{"id":"https://openalex.org/W4401246758","doi":"https://doi.org/10.1109/tiv.2024.3436599","title":"XRMDN: An Extended Recurrent Mixture Density Network for Short-Term Probabilistic Rider Demand Forecasting Considering High Volatility","display_name":"XRMDN: An Extended Recurrent Mixture Density Network for Short-Term Probabilistic Rider Demand Forecasting Considering High Volatility","publication_year":2024,"publication_date":"2024-08-02","ids":{"openalex":"https://openalex.org/W4401246758","doi":"https://doi.org/10.1109/tiv.2024.3436599"},"language":"en","primary_location":{"id":"doi:10.1109/tiv.2024.3436599","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tiv.2024.3436599","pdf_url":null,"source":{"id":"https://openalex.org/S4210199657","display_name":"IEEE Transactions on Intelligent Vehicles","issn_l":"2379-8858","issn":["2379-8858","2379-8904"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Vehicles","raw_type":"journal-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/A5103645044","display_name":"Xiaoming Li","orcid":"https://orcid.org/0000-0002-1925-4495"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Xiaoming Li","raw_affiliation_strings":["Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montréal, QC, Canada"],"affiliations":[{"raw_affiliation_string":"Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montréal, QC, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044216950","display_name":"Hubert Normandin-Taillon","orcid":null},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Hubert Normandin-Taillon","raw_affiliation_strings":["Department of Computer Science and Software Engineering (CSSE), Concordia University, Montréal, QC, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Software Engineering (CSSE), Concordia University, Montréal, QC, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031175011","display_name":"Wang Chun","orcid":"https://orcid.org/0000-0001-6994-0146"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Chun Wang","raw_affiliation_strings":["Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montréal, QC, Canada"],"affiliations":[{"raw_affiliation_string":"Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montréal, QC, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101541230","display_name":"Xiao Huang","orcid":"https://orcid.org/0000-0002-5978-8280"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Xiao Huang","raw_affiliation_strings":["Concordia John Molson School of Business (JMSB), Concordia University, Montréal, QC, Canada"],"affiliations":[{"raw_affiliation_string":"Concordia John Molson School of Business (JMSB), Concordia University, Montréal, QC, Canada","institution_ids":["https://openalex.org/I60158472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5103645044"],"corresponding_institution_ids":["https://openalex.org/I60158472"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12348703,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"10","issue":"3","first_page":"1855","last_page":"1866"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9804999828338623,"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":0.9804999828338623,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9725000262260437,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9696999788284302,"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/probabilistic-logic","display_name":"Probabilistic logic","score":0.7730101346969604},{"id":"https://openalex.org/keywords/volatility","display_name":"Volatility (finance)","score":0.6968327760696411},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.613761842250824},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5603644847869873},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"Probabilistic forecasting","score":0.46305346488952637},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.3694290518760681},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.36183038353919983},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.14941221475601196},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07527974247932434}],"concepts":[{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7730101346969604},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.6968327760696411},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.613761842250824},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5603644847869873},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.46305346488952637},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.3694290518760681},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.36183038353919983},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.14941221475601196},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07527974247932434},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tiv.2024.3436599","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tiv.2024.3436599","pdf_url":null,"source":{"id":"https://openalex.org/S4210199657","display_name":"IEEE Transactions on Intelligent Vehicles","issn_l":"2379-8858","issn":["2379-8858","2379-8904"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Vehicles","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.46000000834465027,"display_name":"Climate action"}],"awards":[{"id":"https://openalex.org/G5990886946","display_name":null,"funder_award_id":"RGPIN-2023-04818","funder_id":"https://openalex.org/F4320334593","funder_display_name":"Natural Sciences and Engineering Research Council of Canada"}],"funders":[{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W1975362087","https://openalex.org/W1979575715","https://openalex.org/W1999996900","https://openalex.org/W2016287239","https://openalex.org/W2029462653","https://openalex.org/W2064675550","https://openalex.org/W2066377449","https://openalex.org/W2106251367","https://openalex.org/W2135604164","https://openalex.org/W2695427614","https://openalex.org/W2695874637","https://openalex.org/W2772724270","https://openalex.org/W2791343342","https://openalex.org/W2885354784","https://openalex.org/W2889260000","https://openalex.org/W2909452395","https://openalex.org/W2912985636","https://openalex.org/W2927719797","https://openalex.org/W2980994438","https://openalex.org/W2982492442","https://openalex.org/W2998652672","https://openalex.org/W3018119453","https://openalex.org/W3020398622","https://openalex.org/W3092020257","https://openalex.org/W3119249947","https://openalex.org/W3128704584","https://openalex.org/W3140863171","https://openalex.org/W3199509052","https://openalex.org/W4200210082","https://openalex.org/W4237748447","https://openalex.org/W4290945572","https://openalex.org/W4294643517","https://openalex.org/W4306965360","https://openalex.org/W4315926508","https://openalex.org/W4317733620","https://openalex.org/W4378575402","https://openalex.org/W4378975792","https://openalex.org/W4383745888","https://openalex.org/W4385245566","https://openalex.org/W4385988005","https://openalex.org/W4387496398","https://openalex.org/W4387717615","https://openalex.org/W4392523490","https://openalex.org/W4400762160","https://openalex.org/W6631190155","https://openalex.org/W6634817459","https://openalex.org/W6685562342","https://openalex.org/W6745609711","https://openalex.org/W6752968661"],"related_works":["https://openalex.org/W2909436466","https://openalex.org/W2769304616","https://openalex.org/W2561944894","https://openalex.org/W2753588648","https://openalex.org/W3188413760","https://openalex.org/W2963188571","https://openalex.org/W2008291043","https://openalex.org/W2373467473","https://openalex.org/W2791458617","https://openalex.org/W3121565704"],"abstract_inverted_index":{"In":[0,67],"the":[1,7,43,61,104,118,150,168,180,189,203],"realm":[2],"of":[3,9,45,107,179],"Mobility-on-Demand":[4],"(MoD)":[5],"systems,":[6],"forecasting":[8,49,53,132,197],"rider":[10],"demand":[11,22,65,95,127,196],"is":[12],"a":[13,78,90,199,206],"cornerstone":[14],"for":[15,103,209],"operational":[16,211],"decision-making":[17],"and":[18,30,36,97,109,121,175,184,213],"system":[19],"optimization.":[20],"Rider":[21],"levels":[23],"are":[24],"profoundly":[25],"influenced":[26],"by":[27],"both":[28],"endogenous":[29,108],"exogenous":[31,110],"factors,":[32],"leading":[33],"to":[34,84,93,172,188,202],"high":[35],"dynamic":[37],"volatility.":[38],"This":[39,112,192],"volatility":[40,162],"significantly":[41,130],"undermines":[42],"efficacy":[44],"conventional":[46],"time":[47],"series":[48],"methods.":[50],"Furthermore,":[51],"traditional":[52],"methodologies":[54],"primarily":[55],"yield":[56],"point":[57],"estimates,":[58],"thereby":[59],"neglecting":[60],"inherent":[62],"uncertainty":[63],"within":[64,117],"projections.":[66],"response,":[68],"we":[69],"propose":[70],"an":[71],"Extended":[72],"Recurrent":[73],"Mixture":[74],"Density":[75],"Network":[76],"(XRMDN),":[77],"novel":[79],"deep":[80],"learning":[81],"framework":[82],"engineered":[83],"address":[85],"these":[86],"challenges.":[87],"XRMDN":[88,148,166],"leverages":[89],"sophisticated":[91],"architecture":[92],"process":[94],"residuals":[96],"variance":[98,122],"through":[99],"correlated":[100],"modules,":[101],"allowing":[102],"flexible":[105],"incorporation":[106],"data.":[111],"architecture,":[113],"featuring":[114],"recurrent":[115],"connections":[116],"weight,":[119],"mean,":[120],"neural":[123],"networks,":[124],"adeptly":[125],"captures":[126],"trends,":[128],"thus":[129],"enhancing":[131,210],"precision,":[133],"particularly":[134],"in":[135,160,177,194,216],"high-volatility":[136],"scenarios.":[137],"Our":[138],"comprehensive":[139],"experimental":[140],"analysis,":[141],"utilizing":[142],"real-world":[143],"MoD":[144,217],"datasets,":[145],"demonstrates":[146],"that":[147],"surpasses":[149],"existing":[151],"benchmark":[152,169,190],"models":[153,170],"across":[154],"various":[155],"evaluation":[156],"metrics,":[157],"notably":[158],"excelling":[159],"high-demand":[161],"contexts.":[163],"Most":[164],"importantly,":[165],"outperforms":[167],"up":[171],"93.0%,":[173],"72.7%,":[174],"31.3%":[176],"terms":[178],"log-likelihood":[181],"value,":[182],"MAPE,":[183],"rejection":[185],"rates":[186],"compared":[187],"models.":[191],"advancement":[193],"probabilistic":[195],"marks":[198],"significant":[200],"contribution":[201],"field,":[204],"offering":[205],"robust":[207],"tool":[208],"efficiency":[212],"customer":[214],"satisfaction":[215],"systems.":[218]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}