{"id":"https://openalex.org/W4417168902","doi":"https://doi.org/10.26599/bdma.2025.9020069","title":"Multiresolution Taxi Demand Prediction: A Big Data Statistical and Zero-Inflated Spatiotemporal GNN Approach","display_name":"Multiresolution Taxi Demand Prediction: A Big Data Statistical and Zero-Inflated Spatiotemporal GNN Approach","publication_year":2025,"publication_date":"2025-12-09","ids":{"openalex":"https://openalex.org/W4417168902","doi":"https://doi.org/10.26599/bdma.2025.9020069"},"language":null,"primary_location":{"id":"doi:10.26599/bdma.2025.9020069","is_oa":true,"landing_page_url":"https://doi.org/10.26599/bdma.2025.9020069","pdf_url":null,"source":{"id":"https://openalex.org/S4210209060","display_name":"Big Data Mining and Analytics","issn_l":"2096-0654","issn":["2096-0654","2097-406X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311901","host_organization_name":"Tsinghua University Press","host_organization_lineage":["https://openalex.org/P4310311901","https://openalex.org/P4353108601"],"host_organization_lineage_names":["Tsinghua University Press","Tsinghua University"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data Mining and Analytics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.26599/bdma.2025.9020069","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101694397","display_name":"Yifei Shen","orcid":"https://orcid.org/0000-0003-4745-9145"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yifei Shen","raw_affiliation_strings":["School of Data Science, Lingnan University,Hong Kong,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Data Science, Lingnan University,Hong Kong,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034590629","display_name":"Wenlong Shi","orcid":"https://orcid.org/0000-0002-8585-8174"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenlong Shi","raw_affiliation_strings":["College of Computer Science and Software Engineering, Shenzhen University,Shenzhen,China,518060"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Software Engineering, Shenzhen University,Shenzhen,China,518060","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101280521","display_name":"Jiaxing Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiaxing Shen","raw_affiliation_strings":["School of Data Science, Lingnan University,Hong Kong,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Data Science, Lingnan University,Hong Kong,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108050610","display_name":"Dandan Wang","orcid":"https://orcid.org/0000-0002-9584-8033"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hengzhi Wang","raw_affiliation_strings":["College of Computer Science and Software Engineering, Shenzhen University,Shenzhen,China,518060"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Software Engineering, Shenzhen University,Shenzhen,China,518060","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101500925","display_name":"Han\u2010Qing Wu","orcid":"https://orcid.org/0000-0001-6687-5270"},"institutions":[{"id":"https://openalex.org/I4210127430","display_name":"Lucky Technology (China)","ror":"https://ror.org/0341fmf68","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210127430"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanqing Wu","raw_affiliation_strings":["Lucky Technology Development Limited,Hong Kong,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lucky Technology Development Limited,Hong Kong,China","institution_ids":["https://openalex.org/I4210127430"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100740023","display_name":"Jiannong Cao","orcid":"https://orcid.org/0000-0002-2725-2529"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Jiannong Cao","raw_affiliation_strings":["The Hong Kong Polytechnic University,Department of Computing,Hong Kong,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic University,Department of Computing,Hong Kong,China","institution_ids":["https://openalex.org/I14243506"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4065,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.81553741,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"9","issue":"1","first_page":"39","last_page":"56"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.7598000168800354,"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":0.7598000168800354,"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/T11942","display_name":"Transportation and Mobility Innovations","score":0.17790000140666962,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"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":0.013399999588727951,"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/robustness","display_name":"Robustness (evolution)","score":0.5252000093460083},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.42309999465942383},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.41350001096725464},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4050000011920929},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.3840999901294708},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.3549000024795532},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.3440999984741211},{"id":"https://openalex.org/keywords/poisson-distribution","display_name":"Poisson distribution","score":0.33880001306533813},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.32409998774528503}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7132999897003174},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5252000093460083},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4952000081539154},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.42309999465942383},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.41350001096725464},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4050000011920929},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.3840999901294708},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37709999084472656},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.3549000024795532},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.3440999984741211},{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.33880001306533813},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.32409998774528503},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31520000100135803},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.2953999936580658},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.28780001401901245},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C139502532","wikidata":"https://www.wikidata.org/wiki/Q1122090","display_name":"Computational intelligence","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.26599/bdma.2025.9020069","is_oa":true,"landing_page_url":"https://doi.org/10.26599/bdma.2025.9020069","pdf_url":null,"source":{"id":"https://openalex.org/S4210209060","display_name":"Big Data Mining and Analytics","issn_l":"2096-0654","issn":["2096-0654","2097-406X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311901","host_organization_name":"Tsinghua University Press","host_organization_lineage":["https://openalex.org/P4310311901","https://openalex.org/P4353108601"],"host_organization_lineage_names":["Tsinghua University Press","Tsinghua University"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data Mining and Analytics","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.26599/bdma.2025.9020069","is_oa":true,"landing_page_url":"https://doi.org/10.26599/bdma.2025.9020069","pdf_url":null,"source":{"id":"https://openalex.org/S4210209060","display_name":"Big Data Mining and Analytics","issn_l":"2096-0654","issn":["2096-0654","2097-406X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311901","host_organization_name":"Tsinghua University Press","host_organization_lineage":["https://openalex.org/P4310311901","https://openalex.org/P4353108601"],"host_organization_lineage_names":["Tsinghua University Press","Tsinghua University"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data Mining and Analytics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6334489386","display_name":null,"funder_award_id":"DR25F4,DB24C4,871242","funder_id":"https://openalex.org/F4320328618","funder_display_name":"Lingnan University"}],"funders":[{"id":"https://openalex.org/F4320328618","display_name":"Lingnan University","ror":"https://ror.org/0563pg902"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W2113298739","https://openalex.org/W2550143307","https://openalex.org/W2695427614","https://openalex.org/W2743316574","https://openalex.org/W2756203131","https://openalex.org/W2782920454","https://openalex.org/W2788134583","https://openalex.org/W2791343342","https://openalex.org/W2884128153","https://openalex.org/W2900682747","https://openalex.org/W2949732208","https://openalex.org/W2998652672","https://openalex.org/W3043748942","https://openalex.org/W3108897154","https://openalex.org/W3174686248","https://openalex.org/W3186458388","https://openalex.org/W4224314270","https://openalex.org/W4283770265","https://openalex.org/W4285184111","https://openalex.org/W4290945572","https://openalex.org/W4294351786","https://openalex.org/W4295699310","https://openalex.org/W4311415338","https://openalex.org/W4324145146","https://openalex.org/W4327566705","https://openalex.org/W4378975792","https://openalex.org/W4393128742","https://openalex.org/W4399154552","https://openalex.org/W4401872781","https://openalex.org/W4402307825","https://openalex.org/W4404861621","https://openalex.org/W4406265442"],"related_works":[],"abstract_inverted_index":{"Urban":[0],"taxi":[1],"demand":[2,57],"prediction":[3,166],"faces":[4],"a":[5,29],"critical":[6],"resolution":[7,167],"paradox:":[8],"high-resolution":[9],"forecasts":[10],"enable":[11],"operational":[12],"agility":[13],"but":[14],"suffer":[15],"from":[16,63],"extreme":[17],"sparsity-induced":[18],"volatility,":[19],"while":[20,150],"low-resolution":[21],"predictions":[22],"sacrifice":[23],"responsiveness":[24],"for":[25,111],"stability.":[26],"We":[27],"present":[28],"Scalable":[30],"SpatioTemporal":[31],"Zero-Inflated":[32,46],"Poisson":[33,47],"Graph":[34,85],"Neural":[35],"Network":[36],"(SSTZIP-GNN),":[37],"that":[38,50,70],"resolves":[39],"this":[40],"paradox":[41],"through":[42],"three":[43],"innovations:":[44],"(1)":[45],"(ZIP)":[48],"integration":[49],"explicitly":[51],"models":[52],"structural":[53],"zeros":[54],"in":[55],"sparse":[56],"distributions,":[58],"distinguishing":[59],"genuine":[60],"low-demand":[61],"periods":[62],"data":[64],"artifacts;":[65],"(2)":[66],"Adaptive":[67],"spatiotemporal":[68],"learning":[69],"dynamically":[71],"adjusts":[72],"kernel":[73],"dilation":[74],"factors":[75],"and":[76,89,105,181],"graph":[77],"diffusion":[78],"rates":[79],"across":[80,154,164,185],"temporal":[81],"resolutions":[82],"using":[83],"Diffusion":[84],"Convolutional":[86,91],"Networks":[87,92],"(DGCNs)":[88],"Temporal":[90],"(TCNs);":[93],"(3)":[94],"Multimodal":[95],"feature":[96],"fusion":[97],"incorporating":[98],"real-time":[99],"crowd-sourced":[100],"mobility":[101,124,183],"data,":[102],"socioeconomic":[103],"indicators,":[104],"Global":[106],"Position":[107],"System":[108],"(GPS)":[109],"trajectories":[110],"enhanced":[112],"robustness":[113],"under":[114],"variable":[115],"urban":[116,187],"conditions.":[117],"Extensive":[118],"evaluation":[119],"on":[120],"130":[121],"million":[122],"real-world":[123],"records":[125],"demonstrates":[126],"superior":[127],"performance,":[128],"achieving":[129],"34.8%":[130],"Mean":[131,159],"Absolute":[132],"Error":[133,161],"(MAE)":[134],"reduction":[135,163],"over":[136],"state-of-the-art":[137],"baselines.":[138],"The":[139],"model":[140],"reduces":[141],"computational":[142],"costs":[143],"by":[144],"46.3%":[145],"compared":[146],"to":[147,174],"ensemble":[148],"approaches":[149],"maintaining":[151],"high":[152],"accuracy":[153],"resolutions,":[155],"delivering":[156],"33.4%-53.3%":[157],"Root":[158],"Square":[160],"(RMSE)":[162],"different":[165],"scenarios.":[168],"This":[169],"unified":[170],"framework":[171],"enables":[172],"cities":[173],"implement":[175],"demand-responsive":[176],"fleet":[177],"management,":[178],"dynamic":[179],"pricing,":[180],"sustainable":[182],"planning":[184],"diverse":[186],"landscapes.":[188]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2026-08-15T07:11:24.734988","created_date":"2025-12-09T00:00:00"}