{"id":"https://openalex.org/W7134912924","doi":"https://doi.org/10.1109/comsnets67989.2026.11418228","title":"Time Series Forecasting of Public Bicycle Demand Using Neural Networks in an Indian Tier-II City","display_name":"Time Series Forecasting of Public Bicycle Demand Using Neural Networks in an Indian Tier-II City","publication_year":2026,"publication_date":"2026-01-06","ids":{"openalex":"https://openalex.org/W7134912924","doi":"https://doi.org/10.1109/comsnets67989.2026.11418228"},"language":null,"primary_location":{"id":"doi:10.1109/comsnets67989.2026.11418228","is_oa":false,"landing_page_url":"https://doi.org/10.1109/comsnets67989.2026.11418228","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 18th International Conference on COMmunication Systems and NETworks (COMSNETS)","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/A5067052561","display_name":"Pragya Verma","orcid":"https://orcid.org/0000-0002-5722-0894"},"institutions":[{"id":"https://openalex.org/I42014448","display_name":"Sardar Vallabhbhai National Institute of Technology Surat","ror":"https://ror.org/02y394t43","country_code":"IN","type":"education","lineage":["https://openalex.org/I42014448"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Pragya Verma","raw_affiliation_strings":["SVNIT,Department Of Civil Engineering,Surat,India,395007"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SVNIT,Department Of Civil Engineering,Surat,India,395007","institution_ids":["https://openalex.org/I42014448"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073177135","display_name":"Shriniwas Arkatkar","orcid":"https://orcid.org/0000-0002-1804-9465"},"institutions":[{"id":"https://openalex.org/I42014448","display_name":"Sardar Vallabhbhai National Institute of Technology Surat","ror":"https://ror.org/02y394t43","country_code":"IN","type":"education","lineage":["https://openalex.org/I42014448"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shriniwas Arkatkar","raw_affiliation_strings":["SVNIT,Department Of Civil Engineering,Surat,India,395007"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SVNIT,Department Of Civil Engineering,Surat,India,395007","institution_ids":["https://openalex.org/I42014448"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047974433","display_name":"Chetan R. Patel","orcid":"https://orcid.org/0000-0003-3395-7856"},"institutions":[{"id":"https://openalex.org/I42014448","display_name":"Sardar Vallabhbhai National Institute of Technology Surat","ror":"https://ror.org/02y394t43","country_code":"IN","type":"education","lineage":["https://openalex.org/I42014448"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Chetan R. Patel","raw_affiliation_strings":["SVNIT,Department Of Civil Engineering,Surat,India,395007"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SVNIT,Department Of Civil Engineering,Surat,India,395007","institution_ids":["https://openalex.org/I42014448"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5067052561"],"corresponding_institution_ids":["https://openalex.org/I42014448"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.46752028,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"721","last_page":"726"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.6377000212669373,"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":0.6377000212669373,"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":0.03709999844431877,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.02329999953508377,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.621999979019165},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5681999921798706},{"id":"https://openalex.org/keywords/demand-forecasting","display_name":"Demand forecasting","score":0.5011000037193298},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.41449999809265137},{"id":"https://openalex.org/keywords/economic-forecasting","display_name":"Economic forecasting","score":0.29600000381469727}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.621999979019165},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5681999921798706},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5449000000953674},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.5011000037193298},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.41449999809265137},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38749998807907104},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3294000029563904},{"id":"https://openalex.org/C163068380","wikidata":"https://www.wikidata.org/wiki/Q3409313","display_name":"Economic forecasting","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.2921000123023987},{"id":"https://openalex.org/C539828613","wikidata":"https://www.wikidata.org/wiki/Q178512","display_name":"Public transport","level":2,"score":0.2915000021457672},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2802000045776367},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.2718000113964081},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26739999651908875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/comsnets67989.2026.11418228","is_oa":false,"landing_page_url":"https://doi.org/10.1109/comsnets67989.2026.11418228","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 18th International Conference on COMmunication Systems and NETworks (COMSNETS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1993676537","https://openalex.org/W2058481364","https://openalex.org/W2068864504","https://openalex.org/W2085092470","https://openalex.org/W2150152686","https://openalex.org/W2151828736","https://openalex.org/W2237480058","https://openalex.org/W2425858923","https://openalex.org/W2621961497","https://openalex.org/W2883073525","https://openalex.org/W2897467070","https://openalex.org/W2901504064","https://openalex.org/W2980806123","https://openalex.org/W4205842095","https://openalex.org/W4283399792","https://openalex.org/W4288808017","https://openalex.org/W4322213370","https://openalex.org/W4324149224","https://openalex.org/W4381847896","https://openalex.org/W4384156387","https://openalex.org/W4384450936","https://openalex.org/W4386860093","https://openalex.org/W4387678090","https://openalex.org/W4388878661","https://openalex.org/W4404252808","https://openalex.org/W4404564689","https://openalex.org/W4408412751"],"related_works":[],"abstract_inverted_index":{"Public":[0],"Bicycle":[1],"Sharing":[2],"(PBS)":[3],"systems":[4],"have":[5],"become":[6],"a":[7,21],"sustainable":[8],"and":[9,51,71,83,101,118,124,172],"efficient":[10],"mode":[11],"of":[12,35,137,148],"urban":[13],"transportation,":[14],"yet":[15],"accurately":[16],"forecasting":[17,159],"their":[18],"demand":[19],"remains":[20],"key":[22],"challenge":[23],"for":[24,155,166],"optimal":[25],"resource":[26],"allocation.":[27],"This":[28],"study":[29],"focuses":[30],"on":[31],"time":[32,77,157],"series":[33,158],"prediction":[34,90],"PBS":[36,62,161],"ridership":[37],"using":[38,96],"advanced":[39],"neural":[40],"network":[41],"architectures,":[42],"Recurrent":[43,53],"Neural":[44],"Networks":[45],"(RNNs),":[46],"Long":[47],"Short-Term":[48],"Memory":[49],"(LSTM),":[50],"Gated":[52],"Units":[54],"(GRU),":[55],"to":[56,67,88],"model":[57,106,128],"temporal":[58],"dependencies":[59],"in":[60,160,175],"Ranchi\u2019s":[61],"data":[63],"spanning":[64],"January":[65],"2022":[66],"April":[68],"2024.":[69],"Temporal":[70],"contextual":[72],"variables":[73],"such":[74,152],"as":[75,153],"4-hour":[76],"slots,":[78],"day-of-week":[79],"patterns,":[80],"seasonal":[81],"trends,":[82],"weather":[84],"parameters":[85],"were":[86],"incorporated":[87],"enhance":[89],"accuracy.":[91],"Model":[92],"performance":[93],"was":[94],"evaluated":[95],"MSE,":[97],"RMSE,":[98],"MAPE,":[99],"MAD,":[100],"MAE":[102,116],"metrics.":[103],"The":[104,126,143],"RNN":[105],"outperformed":[107],"the":[108,111,133,146],"others,":[109],"achieving":[110],"lowest":[112],"test":[113,141],"RMSE":[114],"(58.56),":[115],"(45.87),":[117],"MAPE":[119],"(26.28),":[120],"indicating":[121],"superior":[122],"generalization":[123],"stability.":[125],"GRU":[127],"demonstrated":[129],"comparable":[130],"accuracy,":[131],"while":[132],"LSTM":[134],"showed":[135],"signs":[136],"overfitting":[138],"with":[139],"higher":[140],"errors.":[142],"results":[144],"confirm":[145],"effectiveness":[147],"lightweight":[149],"recurrent":[150],"architectures":[151],"RNNs":[154],"real-time":[156],"systems,":[162],"offering":[163],"valuable":[164],"insights":[165],"improving":[167],"bike":[168],"availability,":[169],"operational":[170],"efficiency,":[171],"user":[173],"satisfaction":[174],"smart":[176],"cities.":[177]},"counts_by_year":[],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2026-03-12T00:00:00"}