"performance":[140],"than":[141],"state-of-the-art":[143],"approaches.":[144],"Specifically,":[145],"reduce":[147],"root":[149],"mean":[150],"square":[151],"error":[152],"(RMSE)":[153],"17%":[155],"according":[156],"quantitative":[158],"evaluation.":[159]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
