{"id":"https://openalex.org/W4392712994","doi":"https://doi.org/10.1145/3638985.3639022","title":"Analyzing the Temporal and Spatial Characteristics of Public Transit Passengers' Travel Behavior Using Multiple Logit Models","display_name":"Analyzing the Temporal and Spatial Characteristics of Public Transit Passengers' Travel Behavior Using Multiple Logit Models","publication_year":2023,"publication_date":"2023-12-14","ids":{"openalex":"https://openalex.org/W4392712994","doi":"https://doi.org/10.1145/3638985.3639022"},"language":"en","primary_location":{"id":"doi:10.1145/3638985.3639022","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3638985.3639022","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 11th International Conference on Information Technology: IoT and Smart City","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/A5101768116","display_name":"Liye Zhang","orcid":"https://orcid.org/0000-0003-0965-2374"},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Liye Zhang","raw_affiliation_strings":["Shandong University Of Science And Technology, China"],"affiliations":[{"raw_affiliation_string":"Shandong University Of Science And Technology, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103133290","display_name":"Yan Lu","orcid":"https://orcid.org/0009-0003-7148-1075"},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Lu","raw_affiliation_strings":["Shandong University Of Science And Technology, China"],"affiliations":[{"raw_affiliation_string":"Shandong University Of Science And Technology, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077154020","display_name":"Zhicheng Ma","orcid":"https://orcid.org/0009-0002-6572-2912"},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhicheng Ma","raw_affiliation_strings":["Shandong University Of Science And Technology, China"],"affiliations":[{"raw_affiliation_string":"Shandong University Of Science And Technology, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058535372","display_name":"Ying Wei","orcid":"https://orcid.org/0009-0004-8480-4600"},"institutions":[{"id":"https://openalex.org/I4210136834","display_name":"Guangxi Transportation Research Institute","ror":"https://ror.org/02w9cqe69","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136834"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Wei","raw_affiliation_strings":["GuangXi Transport Vocational And Technical College, China"],"affiliations":[{"raw_affiliation_string":"GuangXi Transport Vocational And Technical College, China","institution_ids":["https://openalex.org/I4210136834"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101768116"],"corresponding_institution_ids":["https://openalex.org/I80143920"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16484711,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"223","last_page":"228"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9934999942779541,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9934999942779541,"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":0.9897000193595886,"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/T10298","display_name":"Urban Transport and Accessibility","score":0.9884999990463257,"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/public-transport","display_name":"Public transport","score":0.7219597101211548},{"id":"https://openalex.org/keywords/transit","display_name":"Transit (satellite)","score":0.5729409456253052},{"id":"https://openalex.org/keywords/logit","display_name":"Logit","score":0.5475732088088989},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5402211546897888},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.505821943283081},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.49193429946899414},{"id":"https://openalex.org/keywords/travel-behavior","display_name":"Travel behavior","score":0.4619344472885132},{"id":"https://openalex.org/keywords/travel-time","display_name":"Travel time","score":0.4224199652671814},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.39170950651168823},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3639771342277527},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1687096357345581},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.13807284832000732},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12263122200965881}],"concepts":[{"id":"https://openalex.org/C539828613","wikidata":"https://www.wikidata.org/wiki/Q178512","display_name":"Public transport","level":2,"score":0.7219597101211548},{"id":"https://openalex.org/C2778022998","wikidata":"https://www.wikidata.org/wiki/Q651136","display_name":"Transit (satellite)","level":3,"score":0.5729409456253052},{"id":"https://openalex.org/C140331021","wikidata":"https://www.wikidata.org/wiki/Q1868104","display_name":"Logit","level":2,"score":0.5475732088088989},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5402211546897888},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.505821943283081},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.49193429946899414},{"id":"https://openalex.org/C144072006","wikidata":"https://www.wikidata.org/wiki/Q4462116","display_name":"Travel behavior","level":2,"score":0.4619344472885132},{"id":"https://openalex.org/C2985733770","wikidata":"https://www.wikidata.org/wiki/Q1233007","display_name":"Travel time","level":2,"score":0.4224199652671814},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.39170950651168823},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3639771342277527},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1687096357345581},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.13807284832000732},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12263122200965881}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3638985.3639022","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3638985.3639022","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 11th International Conference on Information Technology: IoT and Smart City","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5,"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":6,"referenced_works":["https://openalex.org/W2092351313","https://openalex.org/W2167249443","https://openalex.org/W2889960786","https://openalex.org/W3112039401","https://openalex.org/W3209612995","https://openalex.org/W4292870482"],"related_works":["https://openalex.org/W2052743154","https://openalex.org/W3162329824","https://openalex.org/W4388420020","https://openalex.org/W4238517002","https://openalex.org/W624185289","https://openalex.org/W2126734908","https://openalex.org/W1520414485","https://openalex.org/W2156514048","https://openalex.org/W2316305435","https://openalex.org/W2380183412"],"abstract_inverted_index":{"The":[0,115],"accelerating":[1],"process":[2],"of":[3,44,52,96,111,164],"urbanization":[4],"has":[5,56],"deepened":[6],"the":[7,50,84,87,99,108,124,162,169],"contradiction":[8],"between":[9],"urban":[10],"traffic":[11],"supply":[12],"and":[13,33,66,93,126,146],"residents\u2019":[14],"travel":[15,54,88,109,139,147,151],"demand,":[16],"posing":[17],"significant":[18],"challenges":[19],"to":[20,82,174],"public":[21,112,165],"transportation":[22],"within":[23],"cities.":[24,46],"Currently,":[25],"smart":[26,34,45],"city":[27],"development":[28],"is":[29,157],"being":[30],"promoted":[31],"nationwide,":[32],"mobility":[35],"plays":[36],"a":[37,158],"pivotal":[38],"role":[39],"as":[40],"an":[41],"integral":[42],"component":[43],"Utilizing":[47,71],"big":[48,67],"data,":[49],"study":[51,61],"passenger":[53],"behavior":[55,110],"been":[57],"widely":[58],"applied.":[59],"This":[60],"integrates":[62],"multiple":[63],"Logit":[64],"models":[65],"data":[68,73],"visualization":[69],"methods.":[70],"card-swiping":[72],"from":[74,80,144],"May":[75],"2020,":[76],"recorded":[77],"every":[78],"day":[79],"4:00":[81],"23:00,":[83],"research":[85],"analyzes":[86],"time,":[89],"frequency,":[90],"influencing":[91],"factors,":[92],"spatial":[94],"distribution":[95],"residents":[97],"in":[98,161,168],"West":[100],"Coast":[101],"New":[102],"Area.":[103],"It":[104],"delves":[105],"deeply":[106],"into":[107],"transit":[113,166],"passengers.":[114],"findings":[116],"revealed":[117],"that":[118],"during":[119,123,135],"weekdays,":[120],"passengers":[121,142,167],"traveling":[122],"morning":[125],"evening":[127],"peaks":[128],"are":[129],"1.33":[130],"times":[131],"more":[132],"than":[133],"those":[134],"weekends.":[136],"Age,":[137],"peak":[138],"times,":[140],"whether":[141],"benefit":[143],"discounts,":[145],"distance":[148],"significantly":[149],"impact":[150],"frequency.":[152],"Furthermore,":[153],"on":[154],"weekends,":[155],"there":[156],"21%":[159],"increase":[160],"number":[163],"eastern":[170],"coastal":[171],"regions":[172],"compared":[173],"weekdays.":[175]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}