{"id":"https://openalex.org/W2912260060","doi":"https://doi.org/10.3233/978-1-61499-939-3-296","title":"Prediction of Passenger Traffic Volume Sharing Rate Based on Logit Model","display_name":"Prediction of Passenger Traffic Volume Sharing Rate Based on Logit Model","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2912260060","doi":"https://doi.org/10.3233/978-1-61499-939-3-296","mag":"2912260060"},"language":"en","primary_location":{"id":"doi:10.3233/978-1-61499-939-3-296","is_oa":false,"landing_page_url":"https://doi.org/10.3233/978-1-61499-939-3-296","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"type":"book-chapter","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/A5100324905","display_name":"Rong Wang","orcid":"https://orcid.org/0000-0003-1962-0165"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Wang Rong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051991024","display_name":"Tong Zhang","orcid":"https://orcid.org/0000-0001-7108-0974"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang Tong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064564392","display_name":"Song Liu","orcid":"https://orcid.org/0000-0001-7827-0359"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu Song","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5101748146","display_name":"Zeying Zhang","orcid":"https://orcid.org/0000-0003-3016-9248"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang Zeying","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100324905"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8119,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.62972454,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10698","display_name":"Transportation Planning and Optimization","score":0.9114000201225281,"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"}},"topics":[{"id":"https://openalex.org/T10698","display_name":"Transportation Planning and Optimization","score":0.9114000201225281,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9111999869346619,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/logit","display_name":"Logit","score":0.6243762969970703},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.6204354166984558},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.5487044453620911},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5257533192634583},{"id":"https://openalex.org/keywords/traffic-volume","display_name":"Traffic volume","score":0.5159464478492737},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3852658271789551},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3255496621131897},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.25040048360824585},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.228834867477417},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1906513273715973},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17155465483665466},{"id":"https://openalex.org/keywords/thermodynamics","display_name":"Thermodynamics","score":0.03834792971611023},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.03817856311798096}],"concepts":[{"id":"https://openalex.org/C140331021","wikidata":"https://www.wikidata.org/wiki/Q1868104","display_name":"Logit","level":2,"score":0.6243762969970703},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.6204354166984558},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.5487044453620911},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5257533192634583},{"id":"https://openalex.org/C168443057","wikidata":"https://www.wikidata.org/wiki/Q7001223","display_name":"Traffic volume","level":2,"score":0.5159464478492737},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3852658271789551},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3255496621131897},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.25040048360824585},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.228834867477417},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1906513273715973},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17155465483665466},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.03834792971611023},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.03817856311798096}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/978-1-61499-939-3-296","is_oa":false,"landing_page_url":"https://doi.org/10.3233/978-1-61499-939-3-296","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2798121181","https://openalex.org/W2129863591","https://openalex.org/W2009111155","https://openalex.org/W2366749544","https://openalex.org/W2077865273","https://openalex.org/W2559245114","https://openalex.org/W2145256004","https://openalex.org/W1974047397","https://openalex.org/W1989125082","https://openalex.org/W2052870469"],"abstract_inverted_index":{"Inter-city":[0],"railways":[1],"facilitate":[2],"the":[3,10,18,28,35,44,49,68,73,88,93,109,128,144,154,169,173],"accumulation":[4],"of":[5,38,41,76,92,127,143,172],"labor":[6],"and":[7,20,48,79,103,119,134,138,147,160],"capital":[8],"in":[9,22,43,96,107,113,132],"site":[11],"areas":[12],"through":[13],"high-speed,":[14],"large-capacity":[15],"transportation,":[16],"accelerating":[17],"industrialization":[19],"urbanization":[21],"these":[23],"areas.":[24],"In":[25],"this":[26,97],"paper,":[27],"logit":[29],"model":[30,52,69],"is":[31,53,85,99,115,136,151],"used":[32],"to":[33,72],"calculate":[34],"sharing":[36,145],"rate":[37,91,112,146],"various":[39],"modes":[40],"transportation":[42,46],"intercity":[45,94],"channel,":[47],"utility":[50],"function":[51],"established.":[54],"The":[55,123,140],"parameters":[56],"are":[57],"determined":[58],"by":[59],"linear":[60],"regression":[61],"method":[62],"combined":[63],"with":[64],"questionnaire":[65],"data.":[66],"Then":[67],"was":[70],"applied":[71],"inter-city":[74,130,175],"channel":[75,98],"Changsha,":[77],"Zhuzhou":[78],"Xiangtan":[80],"for":[81,168],"empirical":[82],"analysis.":[83],"It":[84,163],"calculated":[86],"that":[87],"passenger":[89,110,124,148],"transfer":[90,111],"railway":[95,131],"30.89%":[100],"(low":[101,117],"plan)":[102,106,118],"32.86%":[104],"(high":[105,121],"2018;":[108],"2020":[114,135],"37.21%":[116],"39.58%":[120],"plan).":[122],"traffic":[125],"volume":[126],"Changsha-Zhuzhou-Xiangtan":[129,174],"2018":[133],"forecasted":[137],"analyzed.":[139],"sensitivity":[141],"analysis":[142],"flow":[149],"growth":[150],"based":[152],"on":[153],"fare,":[155],"departure":[156],"interval,":[157],"travel":[158],"time":[159],"connection":[161],"time.":[162],"provides":[164],"a":[165],"theoretical":[166],"basis":[167],"future":[170],"adjustment":[171],"railway.":[176]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}