{"id":"https://openalex.org/W3198175902","doi":"https://doi.org/10.1109/tits.2021.3105445","title":"Learning Traffic Network Embeddings for Predicting Congestion Propagation","display_name":"Learning Traffic Network Embeddings for Predicting Congestion Propagation","publication_year":2021,"publication_date":"2021-08-26","ids":{"openalex":"https://openalex.org/W3198175902","doi":"https://doi.org/10.1109/tits.2021.3105445","mag":"3198175902"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2021.3105445","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2021.3105445","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Intelligent Transportation Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://dr.ntu.edu.sg/bitstream/10356/153860/3/Learning%20Traffic%20Network%20Embeddings%20for%20Predicting%20Congestion%20Propagation.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100629959","display_name":"Yidan Sun","orcid":"https://orcid.org/0000-0002-3607-4963"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Yidan Sun","raw_affiliation_strings":["School of Computer Science and Engineering, Nanyang Technological University, Singapore"],"raw_orcid":"https://orcid.org/0000-0002-3607-4963","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076931362","display_name":"Guiyuan Jiang","orcid":"https://orcid.org/0000-0002-1398-821X"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Guiyuan Jiang","raw_affiliation_strings":["School of Computer Science and Engineering, Nanyang Technological University, Singapore"],"raw_orcid":"https://orcid.org/0000-0002-1398-821X","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002694277","display_name":"Siew-Kei Lam","orcid":"https://orcid.org/0000-0002-8346-2635"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Siew-Kei Lam","raw_affiliation_strings":["School of Computer Science and Engineering, Nanyang Technological University, Singapore"],"raw_orcid":"https://orcid.org/0000-0002-8346-2635","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053523719","display_name":"Peilan He","orcid":"https://orcid.org/0000-0002-7411-2801"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Peilan He","raw_affiliation_strings":["School of Computer Science and Engineering, Nanyang Technological University, Singapore"],"raw_orcid":"https://orcid.org/0000-0002-7411-2801","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0936,"has_fulltext":true,"cited_by_count":18,"citation_normalized_percentile":{"value":0.75334365,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"23","issue":"8","first_page":"11591","last_page":"11604"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9957000017166138,"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/T10524","display_name":"Traffic control and management","score":0.9857000112533569,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6568012237548828},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6362899541854858},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6324260234832764},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.5813455581665039},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.40590354800224304},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.333065003156662},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2812999486923218},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.2606610059738159},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17575088143348694}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6568012237548828},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6362899541854858},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6324260234832764},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.5813455581665039},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.40590354800224304},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.333065003156662},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2812999486923218},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.2606610059738159},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17575088143348694}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tits.2021.3105445","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2021.3105445","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Intelligent Transportation Systems","raw_type":"journal-article"},{"id":"pmh:oai:dr.ntu.edu.sg:10356/153860","is_oa":true,"landing_page_url":"https://hdl.handle.net/10356/153860","pdf_url":"https://dr.ntu.edu.sg/bitstream/10356/153860/3/Learning%20Traffic%20Network%20Embeddings%20for%20Predicting%20Congestion%20Propagation.pdf","source":{"id":"https://openalex.org/S4306402609","display_name":"DR-NTU (Nanyang Technological University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I172675005","host_organization_name":"Nanyang Technological University","host_organization_lineage":["https://openalex.org/I172675005"],"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":"Journal Article"}],"best_oa_location":{"id":"pmh:oai:dr.ntu.edu.sg:10356/153860","is_oa":true,"landing_page_url":"https://hdl.handle.net/10356/153860","pdf_url":"https://dr.ntu.edu.sg/bitstream/10356/153860/3/Learning%20Traffic%20Network%20Embeddings%20for%20Predicting%20Congestion%20Propagation.pdf","source":{"id":"https://openalex.org/S4306402609","display_name":"DR-NTU (Nanyang Technological University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I172675005","host_organization_name":"Nanyang Technological University","host_organization_lineage":["https://openalex.org/I172675005"],"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":"Journal Article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320709","display_name":"National Research Foundation Singapore","ror":"https://ror.org/03cpyc314"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3198175902.pdf","grobid_xml":"https://content.openalex.org/works/W3198175902.grobid-xml"},"referenced_works_count":55,"referenced_works":["https://openalex.org/W1639150154","https://openalex.org/W1836902361","https://openalex.org/W1888005072","https://openalex.org/W1985852691","https://openalex.org/W1994318791","https://openalex.org/W2026417691","https://openalex.org/W2029436115","https://openalex.org/W2052106218","https://openalex.org/W2069929199","https://openalex.org/W2119102281","https://openalex.org/W2154454189","https://openalex.org/W2154851992","https://openalex.org/W2344029946","https://openalex.org/W2387462954","https://openalex.org/W2463516922","https://openalex.org/W2463787190","https://openalex.org/W2532536081","https://openalex.org/W2575125657","https://openalex.org/W2604942799","https://openalex.org/W2624466239","https://openalex.org/W2747987831","https://openalex.org/W2756203131","https://openalex.org/W2782194476","https://openalex.org/W2785362665","https://openalex.org/W2793342280","https://openalex.org/W2808466528","https://openalex.org/W2808871417","https://openalex.org/W2886364855","https://openalex.org/W2896202861","https://openalex.org/W2896953764","https://openalex.org/W2901649873","https://openalex.org/W2903871660","https://openalex.org/W2904486783","https://openalex.org/W2940640769","https://openalex.org/W2962975498","https://openalex.org/W2965341826","https://openalex.org/W2968259729","https://openalex.org/W2983244957","https://openalex.org/W2986068433","https://openalex.org/W2991174201","https://openalex.org/W2996585552","https://openalex.org/W2998313947","https://openalex.org/W3005075563","https://openalex.org/W3009165682","https://openalex.org/W3011019592","https://openalex.org/W3015494591","https://openalex.org/W3029390384","https://openalex.org/W3098944285","https://openalex.org/W3103720336","https://openalex.org/W3104097132","https://openalex.org/W6636591160","https://openalex.org/W6680012447","https://openalex.org/W6689637345","https://openalex.org/W6754461212","https://openalex.org/W6774543359"],"related_works":["https://openalex.org/W2081900870","https://openalex.org/W2183306018","https://openalex.org/W2549990292","https://openalex.org/W2345479200","https://openalex.org/W2951819827","https://openalex.org/W2849310602","https://openalex.org/W2419146053","https://openalex.org/W3006008237","https://openalex.org/W2088247287","https://openalex.org/W4313153715"],"abstract_inverted_index":{"Traffic":[0],"congestion":[1,23,32,59,81,100,138,175,190,208],"has":[2],"become":[3],"a":[4,71,119,125,132],"global":[5,154,183],"concern":[6],"due":[7],"to":[8,20,46,69,123],"continuous":[9],"increase":[10],"in":[11,87,211],"traffic":[12,22,39,89,196],"demand":[13],"and":[14,42,66,78,131,153,186,206],"limited":[15],"road":[16,44,63,85,106,129,167],"capacity.":[17],"The":[18,114],"ability":[19],"predict":[21],"propagation,":[24],"which":[25,135,169],"depicts":[26],"the":[27,31,56,62,80,88,97,137,143,166,179,204,207,218],"spatiotemporal":[28],"evolution":[29,82],"of":[30,58,99,105,150,165,174,189],"scenario,":[33],"is":[34,157],"essential":[35],"for":[36,127,160],"developing":[37],"smart":[38],"management":[40],"systems":[41],"enabling":[43],"users":[45],"make":[47],"informed":[48],"route":[49],"choices.":[50],"In":[51,91],"this":[52,68],"work,":[53],"we":[54],"study":[55],"behavior":[57],"propagation":[60,101,112,133,139,184,209],"at":[61],"segment":[64],"level,":[65],"leverage":[67],"develop":[70],"novel":[72],"machine":[73],"learning":[74,161],"framework":[75,94,116],"that":[76,199],"characterizes":[77],"predicts":[79],"among":[83],"different":[84],"segments":[86,107,168],"network.":[90],"particular,":[92],"our":[93,200,212],"can":[95],"infer":[96],"likelihood":[98,140],"between":[102],"any":[103],"pair":[104],"through":[108],"single":[109],"or":[110],"multiple":[111],"paths.":[113],"proposed":[115],"relies":[117],"on":[118,142,217],"network":[120],"embedding":[121,149],"module":[122],"learn":[124],"representation":[126],"each":[128],"segment,":[130],"model":[134],"calculates":[136],"based":[141],"learned":[144],"representations.":[145],"Specifically,":[146],"an":[147],"asymmetric":[148,187],"local":[151,180],"proximity":[152,181],"tendency":[155],"(AE-LPGT)":[156],"relied":[158],"upon":[159],"low":[162],"dimension":[163],"embeddings":[164,213],"incorporate":[170],"various":[171],"realistic":[172],"properties":[173,210],"propagations,":[176],"such":[177],"as":[178],"property,":[182],"tendency,":[185],"transitivity":[188],"propagations.":[191],"Experimental":[192],"results":[193],"with":[194],"Singapore":[195],"data":[197],"show":[198],"method":[201],"significantly":[202],"outperforms":[203],"state-of-the-art,":[205],"have":[214],"significant":[215],"impact":[216],"prediction":[219],"performance.":[220]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}