{"id":"https://openalex.org/W2896202861","doi":"https://doi.org/10.1145/3283207.3283213","title":"Predicting Traffic Congestion Propagation Patterns","display_name":"Predicting Traffic Congestion Propagation Patterns","publication_year":2018,"publication_date":"2018-10-22","ids":{"openalex":"https://openalex.org/W2896202861","doi":"https://doi.org/10.1145/3283207.3283213","mag":"2896202861"},"language":"en","primary_location":{"id":"doi:10.1145/3283207.3283213","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3283207.3283213","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3283207.3283213","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 11th ACM SIGSPATIAL International Workshop on Computational Transportation Science","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3283207.3283213","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014057430","display_name":"Haoyi Xiong","orcid":"https://orcid.org/0000-0003-0741-9034"},"institutions":[{"id":"https://openalex.org/I126307644","display_name":"University of Iowa","ror":"https://ror.org/036jqmy94","country_code":"US","type":"education","lineage":["https://openalex.org/I126307644"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haoyi Xiong","raw_affiliation_strings":["The University of Iowa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Iowa","institution_ids":["https://openalex.org/I126307644"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084260133","display_name":"Amin Vahedian","orcid":null},"institutions":[{"id":"https://openalex.org/I126307644","display_name":"University of Iowa","ror":"https://ror.org/036jqmy94","country_code":"US","type":"education","lineage":["https://openalex.org/I126307644"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Amin Vahedian","raw_affiliation_strings":["The University of Iowa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Iowa","institution_ids":["https://openalex.org/I126307644"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086198510","display_name":"Xun Zhou","orcid":"https://orcid.org/0000-0003-4930-6572"},"institutions":[{"id":"https://openalex.org/I126307644","display_name":"University of Iowa","ror":"https://ror.org/036jqmy94","country_code":"US","type":"education","lineage":["https://openalex.org/I126307644"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xun Zhou","raw_affiliation_strings":["The University of Iowa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Iowa","institution_ids":["https://openalex.org/I126307644"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100630059","display_name":"Yanhua Li","orcid":"https://orcid.org/0000-0001-8972-503X"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanhua Li","raw_affiliation_strings":["Worcester Polytechnic Institute"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5106731907","display_name":"Jun Luo","orcid":"https://orcid.org/0000-0002-2032-0381"},"institutions":[{"id":"https://openalex.org/I4210156165","display_name":"Lenovo (China)","ror":"https://ror.org/04srd9d93","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210156165"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Luo","raw_affiliation_strings":["Machine Intelligence Center, Lenovo Group Limited"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Machine Intelligence Center, Lenovo Group Limited","institution_ids":["https://openalex.org/I4210156165"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":8.9576,"has_fulltext":true,"cited_by_count":38,"citation_normalized_percentile":{"value":0.98659004,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"60","last_page":"69"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9991000294685364,"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":0.9991000294685364,"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/T11106","display_name":"Data Management and Algorithms","score":0.9980000257492065,"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"}},{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","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":0.7544905543327332},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.584205150604248},{"id":"https://openalex.org/keywords/footprint","display_name":"Footprint","score":0.45788872241973877},{"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":0.4265252351760864},{"id":"https://openalex.org/keywords/network-congestion","display_name":"Network congestion","score":0.4119876027107239},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3541804254055023},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.24932622909545898},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.21374639868736267},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.13192322850227356},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11944311857223511},{"id":"https://openalex.org/keywords/network-packet","display_name":"Network packet","score":0.08418619632720947}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7544905543327332},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.584205150604248},{"id":"https://openalex.org/C132943942","wikidata":"https://www.wikidata.org/wiki/Q2562511","display_name":"Footprint","level":2,"score":0.45788872241973877},{"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":0.4265252351760864},{"id":"https://openalex.org/C195563490","wikidata":"https://www.wikidata.org/wiki/Q180368","display_name":"Network congestion","level":3,"score":0.4119876027107239},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3541804254055023},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.24932622909545898},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.21374639868736267},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.13192322850227356},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11944311857223511},{"id":"https://openalex.org/C158379750","wikidata":"https://www.wikidata.org/wiki/Q214111","display_name":"Network packet","level":2,"score":0.08418619632720947},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3283207.3283213","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3283207.3283213","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3283207.3283213","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 11th ACM SIGSPATIAL International Workshop on Computational Transportation Science","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3283207.3283213","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3283207.3283213","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3283207.3283213","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 11th ACM SIGSPATIAL International Workshop on Computational Transportation Science","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.800000011920929}],"awards":[{"id":"https://openalex.org/G320407845","display_name":"CRII: III: Discovering Complex Change Footprint Patterns on Spatio-Temporal Big Data for Urban Sustainability","funder_award_id":"1566386","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5252722770","display_name":"CRII: CPS: CityLines: Designing Urban Hub-and-Spoke Transportation System with Data-Driven Cyber-Control","funder_award_id":"1657350","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8372766862","display_name":"SCC: Leveraging Autonomous Shared Vehicles for Greater Community Health, Equity, Livability, and Prosperity (HELP)","funder_award_id":"1831140","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8761899520","display_name":null,"funder_award_id":"1831140","funder_id":"https://openalex.org/F4320337391","funder_display_name":"Division of Civil, Mechanical and Manufacturing Innovation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337391","display_name":"Division of Civil, Mechanical and Manufacturing Innovation","ror":"https://ror.org/028yd4c30"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2896202861.pdf","grobid_xml":"https://content.openalex.org/works/W2896202861.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W2532536","https://openalex.org/W1988726303","https://openalex.org/W2029436115","https://openalex.org/W2062017159","https://openalex.org/W2100522105","https://openalex.org/W2117618130","https://openalex.org/W2166771065","https://openalex.org/W2167109052","https://openalex.org/W2183679353","https://openalex.org/W2273707087","https://openalex.org/W2294628100","https://openalex.org/W2344216563","https://openalex.org/W2463516922","https://openalex.org/W2463787190","https://openalex.org/W2509029702","https://openalex.org/W2561722812","https://openalex.org/W2565239705","https://openalex.org/W2739060064","https://openalex.org/W2744100271","https://openalex.org/W2762079755","https://openalex.org/W2775800859"],"related_works":["https://openalex.org/W301804496","https://openalex.org/W2042439812","https://openalex.org/W2972320057","https://openalex.org/W2897741166","https://openalex.org/W2988005308","https://openalex.org/W4386289889","https://openalex.org/W1669406372","https://openalex.org/W4211214322","https://openalex.org/W1979597192","https://openalex.org/W4390987329"],"abstract_inverted_index":{"A":[0],"traffic":[1,45],"congestion":[2,15,46,54,70,116,132,144,163],"in":[3,26,38,73,88,119,135],"a":[4,14,19,31,157],"road":[5,11,23,36],"network":[6,33],"may":[7,17],"propagate":[8,56,72],"to":[9,50,77,86,91,164,174,192,216],"upstream":[10],"segments.":[12],"Such":[13],"propagation":[16,47,93,117,133,145],"make":[18],"series":[20],"of":[21,43,130,143,153,159,199,206],"connected":[22],"segments":[24,37],"congested":[25,35,160],"the":[27,41,74,128,136,141,151,188,203,207],"near":[28,75,137,194],"future.":[29,138],"Given":[30],"spatial-temporal":[32],"and":[34,97,201],"current":[39],"time,":[40],"aim":[42],"predicting":[44,131],"pattern":[48],"is":[49,84,156,190],"predict":[51,87,140,193],"where":[52,150],"those":[53],"will":[55,71,126],"to.":[57],"This":[58],"can":[59,220],"provide":[60],"users":[61],"(e.g.":[62],"city":[63],"officials)":[64],"with":[65,197],"valuable":[66],"information":[67],"on":[68,110,180],"how":[69],"future":[76,195],"help":[78],"mitigating":[79],"emerging":[80],"congestions.":[81],"However,":[82],"it":[83],"challenging":[85],"real-time":[89],"due":[90],"complex":[92],"process":[94],"between":[95],"roads":[96,161],"high":[98],"computational":[99],"intensity":[100],"caused":[101],"by":[102],"large":[103],"dataset.":[104],"Recent":[105],"studies":[106,212],"have":[107,213],"been":[108,214],"focusing":[109],"finding":[111],"frequent":[112],"or":[113],"most":[114],"likely":[115],"patterns":[118,134],"historical":[120],"data.":[121],"In":[122],"contrast,":[123],"this":[124,176],"research":[125],"address":[127],"problem":[129],"We":[139,167],"footprint":[142],"as":[146],"Propagation":[147],"Graphs":[148],"(Pro-Graphs)":[149],"root":[152],"each":[154],"Pro-Graph":[155],"set":[158],"propagating":[162],"nearby":[165],"roads.":[166],"propose":[168],"an":[169],"efficient":[170],"algorithm":[171],"called":[172],"PPI_Fast":[173,189],"achieve":[175],"prediction.":[177],"Our":[178],"experiments":[179],"real-word":[181],"dataset":[182],"from":[183],"Shenzhen,":[184],"China":[185],"shows":[186],"that":[187],"able":[191],"propagations":[196],"AUC":[198],"0.75":[200],"improves":[202],"running":[204],"time":[205],"baseline":[208],"algorithm.":[209],"Two":[210],"case":[211],"done":[215],"show":[217],"our":[218],"work":[219],"find":[221],"meaningful":[222],"patterns.":[223]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":3}],"updated_date":"2026-08-06T08:24:18.245995","created_date":"2025-10-10T00:00:00"}
