{"id":"https://openalex.org/W3095408181","doi":null,"title":"Traffic Speed Prediction and Mobility Behavior Analysis Using On-Demand Ride-Hailing Service Data","display_name":"Traffic Speed Prediction and Mobility Behavior Analysis Using On-Demand Ride-Hailing Service Data","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3095408181","mag":"3095408181"},"language":"en","primary_location":{"id":"pmh:oai:stars.library.ucf.edu:etd2020-1157","doi":null,"is_oa":true,"landing_page_url":"https://stars.library.ucf.edu/etd2020/158","pdf_url":"https://stars.library.ucf.edu/cgi/viewcontent.cgi?article=1157&context=etd2020","source":{"id":"https://openalex.org/S4210172555","display_name":"Journal of International Crisis and Risk Communication Research","issn_l":"2576-0017","issn":["2576-0017","2576-0025"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","vabb-shw"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Electronic Theses and Dissertations, 2020-2023","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://stars.library.ucf.edu/cgi/viewcontent.cgi?article=1157&context=etd2020","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051703346","display_name":"Jiechao Zhang","orcid":"https://orcid.org/0009-0006-7738-2272","observed_orcids":["https://orcid.org/0009-0006-7738-2272","https://orcid.org/0000-0003-0396-4932"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Jiechao Zhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5051703346"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9984999895095825,"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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9984999895095825,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.0006000000284984708,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.00019999999494757503,"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/traffic-speed-prediction","display_name":"traffic speed prediction","score":0.996999979019165},{"id":"https://openalex.org/keywords/xgboost","display_name":"XGBoost","score":0.8759999871253967},{"id":"https://openalex.org/keywords/trip-generation","display_name":"trip generation","score":0.8080000281333923},{"id":"https://openalex.org/keywords/commuting-trips","display_name":"commuting trips","score":0.7739999890327454},{"id":"https://openalex.org/keywords/urban-transportation-networks","display_name":"urban transportation networks","score":0.4970000088214874},{"id":"https://openalex.org/keywords/beijing","display_name":"Beijing","score":0.4440000057220459}],"concepts":[{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.47958245873451233},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.4189896285533905},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.39811232686042786},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.39234593510627747},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.3867585062980652},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.20178678631782532},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1861231029033661}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:stars.library.ucf.edu:etd2020-1157","doi":null,"is_oa":true,"landing_page_url":"https://stars.library.ucf.edu/etd2020/158","pdf_url":"https://stars.library.ucf.edu/cgi/viewcontent.cgi?article=1157&context=etd2020","source":{"id":"https://openalex.org/S4210172555","display_name":"Journal of International Crisis and Risk Communication Research","issn_l":"2576-0017","issn":["2576-0017","2576-0025"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","vabb-shw"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Electronic Theses and Dissertations, 2020-2023","raw_type":"text"},{"id":"mag:3095408181","doi":null,"is_oa":false,"landing_page_url":"https://stars.library.ucf.edu/cgi/viewcontent.cgi?article=1157&context=etd2020","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:stars.library.ucf.edu:etd2020-1157","doi":null,"is_oa":true,"landing_page_url":"https://stars.library.ucf.edu/etd2020/158","pdf_url":"https://stars.library.ucf.edu/cgi/viewcontent.cgi?article=1157&context=etd2020","source":{"id":"https://openalex.org/S4210172555","display_name":"Journal of International Crisis and Risk Communication Research","issn_l":"2576-0017","issn":["2576-0017","2576-0025"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","vabb-shw"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Electronic Theses and Dissertations, 2020-2023","raw_type":"text"},"sustainable_development_goals":[{"score":1.0,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"},{"score":0.8970999717712402,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3095408181.pdf","grobid_xml":"https://content.openalex.org/works/W3095408181.grobid-xml"},"referenced_works_count":55,"referenced_works":["https://openalex.org/W623127722","https://openalex.org/W1678356000","https://openalex.org/W1804959266","https://openalex.org/W1973943669","https://openalex.org/W1976040640","https://openalex.org/W1981239355","https://openalex.org/W1982300822","https://openalex.org/W1991551840","https://openalex.org/W1994682066","https://openalex.org/W2002527117","https://openalex.org/W2004353783","https://openalex.org/W2012367234","https://openalex.org/W2015629733","https://openalex.org/W2020616482","https://openalex.org/W2025573206","https://openalex.org/W2031346385","https://openalex.org/W2052250659","https://openalex.org/W2056284729","https://openalex.org/W2064837491","https://openalex.org/W2065146839","https://openalex.org/W2074329989","https://openalex.org/W2077963913","https://openalex.org/W2082729958","https://openalex.org/W2113123156","https://openalex.org/W2117829472","https://openalex.org/W2125189556","https://openalex.org/W2137165487","https://openalex.org/W2138860952","https://openalex.org/W2217607432","https://openalex.org/W2286716912","https://openalex.org/W2293841092","https://openalex.org/W2345172924","https://openalex.org/W2483705917","https://openalex.org/W2521503801","https://openalex.org/W2548696570","https://openalex.org/W2560603808","https://openalex.org/W2563118655","https://openalex.org/W2565492514","https://openalex.org/W2565919573","https://openalex.org/W2579495707","https://openalex.org/W2618763859","https://openalex.org/W2695874637","https://openalex.org/W2732194751","https://openalex.org/W2767802829","https://openalex.org/W2774943290","https://openalex.org/W2793343370","https://openalex.org/W2793443941","https://openalex.org/W2806312459","https://openalex.org/W2889260000","https://openalex.org/W2891705404","https://openalex.org/W2895292469","https://openalex.org/W2896314053","https://openalex.org/W2903595909","https://openalex.org/W2915071069","https://openalex.org/W2946537597"],"related_works":["https://openalex.org/W4404009977","https://openalex.org/W4410027824","https://openalex.org/W3124161371","https://openalex.org/W3038122502","https://openalex.org/W2113298739","https://openalex.org/W2743588682","https://openalex.org/W7100280145","https://openalex.org/W7171571599","https://openalex.org/W7203819607","https://openalex.org/W2182832784"],"abstract_inverted_index":{"Providing":[0],"accurate":[1],"traffic":[2,17,21],"speed":[3,18,52,67],"prediction":[4,19,137,155],"is":[5,163],"essential":[6],"for":[7,65,165],"the":[8,31,49,69,80,116,119,136,145,174,216,244,256,268,283,286,294,302,313,319,326,352],"success":[9],"of":[10,53,118,139,147,177,183,246,270,285,288,297,304,318,321,328,343,354],"Intelligent":[11],"Transportation":[12],"Systems":[13],"(ITS)":[14],"deployments.":[15],"Accurate":[16],"allows":[20],"managers":[22],"take":[23],"proper":[24],"countermeasures":[25],"when":[26],"emergent":[27],"changes":[28],"happen":[29],"in":[30,57,68,150,258,315],"transportation":[32,59],"network.":[33,60],"In":[34,157,211],"this":[35,212],"thesis,":[36,213],"we":[37,142,214,234,254,281],"present":[38],"a":[39,54,151,225,340],"computationally":[40],"less":[41],"expensive":[42],"machine":[43,86],"learning":[44,87],"approach":[45,82],"XGBoost":[46,81,126],"to":[47,75,114,206,238,325],"predict":[48,351],"future":[50,72],"travel":[51,232,305,308],"selected":[55,120],"sub-network":[56],"Beijing's":[58],"We":[61,78],"perform":[62],"different":[63,131,140,181],"experiments":[64],"predicting":[66],"network":[70,152],"from":[71,224],"1":[73],"min":[74],"20":[76],"min.":[77],"compare":[79],"against":[83],"other":[84,128,334],"well-known":[85],"and":[88,95,101,110,199,243,264,267,291,299,301,307],"statistical":[89],"models":[90,129],"such":[91,187],"as":[92,188],"linear":[93],"regression":[94,104],"decision":[96],"tree,":[97,100],"gradient":[98],"boosting":[99],"random":[102],"forest":[103],"models.":[105,121],"Three":[106],"metrics":[107],"MAE,":[108],"MAPE,":[109],"RMSE":[111],"are":[112],"used":[113,205],"evaluate":[115],"performance":[117],"Our":[122,310,337],"results":[123],"show":[124],"that":[125,144,349],"outperforms":[127],"across":[130],"experiment":[132],"conditions.":[133],"Based":[134,229],"on":[135,173,230,333],"accuracy":[138],"links,":[141],"find":[143],"number":[146,287],"vehicles":[148],"operating":[149],"also":[153],"affect":[154],"performance.":[156],"addition,":[158],"understanding":[159],"individual":[160,208],"mobility":[161,209,218,279,316,330,347,356],"behavior":[162],"critical":[164],"modeling":[166],"urban":[167],"dynamics.":[168],"It":[169],"provides":[170],"deeper":[171],"insights":[172],"generative":[175],"mechanisms":[176],"human":[178],"movements.":[179],"Recently,":[180],"types":[182],"emerging":[184,355],"data":[185,222,335],"sources":[186],"mobile":[189],"phone":[190],"call":[191],"detail":[192],"records,":[193],"social":[194],"media":[195],"posts,":[196],"GPS":[197],"observations,":[198],"smart":[200],"card":[201],"transactions":[202],"have":[203],"been":[204],"analyze":[207],"behavior.":[210],"report":[215],"spatio-temporal":[217],"behaviors":[219],"using":[220],"large-scale":[221],"collected":[223],"ride-hailing":[226,322],"service":[227],"platform.":[228],"passenger-level":[231],"data,":[233],"develop":[235],"an":[236],"algorithm":[237],"identify":[239],"users'":[240],"visited":[241,289],"places":[242],"functions":[245],"those":[247],"places.":[248],"To":[249,276],"characterize":[250],"temporal":[251],"movement":[252],"patterns,":[253,280],"reveal":[255],"differences":[257,314],"trip":[259],"generation":[260],"characteristics":[261],"between":[262,273],"commuting":[263],"non-commuting":[265],"trips":[266],"distribution":[269,284,296,303],"gap":[271],"time":[272],"consecutive":[274],"trips.":[275],"understand":[277],"spatial":[278,295],"observe":[282],"place":[290],"their":[292],"rank,":[293],"residences":[298],"workplaces,":[300],"distance":[306],"time.":[309],"analysis":[311],"highlights":[312],"patterns":[317],"users":[320],"services,":[323],"compared":[324],"findings":[327],"existing":[329],"studies":[331],"based":[332],"sources.":[336],"study":[338],"shows":[339],"tremendous":[341],"potential":[342],"developing":[344],"high-resolution":[345],"individual-level":[346],"model":[348],"can":[350],"demand":[353],"services":[357],"with":[358],"high":[359],"accuracy.":[360]},"counts_by_year":[],"updated_date":"2026-09-26T08:19:03.415552","created_date":"2020-11-09T00:00:00"}
