{"id":"https://openalex.org/W6959925621","doi":"https://doi.org/10.13016/tfzc-ae9c","title":"DEVELOPING A STATISTICAL VEHICLE DRIVER BEHAVIOR MODEL FOR ECO-ROUTING DEPLOYMENT","display_name":"DEVELOPING A STATISTICAL VEHICLE DRIVER BEHAVIOR MODEL FOR ECO-ROUTING DEPLOYMENT","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W6959925621","doi":"https://doi.org/10.13016/tfzc-ae9c"},"language":"en","primary_location":{"id":"doi:10.13016/tfzc-ae9c","is_oa":true,"landing_page_url":"https://doi.org/10.13016/tfzc-ae9c","pdf_url":null,"source":{"id":"https://openalex.org/S4306402644","display_name":"Digital Repository at the University of Maryland (University of Maryland College Park)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66946132","host_organization_name":"University of Maryland, College Park","host_organization_lineage":["https://openalex.org/I66946132"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Collection"},"type":"other","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.13016/tfzc-ae9c","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Zhou, Weiyi","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhou, Weiyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12095","display_name":"Vehicle emissions and performance","score":0.9124000072479248,"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"}},"topics":[{"id":"https://openalex.org/T12095","display_name":"Vehicle emissions and performance","score":0.9124000072479248,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.021900000050663948,"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/T10524","display_name":"Traffic control and management","score":0.01899999938905239,"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/energy-consumption","display_name":"Energy consumption","score":0.6787999868392944},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6237000226974487},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.6007000207901001},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.5490000247955322},{"id":"https://openalex.org/keywords/fuel-efficiency","display_name":"Fuel efficiency","score":0.5020999908447266},{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.49900001287460327},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.4494999945163727},{"id":"https://openalex.org/keywords/geospatial-analysis","display_name":"Geospatial analysis","score":0.412200003862381}],"concepts":[{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.6787999868392944},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6338000297546387},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6237000226974487},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.6007000207901001},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.5490000247955322},{"id":"https://openalex.org/C45882903","wikidata":"https://www.wikidata.org/wiki/Q5042317","display_name":"Fuel efficiency","level":2,"score":0.5020999908447266},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.49900001287460327},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.4494999945163727},{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.412200003862381},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.39640000462532043},{"id":"https://openalex.org/C2984118289","wikidata":"https://www.wikidata.org/wiki/Q29954","display_name":"Power consumption","level":3,"score":0.39559999108314514},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.3946000039577484},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.3684000074863434},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.3425999879837036},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3188000023365021},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.31630000472068787},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.31540000438690186},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.30230000615119934},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.301800012588501},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C30772137","wikidata":"https://www.wikidata.org/wiki/Q5164762","display_name":"Consumption (sociology)","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C158739034","wikidata":"https://www.wikidata.org/wiki/Q1907114","display_name":"Metropolitan area","level":2,"score":0.2809999883174896},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.2809999883174896},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2775000035762787},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26669999957084656},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.13016/tfzc-ae9c","is_oa":true,"landing_page_url":"https://doi.org/10.13016/tfzc-ae9c","pdf_url":null,"source":{"id":"https://openalex.org/S4306402644","display_name":"Digital Repository at the University of Maryland (University of Maryland College Park)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66946132","host_organization_name":"University of Maryland, College Park","host_organization_lineage":["https://openalex.org/I66946132"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Collection"}],"best_oa_location":{"id":"doi:10.13016/tfzc-ae9c","is_oa":true,"landing_page_url":"https://doi.org/10.13016/tfzc-ae9c","pdf_url":null,"source":{"id":"https://openalex.org/S4306402644","display_name":"Digital Repository at the University of Maryland (University of Maryland College Park)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66946132","host_organization_name":"University of Maryland, College Park","host_organization_lineage":["https://openalex.org/I66946132"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Collection"},"sustainable_development_goals":[{"score":0.8968908786773682,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Predicting":[0],"energy":[1,17,53,83,142,199,209],"consumption":[2,18,54],"accurately":[3],"and":[4,23,45,64,107,118,158,191,227],"reliably":[5],"is":[6,76,116,150,161],"critical":[7,139],"for":[8,15,36,141],"route":[9],"optimization":[10],"in":[11,79,87,152],"eco-routing.":[12],"State-of-the-practice":[13],"methods":[14,49],"calculating":[16],"utilize":[19],"second-by-second":[20],"speed,":[21],"acceleration,":[22],"power":[24,135],"demand.":[25],"Such":[26],"models":[27],"can":[28],"achieve":[29],"high":[30],"accuracy":[31,188,194,211],"but":[32],"are":[33],"not":[34],"suitable":[35],"forecasting":[37],"usages":[38],"due":[39],"to":[40,51,60,99,212],"strict":[41],"requirement":[42],"of":[43,72,82,104,132,189,195,222],"inputs":[44],"computing":[46],"resources.":[47],"Other":[48],"used":[50],"predict":[52],"rely":[55],"on":[56,110],"average":[57],"speed":[58,187],"data":[59,62],"reduce":[61],"collection":[63],"computation":[65,228],"efforts.":[66],"However,":[67],"they":[68],"ignore":[69],"the":[70,102,130,153,159,168,179,203,208,220],"individuality":[71],"driving":[73,108,112,146],"behavior,":[74],"which":[75],"particularly":[77],"important":[78],"near-term":[80],"predictions":[81],"consumption,":[84],"as":[85],"shown":[86],"this":[88,217],"paper.":[89],"This":[90],"study":[91,218],"develops":[92],"an":[93,184],"input-output":[94],"hidden":[95],"Markov":[96],"model":[97,115,149,182,201],"(IOHMM)":[98],"cope":[100],"with":[101,167],"influence":[103],"external":[105],"environment":[106],"behaviors":[109],"individual":[111],"features.":[113,147],"The":[114,126,148,197],"built":[117],"trained":[119],"using":[120],"passively":[121],"collected":[122],"geospatial":[123],"location":[124],"data.":[125],"approach":[127],"furthermore":[128],"improves":[129],"prediction":[131,200,210],"vehicle":[133],"specific":[134],"(VSP)":[136],"distribution,":[137],"a":[138],"parameter":[140],"predication,":[143],"through":[144],"predicted":[145],"tested":[151],"Washington":[154],"D.C.":[155],"metropolitan":[156],"area,":[157],"performance":[160],"evaluated":[162],"by":[163,206],"comparing":[164],"various":[165],"indicators":[166],"real-world":[169],"values":[170],"obtained":[171,215],"from":[172,216],"in-vehicle":[173],"fuel":[174],"recording":[175],"devices.":[176],"In":[177],"general,":[178],"IOHMM":[180],"behavior":[181],"demonstrates":[183],"overall":[185],"cruising":[186],"86.85%":[190],"acceleration":[192],"rate":[193],"82.73%.":[196],"behavior-integrated":[198],"outperforms":[202],"traditional":[204],"approaches":[205],"increasing":[207],"86.81%.":[213],"Results":[214],"corroborate":[219],"importance":[221],"behavioral":[223],"richness,":[224],"environmental":[225],"dynamics,":[226],"efficiency.":[229]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
