{"id":"https://openalex.org/W7127045554","doi":"https://doi.org/10.1145/3784833.3784861","title":"Dynamic Prediction of Shared Bikes Based on the MTConvLSTM Model With Dynamic Learning Rate Adjustment","display_name":"Dynamic Prediction of Shared Bikes Based on the MTConvLSTM Model With Dynamic Learning Rate Adjustment","publication_year":2025,"publication_date":"2025-11-12","ids":{"openalex":"https://openalex.org/W7127045554","doi":"https://doi.org/10.1145/3784833.3784861"},"language":null,"primary_location":{"id":"doi:10.1145/3784833.3784861","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3784833.3784861","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 11th International Conference on Communication and Information Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3784833.3784861","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124738704","display_name":"ShengRan Ye","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136246","display_name":"China Telecom (China)","ror":"https://ror.org/03jgnzt20","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210136246"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"ShengRan Ye","raw_affiliation_strings":["China Telecom Group Co., Ltd. Beijing Branch, Beijing, China"],"affiliations":[{"raw_affiliation_string":"China Telecom Group Co., Ltd. Beijing Branch, Beijing, China","institution_ids":["https://openalex.org/I4210136246"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124701962","display_name":"Zirui Fang","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zirui Fang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124706560","display_name":"Junjie Li","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junjie Li","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124709289","display_name":"Junyu Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junyu Chen","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124724296","display_name":"Yumei Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yumei Wang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Bejing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Bejing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124741797","display_name":"Hao Ji","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136246","display_name":"China Telecom (China)","ror":"https://ror.org/03jgnzt20","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210136246"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Ji","raw_affiliation_strings":["China Telecom Group Co., Ltd. Beijing Branch, Beijing, China"],"affiliations":[{"raw_affiliation_string":"China Telecom Group Co., Ltd. Beijing Branch, Beijing, China","institution_ids":["https://openalex.org/I4210136246"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5124738704"],"corresponding_institution_ids":["https://openalex.org/I4210136246"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.68375499,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"495","last_page":"499"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.6498000025749207,"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":0.6498000025749207,"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/T10298","display_name":"Urban Transport and Accessibility","score":0.09650000184774399,"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":0.053300000727176666,"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/resource","display_name":"Resource (disambiguation)","score":0.5203999876976013},{"id":"https://openalex.org/keywords/incremental-learning","display_name":"Incremental learning","score":0.492000013589859},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.45989999175071716},{"id":"https://openalex.org/keywords/order","display_name":"Order (exchange)","score":0.3781000077724457},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3610000014305115},{"id":"https://openalex.org/keywords/resource-allocation","display_name":"Resource allocation","score":0.34220001101493835},{"id":"https://openalex.org/keywords/dynamic-network-analysis","display_name":"Dynamic network analysis","score":0.33250001072883606}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7016000151634216},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.5203999876976013},{"id":"https://openalex.org/C2780735816","wikidata":"https://www.wikidata.org/wiki/Q28324931","display_name":"Incremental learning","level":2,"score":0.492000013589859},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4643999934196472},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.45989999175071716},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3978999853134155},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.3781000077724457},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3610000014305115},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.34220001101493835},{"id":"https://openalex.org/C13540734","wikidata":"https://www.wikidata.org/wiki/Q5318996","display_name":"Dynamic network analysis","level":2,"score":0.33250001072883606},{"id":"https://openalex.org/C49545453","wikidata":"https://www.wikidata.org/wiki/Q69883","display_name":"Urban planning","level":2,"score":0.33169999718666077},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.3077999949455261},{"id":"https://openalex.org/C2986442093","wikidata":"https://www.wikidata.org/wiki/Q1034047","display_name":"Traffic planning","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C2780609101","wikidata":"https://www.wikidata.org/wiki/Q17156588","display_name":"Resource management (computing)","level":2,"score":0.303600013256073},{"id":"https://openalex.org/C114809511","wikidata":"https://www.wikidata.org/wiki/Q1412924","display_name":"Flow network","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C3017409249","wikidata":"https://www.wikidata.org/wiki/Q23582796","display_name":"Resource use","level":2,"score":0.2711000144481659},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.27079999446868896}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3784833.3784861","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3784833.3784861","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 11th International Conference on Communication and Information Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3784833.3784861","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3784833.3784861","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 11th International Conference on Communication and Information Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.7853887677192688,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2788114581","https://openalex.org/W2911662370","https://openalex.org/W2949995781","https://openalex.org/W3006043023","https://openalex.org/W3016406232","https://openalex.org/W3131981721","https://openalex.org/W3187845984","https://openalex.org/W4205101288","https://openalex.org/W4205846614","https://openalex.org/W4221058635","https://openalex.org/W4308080441","https://openalex.org/W4361862417","https://openalex.org/W4389314060","https://openalex.org/W4393034711","https://openalex.org/W4393034800","https://openalex.org/W4403123833","https://openalex.org/W4412514822"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,36,43,63,93,102,110],"development":[2],"of":[3,12,18,40,47,66,96,106],"technology,":[4],"shared":[5,19,107],"bikes":[6,20],"have":[7],"become":[8],"an":[9],"indispensable":[10],"part":[11],"people\u2019s":[13],"lives.":[14],"The":[15],"dynamic":[16,94,114],"prediction":[17,65],"is":[21],"crucial":[22],"for":[23,35,141],"urban":[24,142,147],"traffic":[25,143,148],"planning":[26,144,149],"and":[27,38,45,57,68,89,91,104,145,150],"resource":[28,151],"optimization.":[29],"Existing":[30],"deep":[31,80],"learning-based":[32],"methods,":[33],"whether":[34],"optimization":[37],"adjustment":[39,117],"density":[41,88,103],"or":[42],"diversion":[44],"integration":[46],"flow,":[48,90],"can":[49],"only":[50],"make":[51],"partial":[52],"predictions,":[53],"which":[54],"are":[55],"limited":[56],"unable":[58],"to":[59,72,86,99,123],"take":[60],"into":[61],"account":[62],"overall":[64],"flow":[67,105],"density.":[69],"In":[70],"order":[71],"solve":[73],"these":[74],"problems,":[75],"we":[76],"use":[77],"a":[78,113],"multi-task":[79],"learning":[81,115],"framework":[82],"based":[83],"on":[84],"ConvLSTM":[85],"correlate":[87],"combines":[92],"characteristics":[95],"spatio-temporal":[97],"data":[98],"efficiently":[100],"predict":[101],"bicycles.":[108],"At":[109],"same":[111],"time,":[112],"rate":[116],"strategy":[118],"has":[119],"also":[120],"been":[121],"incorporated":[122],"enhance":[124],"training":[125],"efficiency.":[126],"Experiments":[127],"show":[128],"that":[129],"this":[130],"model":[131],"outperforms":[132],"existing":[133],"methods":[134],"in":[135],"multiple":[136],"metrics,":[137],"providing":[138],"reliable":[139],"support":[140],"facilitating":[146],"allocation.":[152]},"counts_by_year":[],"updated_date":"2026-02-06T02:01:19.302388","created_date":"2026-02-03T00:00:00"}
