{"id":"https://openalex.org/W4366307177","doi":"https://doi.org/10.1109/icite56321.2022.10101416","title":"Dynamic Origin-Destination Demand Prediction with Improved LSTM Model","display_name":"Dynamic Origin-Destination Demand Prediction with Improved LSTM Model","publication_year":2022,"publication_date":"2022-11-11","ids":{"openalex":"https://openalex.org/W4366307177","doi":"https://doi.org/10.1109/icite56321.2022.10101416"},"language":"en","primary_location":{"id":"doi:10.1109/icite56321.2022.10101416","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icite56321.2022.10101416","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","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/A5101969436","display_name":"Wei Tan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210094968","display_name":"South China Municipal Engineering Design and Research Institute (China)","ror":"https://ror.org/00n7rz703","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210094968"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wei Tan","raw_affiliation_strings":["Southwest municipal engineering design & research institute of China,Chengdu,China"],"affiliations":[{"raw_affiliation_string":"Southwest municipal engineering design & research institute of China,Chengdu,China","institution_ids":["https://openalex.org/I4210094968"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100397465","display_name":"Yang Yang","orcid":"https://orcid.org/0000-0001-9172-1695"},"institutions":[{"id":"https://openalex.org/I4210094968","display_name":"South China Municipal Engineering Design and Research Institute (China)","ror":"https://ror.org/00n7rz703","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210094968"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Yang","raw_affiliation_strings":["Southwest municipal engineering design & research institute of China,Chengdu,China"],"affiliations":[{"raw_affiliation_string":"Southwest municipal engineering design & research institute of China,Chengdu,China","institution_ids":["https://openalex.org/I4210094968"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064863311","display_name":"Pengfa Song","orcid":null},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengfa Song","raw_affiliation_strings":["Southwest Jiaotong University,School of Transportation and Logistics,Chengdu,China,610031","National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University,School of Transportation and Logistics,Chengdu,China,610031","institution_ids":["https://openalex.org/I4800084"]},{"raw_affiliation_string":"National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Chengdu, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112303055","display_name":"Yanning Huang","orcid":"https://orcid.org/0000-0001-6918-8556"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanning Huang","raw_affiliation_strings":["Southwest Jiaotong University,School of Transportation and Logistics,Chengdu,China,610031","National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University,School of Transportation and Logistics,Chengdu,China,610031","institution_ids":["https://openalex.org/I4800084"]},{"raw_affiliation_string":"National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Chengdu, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100375018","display_name":"Jing Liu","orcid":"https://orcid.org/0000-0002-5347-8281"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Liu","raw_affiliation_strings":["Southwest Jiaotong University,School of Transportation and Logistics,Chengdu,China,610031","National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University,School of Transportation and Logistics,Chengdu,China,610031","institution_ids":["https://openalex.org/I4800084"]},{"raw_affiliation_string":"National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Chengdu, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037521493","display_name":"Fangfang Zheng","orcid":"https://orcid.org/0000-0002-0150-6711"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fangfang Zheng","raw_affiliation_strings":["Southwest Jiaotong University,School of Transportation and Logistics,Chengdu,China,610031","National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University,School of Transportation and Logistics,Chengdu,China,610031","institution_ids":["https://openalex.org/I4800084"]},{"raw_affiliation_string":"National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Chengdu, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5101969436"],"corresponding_institution_ids":["https://openalex.org/I4210094968"],"apc_list":null,"apc_paid":null,"fwci":0.1058,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.45002037,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"344","last_page":"349"},"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9994999766349792,"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":0.9972000122070312,"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/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.7863904237747192},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7799137830734253},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.5160672068595886},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.487490177154541},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.4641849398612976},{"id":"https://openalex.org/keywords/spatial-correlation","display_name":"Spatial correlation","score":0.46148911118507385},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44167912006378174},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4289938807487488},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.29351896047592163},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.13535022735595703},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08955162763595581}],"concepts":[{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.7863904237747192},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7799137830734253},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.5160672068595886},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.487490177154541},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.4641849398612976},{"id":"https://openalex.org/C150060386","wikidata":"https://www.wikidata.org/wiki/Q7574054","display_name":"Spatial correlation","level":2,"score":0.46148911118507385},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44167912006378174},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4289938807487488},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29351896047592163},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.13535022735595703},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08955162763595581},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icite56321.2022.10101416","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icite56321.2022.10101416","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6000000238418579,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W651132625","https://openalex.org/W1489040641","https://openalex.org/W1973943669","https://openalex.org/W1983483726","https://openalex.org/W1989522934","https://openalex.org/W2016668479","https://openalex.org/W2046466117","https://openalex.org/W2060140077","https://openalex.org/W2064675550","https://openalex.org/W2107647298","https://openalex.org/W2148343118","https://openalex.org/W2470083109","https://openalex.org/W2730580788","https://openalex.org/W2913894912","https://openalex.org/W2956452632","https://openalex.org/W3003635954","https://openalex.org/W3007547063","https://openalex.org/W3108689572","https://openalex.org/W3111769493","https://openalex.org/W3212989947","https://openalex.org/W6621466622","https://openalex.org/W6720033815"],"related_works":["https://openalex.org/W1610502790","https://openalex.org/W2082074857","https://openalex.org/W1576363358","https://openalex.org/W26040078","https://openalex.org/W3127069196","https://openalex.org/W3105571901","https://openalex.org/W4205257898","https://openalex.org/W2389069338","https://openalex.org/W1605860016","https://openalex.org/W4294533716"],"abstract_inverted_index":{"As":[0],"the":[1,13,18,23,38,43,48,66,73,78,90,101,112,120,126,129,140,143,150,155,164,168,172,179],"basis":[2],"of":[3,28,52,72,86,105,116,128,142,161],"dynamic":[4,61],"traffic":[5,7,29,53],"distribution,":[6],"demand":[8,34],"prediction":[9,35,63],"needs":[10],"to":[11],"address":[12],"complicated":[14],"coupling":[15],"correlations":[16],"within":[17],"road":[19,44],"network":[20,45,74],"structure":[21],"and":[22,25,47,50,68,93,119,167,183],"spatial":[24,98,102],"seasonal":[26],"variation":[27],"demand.":[30,137],"Dynamic":[31],"origin-destination":[32],"(OD)":[33],"can":[36],"present":[37],"correlation":[39,103,114],"between":[40],"nodes":[41],"in":[42,163,186,189],"pattern":[46],"time-varying":[49],"periodicity":[51],"data.":[54,107],"In":[55],"this":[56],"paper,":[57],"we":[58,146],"propose":[59],"a":[60],"OD":[62,106,117,136,151],"model":[64,84,175],"considering":[65],"spatial-temporal":[67],"environmental":[69],"aspects":[70],"(STE)":[71],"(STE-CNN-LSTM)":[75],"based":[76],"on":[77,135],"Long-Short-Term-Memory":[79],"(LSTM)":[80],"algorithm.":[81],"The":[82,97,108],"proposed":[83,144,173],"consists":[85],"three":[87],"modules":[88],"including":[89],"spatial,":[91],"temporal":[92,109],"external":[94,121,130],"environment":[95,122],"modules.":[96],"module":[99,110,123],"extracts":[100,111],"information":[104,115],"time":[113],"data,":[118],"deals":[124],"with":[125],"impact":[127],"factors":[131],"(e.g.,":[132],"weather,":[133],"accidents)":[134],"To":[138],"verify":[139],"effectiveness":[141],"model,":[145],"conducted":[147],"experiments":[148],"using":[149],"dataset":[152],"collected":[153],"by":[154],"Caltrans":[156],"Performance":[157],"Measurement":[158],"System":[159],"(PeMS)":[160],"California":[162],"United":[165],"States,":[166],"results":[169],"show":[170],"that":[171],"STE-CNN-LSTM":[174],"performs":[176],"better":[177],"than":[178],"ARIMA,":[180],"general":[181],"LSTM":[182],"CNN-LSTM":[184],"models":[185],"most":[187,190],"respect":[188],"respects.":[191]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}