{"id":"https://openalex.org/W4391094333","doi":"https://doi.org/10.1109/bigdata59044.2023.10386463","title":"Difforecast: Image Generation Based Highway Traffic Forecasting with Diffusion Model","display_name":"Difforecast: Image Generation Based Highway Traffic Forecasting with Diffusion Model","publication_year":2023,"publication_date":"2023-12-15","ids":{"openalex":"https://openalex.org/W4391094333","doi":"https://doi.org/10.1109/bigdata59044.2023.10386463"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata59044.2023.10386463","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata59044.2023.10386463","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Big Data (BigData)","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/A5114120421","display_name":"Pengnan Chi","orcid":null},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Pengnan Chi","raw_affiliation_strings":["KTH Royal Institute of Technology,ITS lab,Department of Civil and Architectural Engineering,Brinellv&#x00E4;gen 23,Stockholm,Sweden,10044"],"affiliations":[{"raw_affiliation_string":"KTH Royal Institute of Technology,ITS lab,Department of Civil and Architectural Engineering,Brinellv&#x00E4;gen 23,Stockholm,Sweden,10044","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103171792","display_name":"Xiaoliang Ma","orcid":"https://orcid.org/0000-0001-5526-4511"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Xiaoliang Ma","raw_affiliation_strings":["KTH Royal Institute of Technology,ITS lab,Department of Civil and Architectural Engineering,Brinellv&#x00E4;gen 23,Stockholm,Sweden,10044"],"affiliations":[{"raw_affiliation_string":"KTH Royal Institute of Technology,ITS lab,Department of Civil and Architectural Engineering,Brinellv&#x00E4;gen 23,Stockholm,Sweden,10044","institution_ids":["https://openalex.org/I86987016"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5114120421"],"corresponding_institution_ids":["https://openalex.org/I86987016"],"apc_list":null,"apc_paid":null,"fwci":0.4741,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.63967565,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"608","last_page":"615"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9997000098228455,"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":0.9997000098228455,"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/T10524","display_name":"Traffic control and management","score":0.9894999861717224,"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"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.974399983882904,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7022922039031982},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5208576321601868},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4738227128982544},{"id":"https://openalex.org/keywords/traffic-generation-model","display_name":"Traffic generation model","score":0.45938944816589355},{"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":0.4370567798614502},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42998045682907104},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.42063888907432556},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3303682208061218},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.29120540618896484},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.24402207136154175},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.23361074924468994},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14529940485954285},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.08980685472488403}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7022922039031982},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5208576321601868},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4738227128982544},{"id":"https://openalex.org/C176715033","wikidata":"https://www.wikidata.org/wiki/Q2080768","display_name":"Traffic generation model","level":2,"score":0.45938944816589355},{"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":0.4370567798614502},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42998045682907104},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.42063888907432556},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3303682208061218},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.29120540618896484},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.24402207136154175},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.23361074924468994},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14529940485954285},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.08980685472488403},{"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/bigdata59044.2023.10386463","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata59044.2023.10386463","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1973943669","https://openalex.org/W2064675550","https://openalex.org/W2075795319","https://openalex.org/W2130942839","https://openalex.org/W2152878241","https://openalex.org/W2423557781","https://openalex.org/W2519091744","https://openalex.org/W2572939427","https://openalex.org/W2573587735","https://openalex.org/W2783478760","https://openalex.org/W2901504064","https://openalex.org/W2963358464","https://openalex.org/W2965341826","https://openalex.org/W2969210779","https://openalex.org/W2990330017","https://openalex.org/W3036167779","https://openalex.org/W3080253043","https://openalex.org/W3094502228","https://openalex.org/W3139204882","https://openalex.org/W3162926177","https://openalex.org/W4213130097","https://openalex.org/W4306317966","https://openalex.org/W4308558335","https://openalex.org/W4312933868","https://openalex.org/W4385245566","https://openalex.org/W4388918319","https://openalex.org/W4390872297","https://openalex.org/W6631190155","https://openalex.org/W6679045638","https://openalex.org/W6679436768","https://openalex.org/W6739901393","https://openalex.org/W6746015598","https://openalex.org/W6779823529","https://openalex.org/W6784333009","https://openalex.org/W6786152982","https://openalex.org/W6788990321","https://openalex.org/W6795288823","https://openalex.org/W6838697126"],"related_works":["https://openalex.org/W2587362999","https://openalex.org/W2149721642","https://openalex.org/W432084041","https://openalex.org/W1977405947","https://openalex.org/W1977153226","https://openalex.org/W4361199786","https://openalex.org/W4239349137","https://openalex.org/W2963251637","https://openalex.org/W1463884142","https://openalex.org/W239469043"],"abstract_inverted_index":{"Monitoring":[0],"and":[1,16,24,86,101],"forecasting":[2,59],"of":[3,18,43,51,76,83],"road":[4],"traffic":[5,13,22,28,52,63,87,109],"conditions":[6],"is":[7,17,95,105],"a":[8,40,66,102],"common":[9],"practice":[10],"for":[11,61],"real":[12],"information":[14],"system,":[15],"vital":[19],"importance":[20],"to":[21,47,97,107],"management":[23],"control.":[25],"While":[26],"dynamic":[27],"patterns":[29],"can":[30],"be":[31],"intuitively":[32],"represented":[33],"by":[34,111],"space-time":[35,44],"diagrams,":[36],"this":[37],"study":[38],"proposes":[39],"new":[41],"concept":[42],"image":[45,68],"(ST-image)":[46],"incorporate":[48],"physical":[49,84],"meanings":[50],"state":[53],"variables.":[54],"We":[55,71],"therefore":[56],"transform":[57],"the":[58,73,77,81,99,113,118],"problem":[60],"time-series":[62],"states":[64],"into":[65],"conditional":[67],"generation":[69],"problem.":[70],"explore":[72],"inherent":[74],"properties":[75],"ST":[78],"images":[79],"from":[80],"perspectives":[82],"meaning":[85],"dynamics.":[88],"An":[89],"innovative":[90],"deep":[91],"learning":[92],"based":[93,116],"architecture":[94],"designed":[96],"process":[98],"ST-image,":[100],"diffusion":[103],"model":[104],"trained":[106],"obtain":[108],"forecasts":[110],"generating":[112],"future":[114],"ST-images":[115],"on":[117],"historical":[119],"patterns.":[120]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2025-10-10T00:00:00"}
