{"id":"https://openalex.org/W4415250877","doi":"https://doi.org/10.1109/hpec67600.2025.11196391","title":"Predicting Ports Congestion by Utilizing State Space Models","display_name":"Predicting Ports Congestion by Utilizing State Space Models","publication_year":2025,"publication_date":"2025-09-15","ids":{"openalex":"https://openalex.org/W4415250877","doi":"https://doi.org/10.1109/hpec67600.2025.11196391"},"language":"en","primary_location":{"id":"doi:10.1109/hpec67600.2025.11196391","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpec67600.2025.11196391","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE High Performance Extreme Computing Conference (HPEC)","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/A5115491740","display_name":"Nikolay Aristov","orcid":null},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Nikolay Aristov","raw_affiliation_strings":["Massachusetts Institute of Technology,Center for Transportation and Logistics,Cambridge,MA,USA"],"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology,Center for Transportation and Logistics,Cambridge,MA,USA","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050654045","display_name":"Elenna Dugundji","orcid":"https://orcid.org/0000-0001-9277-9485"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Elenna R. Dugundji","raw_affiliation_strings":["Massachusetts Institute of Technology,Center for Transportation and Logistics,Cambridge,MA,USA"],"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology,Center for Transportation and Logistics,Cambridge,MA,USA","institution_ids":["https://openalex.org/I63966007"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5115491740"],"corresponding_institution_ids":["https://openalex.org/I63966007"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.43570155,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11223","display_name":"Maritime Ports and Logistics","score":0.7998999953269958,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T11223","display_name":"Maritime Ports and Logistics","score":0.7998999953269958,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T12126","display_name":"Maritime Transport Emissions and Efficiency","score":0.7164999842643738,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.5049999952316284},{"id":"https://openalex.org/keywords/bilinear-interpolation","display_name":"Bilinear interpolation","score":0.49559998512268066},{"id":"https://openalex.org/keywords/port","display_name":"Port (circuit theory)","score":0.47760000824928284},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.4230000078678131},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.41200000047683716},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4002000093460083},{"id":"https://openalex.org/keywords/supply-chain","display_name":"Supply chain","score":0.39250001311302185},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.3894999921321869},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.3547999858856201}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.652999997138977},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.5049999952316284},{"id":"https://openalex.org/C205203396","wikidata":"https://www.wikidata.org/wiki/Q612143","display_name":"Bilinear interpolation","level":2,"score":0.49559998512268066},{"id":"https://openalex.org/C32802771","wikidata":"https://www.wikidata.org/wiki/Q2443617","display_name":"Port (circuit theory)","level":2,"score":0.47760000824928284},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4722000062465668},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.4230000078678131},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.41200000047683716},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4002000093460083},{"id":"https://openalex.org/C108713360","wikidata":"https://www.wikidata.org/wiki/Q1824206","display_name":"Supply chain","level":2,"score":0.39250001311302185},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.3894999921321869},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.3547999858856201},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.34389999508857727},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.33239999413490295},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.32339999079704285},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.323199987411499},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.3190999925136566},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3093000054359436},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3061000108718872},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2955999970436096},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2896000146865845},{"id":"https://openalex.org/C3019022308","wikidata":"https://www.wikidata.org/wiki/Q1418353","display_name":"Multidimensional data","level":2,"score":0.289000004529953},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.2639999985694885},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25850000977516174},{"id":"https://openalex.org/C29825287","wikidata":"https://www.wikidata.org/wiki/Q1427940","display_name":"Warning system","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.2540000081062317},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.2529999911785126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/hpec67600.2025.11196391","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpec67600.2025.11196391","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE High Performance Extreme Computing Conference (HPEC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2295598076","https://openalex.org/W2551087110","https://openalex.org/W2612358188","https://openalex.org/W3012562343","https://openalex.org/W3033739625","https://openalex.org/W4288051329","https://openalex.org/W4400420978"],"related_works":[],"abstract_inverted_index":{"Maritime":[0],"port":[1,32,225],"congestion":[2,21,252],"poses":[3],"significant":[4,221],"risks":[5],"to":[6,48,107,126,234,264],"global":[7],"supply":[8,39,286],"chains,":[9],"as":[10,246,281],"ports":[11],"handle":[12],"over":[13,123,236],"80%":[14],"of":[15,132],"world":[16],"trade":[17],"by":[18],"volume.":[19],"Current":[20],"prediction":[22,185],"methods":[23,239],"struggle":[24],"with":[25,60,86,148,227],"the":[26,112,119,200,256],"complex":[27,265],"temporal":[28,62,196,277],"dependencies":[29],"inherent":[30],"in":[31],"operations,":[33],"limiting":[34],"their":[35],"effectiveness":[36],"for":[37,90,95,111,169,215,223,250,259,284],"proactive":[38],"chain":[40,287],"management.":[41],"This":[42],"study":[43],"introduces":[44],"state-space":[45,109,262],"models":[46,110,193],"(SSMs)":[47],"maritime":[49,251],"logistics":[50,267],"forecasting,":[51],"proposing":[52],"a":[53,247],"novel":[54,102],"architecture":[55,203],"that":[56,163,177],"combines":[57],"input-adaptive":[58,91],"dynamics":[59],"multidimensional":[61,96,108],"processing.We":[63],"develop":[64],"and":[65,99,143,171,198,208,254,272],"evaluate":[66,136],"three":[67],"SSM":[68,184,218],"variants":[69],"using":[70],"Automatic":[71],"Identification":[72],"System":[73],"(AIS)":[74],"vessel":[75],"tracking":[76],"data":[77],"from":[78,122,232],"20":[79],"East":[80],"Coast":[81],"U.S.":[82],"terminals:":[83],"(1)":[84,178],"Liquid-S4":[85],"polynomial":[87],"bilinear":[88],"kernels":[89,106],"dynamics,":[92],"(2)":[93,187],"S4ND":[94,188,228],"signal":[97],"processing,":[98],"(3)":[100,199],"Liquid-S4ND-PB\u2014a":[101],"combination":[103],"extending":[104],"liquid":[105],"first":[113],"time.":[114],"Principal":[115],"component":[116],"analysis":[117],"reduces":[118],"feature":[120],"space":[121],"9,800":[124],"dimensions":[125],"9":[127],"components":[128],"while":[129],"retaining":[130],"99%":[131],"variance.":[133],"We":[134],"systematically":[135],"historical":[137,180],"window":[138],"lengths":[139],"(7,":[140],"14,":[141],"28,":[142],"56":[144],"days)":[145],"across":[146],"terminals":[147],"varying":[149],"throughput":[150],"characteristics.Comprehensive":[151],"evaluation":[152],"against":[153],"established":[154],"baselines":[155],"(XG-Boost,":[156],"LSTM,":[157],"spatial-temporal":[158],"graph":[159],"neural":[160],"networks)":[161],"reveals":[162],"SSMs":[164,245,280],"achieve":[165],"competitive":[166],"performance,":[167],"particularly":[168],"medium":[170],"low-throughput":[172],"terminals.":[173],"Key":[174],"findings":[175],"demonstrate":[176],"extended":[179,276],"windows":[181],"consistently":[182],"improve":[183],"accuracy,":[186],"outperforms":[189],"standard":[190],"liquid-enhanced":[191],"S4":[192],"on":[194,275],"longer":[195],"sequences,":[197],"proposed":[201],"Liquid-S4ND-PB":[202],"effectively":[204],"captures":[205],"both":[206],"short":[207],"long-term":[209],"dependencies.":[210],"While":[211],"XGBoost":[212],"maintains":[213],"superiority":[214],"high-throughput":[216],"terminals,":[217],"approaches":[219],"show":[220],"promise":[222],"diverse":[224],"environments,":[226],"achieving":[229],"MAE":[230],"improvements":[231],"0.51":[233],"0.47":[235],"previous":[237],"best-performing":[238],"at":[240],"medium-throughput":[241],"facilities.This":[242],"work":[243],"establishes":[244],"viable":[248],"paradigm":[249],"forecasting":[253],"provides":[255],"theoretical":[257],"foundation":[258],"applying":[260],"adaptive":[261],"modeling":[263],"spatio-temporal":[266],"problems.":[268],"The":[269],"demonstrated":[270],"scalability":[271],"strong":[273],"performance":[274],"horizons":[278],"position":[279],"critical":[282],"tools":[283],"next-generation":[285],"optimization.":[288]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-16T00:00:00"}