{"id":"https://openalex.org/W4385299193","doi":"https://doi.org/10.1145/3603781.3603842","title":"Passenger Flow Prediction of Tianjin Metro Line 3 under Time Series Clustering","display_name":"Passenger Flow Prediction of Tianjin Metro Line 3 under Time Series Clustering","publication_year":2023,"publication_date":"2023-05-26","ids":{"openalex":"https://openalex.org/W4385299193","doi":"https://doi.org/10.1145/3603781.3603842"},"language":"en","primary_location":{"id":"doi:10.1145/3603781.3603842","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3603781.3603842","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things","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/A5101405366","display_name":"Zhao Wang","orcid":"https://orcid.org/0009-0006-8796-8154"},"institutions":[{"id":"https://openalex.org/I136765683","display_name":"Tianjin University of Technology","ror":"https://ror.org/00zbe0w13","country_code":"CN","type":"education","lineage":["https://openalex.org/I136765683"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhao Wang","raw_affiliation_strings":["Tianjin University of Technology, China"],"raw_orcid":"https://orcid.org/0009-0006-8796-8154","affiliations":[{"raw_affiliation_string":"Tianjin University of Technology, China","institution_ids":["https://openalex.org/I136765683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101405366"],"corresponding_institution_ids":["https://openalex.org/I136765683"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09732332,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"343","last_page":"348"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9995999932289124,"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":0.9995999932289124,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9988999962806702,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9983999729156494,"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/autoregressive-model","display_name":"Autoregressive model","score":0.6795519590377808},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6383644342422485},{"id":"https://openalex.org/keywords/autoregressive\u2013moving-average-model","display_name":"Autoregressive\u2013moving-average model","score":0.5632850527763367},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5373562574386597},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.48534002900123596},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.475553959608078},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.4590754210948944},{"id":"https://openalex.org/keywords/public-transport","display_name":"Public transport","score":0.4295576810836792},{"id":"https://openalex.org/keywords/urban-rail-transit","display_name":"Urban rail transit","score":0.42751574516296387},{"id":"https://openalex.org/keywords/rail-transit","display_name":"Rail transit","score":0.41118359565734863},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.3064255118370056},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.2973342537879944},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.2785719633102417},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24195316433906555},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.17548424005508423},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.13187023997306824}],"concepts":[{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.6795519590377808},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6383644342422485},{"id":"https://openalex.org/C74883015","wikidata":"https://www.wikidata.org/wiki/Q290467","display_name":"Autoregressive\u2013moving-average model","level":3,"score":0.5632850527763367},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5373562574386597},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.48534002900123596},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.475553959608078},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.4590754210948944},{"id":"https://openalex.org/C539828613","wikidata":"https://www.wikidata.org/wiki/Q178512","display_name":"Public transport","level":2,"score":0.4295576810836792},{"id":"https://openalex.org/C2780434240","wikidata":"https://www.wikidata.org/wiki/Q3491904","display_name":"Urban rail transit","level":2,"score":0.42751574516296387},{"id":"https://openalex.org/C2992701429","wikidata":"https://www.wikidata.org/wiki/Q3491904","display_name":"Rail transit","level":2,"score":0.41118359565734863},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3064255118370056},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.2973342537879944},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2785719633102417},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24195316433906555},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.17548424005508423},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.13187023997306824},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3603781.3603842","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3603781.3603842","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.8399999737739563,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2528510132","https://openalex.org/W2975195404","https://openalex.org/W2983149012","https://openalex.org/W2990671891","https://openalex.org/W3013249682","https://openalex.org/W3037245145","https://openalex.org/W3041342006","https://openalex.org/W3049562621","https://openalex.org/W3187844357"],"related_works":["https://openalex.org/W199725827","https://openalex.org/W4248896451","https://openalex.org/W2011781613","https://openalex.org/W2953473307","https://openalex.org/W1980504576","https://openalex.org/W4394470125","https://openalex.org/W4388138713","https://openalex.org/W2033691733","https://openalex.org/W2903291457","https://openalex.org/W2004724997"],"abstract_inverted_index":{"Taking":[0],"Tianjin":[1],"Metro":[2],"Line":[3],"3":[4],"as":[5],"an":[6],"example,":[7],"this":[8],"study":[9],"mainly":[10],"explored":[11],"the":[12,18,30,38,73,83,91,94,99,103],"influence":[13],"of":[14,20,77,80,102,106],"forecasting":[15,24],"methods":[16,92],"on":[17,29],"accuracy":[19,96],"short-term":[21,74],"passenger":[22,35,75,100],"flow":[23,36,76,101],"for":[25],"rail":[26,48],"transit.":[27],"Based":[28],"station's":[31],"inbound":[32],"and":[33,54,63,82,113,115],"outbound":[34],"attributes,":[37],"time":[39],"sequence":[40],"clustering":[41],"method":[42],"was":[43],"applied":[44],"to":[45,71],"classify":[46],"urban":[47],"transit":[49],"stations":[50,107],"into":[51],"four":[52,104],"categories,":[53],"extreme":[55],"gradient":[56],"boosting":[57],"(XG":[58],"Boost),":[59],"back":[60],"propagation":[61],"(BP)":[62],"autoregressive":[64],"moving":[65],"average":[66],"(ARMA)":[67],"models":[68],"were":[69,85],"used":[70],"predict":[72],"each":[78],"type":[79],"stations,":[81],"results":[84,88],"compared.":[86],"The":[87],"show":[89],"that":[90],"with":[93],"highest":[95],"in":[97,122],"predicting":[98],"types":[105],"are":[108],"XG":[109,116],"Boost,":[110],"ARMA,":[111],"ARMA":[112],"BP,":[114],"Boost":[117],"shows":[118],"a":[119],"greater":[120],"advantage":[121],"prediction":[123],"time.":[124]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
