{"id":"https://openalex.org/W2897410508","doi":"https://doi.org/10.1145/3239576.3239623","title":"City-Level Traffic Flow Prediction via LSTM Networks","display_name":"City-Level Traffic Flow Prediction via LSTM Networks","publication_year":2018,"publication_date":"2018-06-16","ids":{"openalex":"https://openalex.org/W2897410508","doi":"https://doi.org/10.1145/3239576.3239623","mag":"2897410508"},"language":"en","primary_location":{"id":"doi:10.1145/3239576.3239623","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3239576.3239623","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Advances in Image Processing","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/A5047621701","display_name":"Zikai Zou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zikai Zou","raw_affiliation_strings":["Institute for DeepCom Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for DeepCom Research, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048042559","display_name":"Peter Gao","orcid":"https://orcid.org/0000-0002-8518-9601"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peter Gao","raw_affiliation_strings":["Institute for DeepCom Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for DeepCom Research, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101781798","display_name":"Chang Yao","orcid":"https://orcid.org/0000-0002-4605-5352"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chang Yao","raw_affiliation_strings":["Institute for DeepCom Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for DeepCom Research, Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.121,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.79195324,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"149","last_page":"153"},"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/T10524","display_name":"Traffic control and management","score":0.9965999722480774,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9925000071525574,"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":0.7215811610221863},{"id":"https://openalex.org/keywords/intuition","display_name":"Intuition","score":0.6161757707595825},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5656207203865051},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.564208447933197},{"id":"https://openalex.org/keywords/floating-car-data","display_name":"Floating car data","score":0.49858593940734863},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.48898613452911377},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.47180095314979553},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.42273640632629395},{"id":"https://openalex.org/keywords/traffic-generation-model","display_name":"Traffic generation model","score":0.4215913712978363},{"id":"https://openalex.org/keywords/traffic-system","display_name":"Traffic system","score":0.4213711619377136},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.4026162028312683},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33840203285217285},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3379230499267578},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.3021294176578522},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.27680107951164246},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.18241187930107117},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1252783238887787},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.07964149117469788},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07517433166503906}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7215811610221863},{"id":"https://openalex.org/C132010649","wikidata":"https://www.wikidata.org/wiki/Q189222","display_name":"Intuition","level":2,"score":0.6161757707595825},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5656207203865051},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.564208447933197},{"id":"https://openalex.org/C64093975","wikidata":"https://www.wikidata.org/wiki/Q356677","display_name":"Floating car data","level":3,"score":0.49858593940734863},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.48898613452911377},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.47180095314979553},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.42273640632629395},{"id":"https://openalex.org/C176715033","wikidata":"https://www.wikidata.org/wiki/Q2080768","display_name":"Traffic generation model","level":2,"score":0.4215913712978363},{"id":"https://openalex.org/C2984717066","wikidata":"https://www.wikidata.org/wiki/Q4323994","display_name":"Traffic system","level":2,"score":0.4213711619377136},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.4026162028312683},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33840203285217285},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3379230499267578},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.3021294176578522},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27680107951164246},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.18241187930107117},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1252783238887787},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.07964149117469788},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07517433166503906},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3239576.3239623","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3239576.3239623","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Advances in Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.8399999737739563,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1485009520","https://openalex.org/W2016366434","https://openalex.org/W2024934110","https://openalex.org/W2046471716","https://openalex.org/W2088136988","https://openalex.org/W2090274112","https://openalex.org/W2130942839","https://openalex.org/W2166874862","https://openalex.org/W2325261190","https://openalex.org/W2528040708","https://openalex.org/W2528639018","https://openalex.org/W2594994417","https://openalex.org/W2763555263","https://openalex.org/W2792525550","https://openalex.org/W2949888546","https://openalex.org/W2952740813","https://openalex.org/W2953118818","https://openalex.org/W2962721744","https://openalex.org/W2964335392","https://openalex.org/W3098552807","https://openalex.org/W3098815845"],"related_works":["https://openalex.org/W2009112536","https://openalex.org/W2410941711","https://openalex.org/W2973192971","https://openalex.org/W2005409769","https://openalex.org/W2008793610","https://openalex.org/W2044422050","https://openalex.org/W4360619413","https://openalex.org/W4390341805","https://openalex.org/W3069032","https://openalex.org/W2351802214"],"abstract_inverted_index":{"Along":[0],"with":[1,70,119,145,160],"the":[2,10,46,82,98,104,110,161],"rapid":[3],"development":[4],"of":[5,13,122],"modern":[6],"urban":[7,59,75],"cities":[8],"and":[9,29,39,90,108,125,142],"increasing":[11],"number":[12],"vehicles,":[14],"they":[15],"also":[16],"cause":[17],"severe":[18],"traffic":[19,42,47,68,72,83,92,106,135],"problems.":[20],"They":[21],"trouble":[22],"citizens":[23],"a":[24,50,116,140],"lot":[25],"in":[26,53,74,130],"wasting":[27],"time":[28,132],"gas":[30],"energy,":[31],"leading":[32],"to":[33,66,102],"their":[34],"bad":[35],"temper":[36],"during":[37],"work,":[38],"even":[40],"causing":[41],"accidents.":[43],"Thus,":[44],"monitoring":[45],"flow":[48,69],"is":[49,79,85,94],"critical":[51],"portion":[52],"building":[54],"intelligent":[55],"transportation":[56],"systems":[57],"for":[58,133],"cities.":[60,76],"In":[61],"this":[62],"paper,":[63],"we":[64,96,114],"aim":[65],"predict":[67],"big":[71],"data":[73,107],"Our":[77],"intuition":[78],"that":[80,152],"although":[81],"information":[84],"influenced":[86],"by":[87],"many":[88],"factors":[89],"precise":[91],"modeling":[93],"difficult,":[95],"use":[97],"machine":[99],"learning":[100],"techniques":[101],"analyze":[103],"historical":[105,123],"train":[109],"prediction":[111,128,157],"model.":[112],"Particularly,":[113],"build":[115],"LSTM":[117],"model":[118],"different":[120],"dimensions":[121],"data,":[124],"then":[126],"make":[127],"almost":[129],"real":[131],"current":[134],"flows.":[136],"We":[137],"have":[138],"built":[139],"prototype":[141],"tested":[143],"it":[144],"two":[146],"benchmark":[147],"dataset.":[148],"Experiment":[149],"results":[150],"show":[151],"our":[153],"system":[154],"obtains":[155],"high":[156],"accuracy":[158],"compared":[159],"ground":[162],"truth.":[163]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
