{"id":"https://openalex.org/W2768944500","doi":"https://doi.org/10.1109/cdc.2017.8264609","title":"Reducing inefficiencies in taxi systems","display_name":"Reducing inefficiencies in taxi systems","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2768944500","doi":"https://doi.org/10.1109/cdc.2017.8264609","mag":"2768944500"},"language":"en","primary_location":{"id":"doi:10.1109/cdc.2017.8264609","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc.2017.8264609","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 56th Annual Conference on Decision and Control (CDC)","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/A5034687114","display_name":"Chenguang Zhu","orcid":"https://orcid.org/0000-0001-6955-8924"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chenguang Zhu","raw_affiliation_strings":["Microsoft AI+Research, One Microsoft Way, Redmond, WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft AI+Research, One Microsoft Way, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018734150","display_name":"Balaji Prabhakar","orcid":null},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Balaji Prabhakar","raw_affiliation_strings":["Departments of Electrical Engineering, Computer Science of Stanford University, Stanford, CA, USA"],"affiliations":[{"raw_affiliation_string":"Departments of Electrical Engineering, Computer Science of Stanford University, Stanford, CA, USA","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5034687114"],"corresponding_institution_ids":["https://openalex.org/I1290206253"],"apc_list":null,"apc_paid":null,"fwci":1.5795,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.8568444,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"6301","last_page":"6306"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11942","display_name":"Transportation and Mobility Innovations","score":0.9997000098228455,"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/T11942","display_name":"Transportation and Mobility Innovations","score":0.9997000098228455,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9973000288009644,"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/T12306","display_name":"Urban and Freight Transport Logistics","score":0.9871000051498413,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5817129611968994}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5817129611968994}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cdc.2017.8264609","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc.2017.8264609","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 56th Annual Conference on Decision and Control (CDC)","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":23,"referenced_works":["https://openalex.org/W156001215","https://openalex.org/W312709805","https://openalex.org/W783419490","https://openalex.org/W1504021368","https://openalex.org/W1969454839","https://openalex.org/W1976993400","https://openalex.org/W1982480944","https://openalex.org/W2010672263","https://openalex.org/W2025766355","https://openalex.org/W2032706896","https://openalex.org/W2038142588","https://openalex.org/W2049412363","https://openalex.org/W2090359754","https://openalex.org/W2132968912","https://openalex.org/W2142965730","https://openalex.org/W2156036190","https://openalex.org/W2157529519","https://openalex.org/W2161581167","https://openalex.org/W2480327167","https://openalex.org/W3104066485","https://openalex.org/W4239634521","https://openalex.org/W6606267662","https://openalex.org/W6644702372"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W4391913857","https://openalex.org/W2350741829","https://openalex.org/W2530322880"],"abstract_inverted_index":{"Taxi":[0],"systems":[1,7],"are":[2,22,64],"perfect":[3],"examples":[4],"of":[5,41,46,115,118,120,132,149,165,180,183],"supply-demand":[6],"in":[8,59,67,160,173],"which":[9,70],"taxi":[10,42,47,73,186,195],"vehicles":[11],"and":[12,52,87],"drivers":[13],"constitute":[14],"the":[15,23,93,136,146,163,181,184,189],"supply":[16],"side,":[17],"while":[18,152,188],"passengers":[19,58],"hailing":[20],"taxis":[21,123,151,167],"demand":[24],"side.":[25],"However,":[26],"various":[27],"inefficiencies":[28,63,86],"can":[29,143],"be":[30],"embedded":[31],"within":[32],"such":[33],"a":[34,44,89,125,130],"large-scale":[35],"system,":[36],"e.g.":[37,129],"an":[38,50],"excessive":[39],"number":[40,148,164],"vehicles,":[43],"shortage":[45],"supplies":[48],"after":[49],"event":[51],"long":[53,126],"idle":[54,155,191],"times":[55],"with":[56,102],"no":[57],"taxis.":[60],"These":[61],"systemic":[62],"often":[65],"overlooked":[66],"previous":[68],"literature,":[69],"focuses":[71],"on":[72,98],"dispatching":[74],"mechanisms":[75],"to":[76,122,168],"satisfy":[77],"short-term":[78],"demand.":[79],"In":[80],"this":[81,141],"paper,":[82],"we":[83],"address":[84],"these":[85],"propose":[88],"novel":[90],"model":[91,106,112,142],"for":[92],"trip":[94,137],"assignment":[95,138],"problem":[96],"based":[97],"network":[99],"flow.":[100],"Compared":[101],"existing":[103],"methods,":[104],"our":[105,161],"is":[107,113,177],"much":[108],"more":[109],"scalable.":[110],"This":[111],"capable":[114],"assigning":[116],"hundreds":[117],"thousands":[119],"trips":[121,172],"over":[124],"time":[127,192],"interval,":[128],"shift":[131],"12":[133],"hours.":[134],"Furthermore,":[135],"given":[139],"by":[140,197],"effectively":[144],"minimize":[145],"total":[147],"required":[150,166],"reducing":[153],"incurred":[154,193],"time.":[156],"Experiments":[157],"show":[158],"that":[159],"model,":[162],"finish":[169],"all":[170],"observed":[171],"New":[174],"York":[175],"City":[176],"only":[178],"72%":[179],"size":[182],"current":[185],"fleet,":[187],"average":[190],"per":[194],"drops":[196],"32%.":[198]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
