{"id":"https://openalex.org/W4312814001","doi":"https://doi.org/10.1109/tkde.2022.3221183","title":"Traffic Flow Prediction Based on Spatiotemporal Potential Energy Fields","display_name":"Traffic Flow Prediction Based on Spatiotemporal Potential Energy Fields","publication_year":2022,"publication_date":"2022-11-10","ids":{"openalex":"https://openalex.org/W4312814001","doi":"https://doi.org/10.1109/tkde.2022.3221183"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2022.3221183","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2022.3221183","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Knowledge and Data Engineering","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/A5100400846","display_name":"Jingyuan Wang","orcid":"https://orcid.org/0000-0003-0651-1592"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]},{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jingyuan Wang","raw_affiliation_strings":["School of Computer Science and Engineering, School of Economics and Management, Beihang University, Beijing, China","Pengcheng Laboratory, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0003-0651-1592","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, School of Economics and Management, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]},{"raw_affiliation_string":"Pengcheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023627152","display_name":"Jiahao Ji","orcid":"https://orcid.org/0000-0003-3029-2262"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiahao Ji","raw_affiliation_strings":["School of Computer Science and Engineering, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-3029-2262","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021681759","display_name":"Zhe Jiang","orcid":"https://orcid.org/0000-0002-3576-6976"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhe Jiang","raw_affiliation_strings":["Department of Computer &amp; Information Science &amp; Engineering, University of Florida, Gainesville, FL, USA"],"raw_orcid":"https://orcid.org/0000-0002-3576-6976","affiliations":[{"raw_affiliation_string":"Department of Computer &amp; Information Science &amp; Engineering, University of Florida, Gainesville, FL, USA","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081275566","display_name":"Leilei Sun","orcid":"https://orcid.org/0000-0002-0157-1716"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Leilei Sun","raw_affiliation_strings":["State Key Laboratory of Software Development Environment (SKLSDE), Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-0157-1716","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Software Development Environment (SKLSDE), Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100400846"],"corresponding_institution_ids":["https://openalex.org/I4210136793","https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":7.0201,"has_fulltext":false,"cited_by_count":67,"citation_normalized_percentile":{"value":0.98118964,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"35","issue":"9","first_page":"9073","last_page":"9087"},"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9986000061035156,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.996999979019165,"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/computer-science","display_name":"Computer science","score":0.7039791345596313},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.47722628712654114},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4717077910900116},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.47065845131874084},{"id":"https://openalex.org/keywords/autocorrelation","display_name":"Autocorrelation","score":0.46950483322143555},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.46815064549446106},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.46270516514778137},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.45548146963119507},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.43566399812698364},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40433400869369507},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.15452221035957336},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1519961953163147}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7039791345596313},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.47722628712654114},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4717077910900116},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.47065845131874084},{"id":"https://openalex.org/C5297727","wikidata":"https://www.wikidata.org/wiki/Q786970","display_name":"Autocorrelation","level":2,"score":0.46950483322143555},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.46815064549446106},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.46270516514778137},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.45548146963119507},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.43566399812698364},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40433400869369507},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.15452221035957336},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1519961953163147},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tkde.2022.3221183","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2022.3221183","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Knowledge and Data Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.7400000095367432,"id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G1604724560","display_name":null,"funder_award_id":"82161148011","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2363765612","display_name":null,"funder_award_id":"72171013","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7502749798","display_name":null,"funder_award_id":"62272023","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8651611484","display_name":null,"funder_award_id":"72222022","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":55,"referenced_works":["https://openalex.org/W1965680834","https://openalex.org/W2017377889","https://openalex.org/W2025546535","https://openalex.org/W2042706982","https://openalex.org/W2055195882","https://openalex.org/W2090978188","https://openalex.org/W2095293504","https://openalex.org/W2131739422","https://openalex.org/W2318951588","https://openalex.org/W2434631083","https://openalex.org/W2515292392","https://openalex.org/W2530386080","https://openalex.org/W2533328922","https://openalex.org/W2565239705","https://openalex.org/W2583466634","https://openalex.org/W2605281927","https://openalex.org/W2739060064","https://openalex.org/W2744444739","https://openalex.org/W2756203131","https://openalex.org/W2760942449","https://openalex.org/W2809086059","https://openalex.org/W2910892140","https://openalex.org/W2942843559","https://openalex.org/W2949685089","https://openalex.org/W2950635152","https://openalex.org/W2952785130","https://openalex.org/W2962790412","https://openalex.org/W2962996477","https://openalex.org/W2964053796","https://openalex.org/W2964319113","https://openalex.org/W2965341826","https://openalex.org/W2970090621","https://openalex.org/W2973201950","https://openalex.org/W2983448484","https://openalex.org/W3036384654","https://openalex.org/W3080422828","https://openalex.org/W3102730661","https://openalex.org/W3103720336","https://openalex.org/W3107462623","https://openalex.org/W3108550173","https://openalex.org/W3122912982","https://openalex.org/W3127180809","https://openalex.org/W3135694489","https://openalex.org/W3152249529","https://openalex.org/W3174022889","https://openalex.org/W3213170289","https://openalex.org/W3214685769","https://openalex.org/W3217450528","https://openalex.org/W4283819096","https://openalex.org/W4290945861","https://openalex.org/W6639611172","https://openalex.org/W6679436768","https://openalex.org/W6745537798","https://openalex.org/W6747962424","https://openalex.org/W6780221082"],"related_works":["https://openalex.org/W747394405","https://openalex.org/W1987236514","https://openalex.org/W174653542","https://openalex.org/W1975632186","https://openalex.org/W2769759987","https://openalex.org/W122018179","https://openalex.org/W2058740861","https://openalex.org/W2530088135","https://openalex.org/W2370106417","https://openalex.org/W2383138325"],"abstract_inverted_index":{"Traffic":[0],"flow":[1,110,156,242],"prediction":[2,238],"is":[3,133],"a":[4,82,87,142,164,185,192,202],"fundamental":[5],"problem":[6],"in":[7,235,285],"spatiotemporal":[8,48,165,253],"data":[9],"mining.":[10],"Most":[11],"of":[12,38,67,81,173,184,217,240,293],"the":[13,35,47,56,64,72,92,98,107,136,148,159,171,223,231,252,273,282,290,298],"existing":[14],"studies":[15,279],"focuses":[16],"on":[17,261],"designing":[18],"statistical":[19],"models":[20],"to":[21,33,62,105,126,145,169,208,229],"fit":[22],"historical":[23],"traffic":[24,109,120,155,241,264],"data,":[25],"which":[26,54,182],"are":[27,243,249],"purely":[28],"data-driven":[29,75,88,160],"approaches":[30],"and":[31,70,86,191,204,213],"fail":[32],"reveal":[34,289],"underlying":[36,65,291],"mechanisms":[37,66,292],"urban":[39,68,294],"traffic.":[40],"To":[41],"address":[42],"this":[43],"issue,":[44],"we":[45],"propose":[46],"potential":[49,115,124,128],"energy":[50,116],"field":[51,57,93],"model":[52,168,176,230,271,299],"(ST-PEF+),":[53],"applies":[55],"theory":[58,73,94],"for":[59,95,188,195],"human":[60,96],"mobility":[61],"interpret":[63],"traffic,":[69,295],"introduces":[71],"into":[74,112],"deep":[76,166,254],"learning":[77,167,255],"models.":[78],"ST-PEF+":[79,162],"consists":[80,183],"PEF":[83,99],"extraction":[84,100],"module":[85,101],"module.":[89],"Inspired":[90],"by":[91,135,251],"mobility,":[97],"adopts":[102,177],"an":[103],"algorithm":[104,151],"decompose":[106,153],"grid-based":[108],"graph":[111],"several":[113],"polytree-based":[114],"fields":[117],"(PEFs),":[118],"where":[119],"flows":[121],"from":[122,246],"high":[123],"locations":[125],"low":[127],"locations,":[129],"just":[130],"as":[131],"water":[132],"driven":[134],"gravity":[137],"field.":[138],"We":[139,257],"also":[140],"provide":[141],"theoretical":[143],"analysis":[144],"ensure":[146],"that":[147,248,269,281],"polytree":[149],"decomposition":[150],"can":[152,288],"any":[154],"graph.":[157],"In":[158,276],"module,":[161],"learns":[163],"predict":[170],"dynamics":[172],"PEFs.":[174,218,236],"The":[175,198,219,237,266],"correlation-adaptive":[178],"neural":[179],"network":[180],"structures,":[181],"temporal":[186,189,199],"component":[187,194,200,221],"correlations":[190],"spatial":[193,196,220,233],"correlations.":[197],"employs":[201],"GRU":[203],"DCN":[205],"combined":[206],"structure":[207,234],"capture":[209],"both":[210],"short-term":[211],"autocorrelation":[212],"long-term":[214],"repeating":[215],"patterns":[216],"extends":[222],"GAT":[224],"using":[225],"weighted":[226],"directed":[227],"attention":[228],"asymmetric":[232],"results":[239,267],"finally":[244],"derived":[245],"PEFs":[247,283],"predicted":[250],"model.":[256],"conduct":[258],"extensive":[259],"evaluations":[260],"three":[262],"real-world":[263],"datasets.":[265],"show":[268],"our":[270,286],"outperforms":[272],"state-of-the-art":[274],"baselines.":[275],"addition,":[277],"case":[278],"confirm":[280],"learned":[284],"framework":[287],"thus":[296],"improving":[297],"interpretability.":[300]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":34},{"year":2024,"cited_by_count":27},{"year":2023,"cited_by_count":4}],"updated_date":"2026-04-27T08:22:11.395708","created_date":"2025-10-10T00:00:00"}
