{"id":"https://openalex.org/W4391770346","doi":"https://doi.org/10.1109/itsc57777.2023.10422082","title":"Distributed Points Data Driven LSTM Neural Networks for Daily Traffic Forecasting <sup>*</sup>","display_name":"Distributed Points Data Driven LSTM Neural Networks for Daily Traffic Forecasting <sup>*</sup>","publication_year":2023,"publication_date":"2023-09-24","ids":{"openalex":"https://openalex.org/W4391770346","doi":"https://doi.org/10.1109/itsc57777.2023.10422082"},"language":"en","primary_location":{"id":"doi:10.1109/itsc57777.2023.10422082","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc57777.2023.10422082","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)","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/A5022356497","display_name":"Taomei Zhu","orcid":"https://orcid.org/0000-0001-9081-5585"},"institutions":[{"id":"https://openalex.org/I50357001","display_name":"Universidad Carlos III de Madrid","ror":"https://ror.org/03ths8210","country_code":"ES","type":"education","lineage":["https://openalex.org/I50357001"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Taomei Zhu","raw_affiliation_strings":["Carlos III University of Madrid,Department of Mechanical Engineering,Madrid,Spain,28911"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carlos III University of Madrid,Department of Mechanical Engineering,Madrid,Spain,28911","institution_ids":["https://openalex.org/I50357001"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058375483","display_name":"Mar\u00eda Jes\u00fas L\u00f3pez Boada","orcid":"https://orcid.org/0000-0001-5377-0023"},"institutions":[{"id":"https://openalex.org/I50357001","display_name":"Universidad Carlos III de Madrid","ror":"https://ror.org/03ths8210","country_code":"ES","type":"education","lineage":["https://openalex.org/I50357001"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Maria Jesus L. Boada","raw_affiliation_strings":["Carlos III University of Madrid,Department of Mechanical Engineering,Madrid,Spain,28911"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carlos III University of Madrid,Department of Mechanical Engineering,Madrid,Spain,28911","institution_ids":["https://openalex.org/I50357001"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023304219","display_name":"Beatriz L. Boada","orcid":"https://orcid.org/0000-0002-8061-068X"},"institutions":[{"id":"https://openalex.org/I50357001","display_name":"Universidad Carlos III de Madrid","ror":"https://ror.org/03ths8210","country_code":"ES","type":"education","lineage":["https://openalex.org/I50357001"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Beatriz L. Boada","raw_affiliation_strings":["Carlos III University of Madrid,Department of Mechanical Engineering,Madrid,Spain,28911"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carlos III University of Madrid,Department of Mechanical Engineering,Madrid,Spain,28911","institution_ids":["https://openalex.org/I50357001"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1441,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.50836205,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"4744","last_page":"4749"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9983000159263611,"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":0.9983000159263611,"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/T10320","display_name":"Neural Networks and Applications","score":0.9569000005722046,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9438999891281128,"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":0.6661600470542908},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6554154753684998},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48213550448417664},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4270949959754944},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3598455786705017},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.09028124809265137}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6661600470542908},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6554154753684998},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48213550448417664},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4270949959754944},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3598455786705017},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.09028124809265137}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc57777.2023.10422082","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc57777.2023.10422082","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2004353783","https://openalex.org/W2036785686","https://openalex.org/W2040297119","https://openalex.org/W2775717462","https://openalex.org/W2792764867","https://openalex.org/W2903871660","https://openalex.org/W2963241951","https://openalex.org/W2964335123","https://openalex.org/W2965341826","https://openalex.org/W2996847713","https://openalex.org/W2997848713","https://openalex.org/W3160663839","https://openalex.org/W3212989947","https://openalex.org/W4281990365","https://openalex.org/W4283219050","https://openalex.org/W4297733535","https://openalex.org/W4308080108","https://openalex.org/W4308080993","https://openalex.org/W4308081670","https://openalex.org/W4327600908","https://openalex.org/W6746015598"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3209574120","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Daily":[0],"traffic":[1,7,13,49,83,117,158],"forecasting":[2,36,84,110],"is":[3,122],"crucial":[4],"for":[5,51],"optimizing":[6],"operations,":[8],"providing":[9],"traveler":[10],"information,":[11],"assisting":[12],"control":[14],"on":[15],"events,":[16],"supporting":[17],"incident":[18],"management,":[19],"etc.":[20],"However,":[21],"we":[22,57],"are":[23,105,135],"still":[24],"confronted":[25],"with":[26,115],"many":[27],"challenges,":[28],"mainly":[29],"due":[30],"to":[31,78,108,124,138],"the":[32,44,126,131,140,143,149,152],"medium-":[33,144],"and":[34,41,43,65,93,99,128,145,154],"long-term":[35,146],"horizon,":[37],"which":[38],"requires":[39],"details":[40],"trends,":[42],"difficulty":[45],"of":[46,88,96,103,130,142,151,157],"obtaining":[47],"complete":[48],"data":[50,62],"large":[52],"networks.":[53],"In":[54],"this":[55],"paper,":[56],"propose":[58],"a":[59,67,81],"distributed":[60],"points":[61],"driven":[63],"model":[64],"apply":[66],"hybrid":[68],"graph":[69],"attention":[70],"based":[71],"long-short":[72],"term":[73],"memory":[74],"(GAT":[75],"-LSTM)":[76],"architecture":[77],"carry":[79],"out":[80],"24-hour":[82],"task.":[85],"External":[86],"factors":[87],"holidays,":[89],"extreme":[90],"weather":[91],"changes,":[92],"vacation":[94],"habits":[95],"local":[97],"people":[98],"other":[100],"special":[101],"periods":[102],"time":[104],"also":[106,136],"encoded":[107],"improve":[109],"accuracy.":[111],"A":[112],"case":[113],"study":[114],"real-world":[116],"datasets":[118],"from":[119,148],"Madrid":[120],"city":[121],"introduced":[123],"demonstrate":[125],"validity":[127],"usability":[129],"model.":[132],"Experimental":[133],"results":[134],"used":[137],"reveal":[139],"characteristics":[141],"forecasts":[147],"perspective":[150],"spatial":[153],"temporal":[155],"granularities":[156],"data.":[159]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}