{"id":"https://openalex.org/W1973200185","doi":"https://doi.org/10.1145/2493432.2493466","title":"Inferring human mobility patterns from taxicab location traces","display_name":"Inferring human mobility patterns from taxicab location traces","publication_year":2013,"publication_date":"2013-09-08","ids":{"openalex":"https://openalex.org/W1973200185","doi":"https://doi.org/10.1145/2493432.2493466","mag":"1973200185"},"language":"en","primary_location":{"id":"doi:10.1145/2493432.2493466","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2493432.2493466","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing","raw_type":"proceedings-article"},"type":"conference-paper","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/A5011324325","display_name":"Raghu Ganti","orcid":"https://orcid.org/0000-0003-3658-7918"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]},{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Raghu Ganti","raw_affiliation_strings":["IBM T J Watson Research Center, Yorktown Heights, NY, USA","IBM T. J. Watson Research Center, Yorktown Heights , NY, USA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM T J Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]},{"raw_affiliation_string":"IBM T. J. Watson Research Center, Yorktown Heights , NY, USA#TAB#","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072108555","display_name":"Mudhakar Srivatsa","orcid":"https://orcid.org/0000-0002-5874-3750"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]},{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mudhakar Srivatsa","raw_affiliation_strings":["IBM T J Watson Research Center, Yorktown Heights, NY, USA","IBM T. J. Watson Research Center, Yorktown Heights , NY, USA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM T J Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]},{"raw_affiliation_string":"IBM T. J. Watson Research Center, Yorktown Heights , NY, USA#TAB#","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110500429","display_name":"Anand Ranganathan","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]},{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anand Ranganathan","raw_affiliation_strings":["IBM T J Watson Research Center, Yorktown Heights, NY, USA","IBM T. J. Watson Research Center, Yorktown Heights , NY, USA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM T J Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]},{"raw_affiliation_string":"IBM T. J. Watson Research Center, Yorktown Heights , NY, USA#TAB#","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019539533","display_name":"Jiawei Han","orcid":"https://orcid.org/0000-0002-3629-2696"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiawei Han","raw_affiliation_strings":["University of Illinois, Urbana-Champaign, Champaign, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois, Urbana-Champaign, Champaign, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":42,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"459","last_page":"468"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9998999834060669,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9998999834060669,"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/T11106","display_name":"Data Management and Algorithms","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11942","display_name":"Transportation and Mobility Innovations","score":0.9945999979972839,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/trips-architecture","display_name":"TRIPS architecture","score":0.7956084609031677},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7283414602279663},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.5251973867416382},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4753308594226837},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46128812432289124},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.42494627833366394},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.41966283321380615},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2369627058506012},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22499296069145203},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08357551693916321}],"concepts":[{"id":"https://openalex.org/C157085824","wikidata":"https://www.wikidata.org/wiki/Q2384809","display_name":"TRIPS architecture","level":2,"score":0.7956084609031677},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7283414602279663},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.5251973867416382},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4753308594226837},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46128812432289124},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.42494627833366394},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.41966283321380615},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2369627058506012},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22499296069145203},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08357551693916321},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2493432.2493466","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2493432.2493466","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.8299999833106995}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321528","display_name":"Shanghai Educational Development Foundation","ror":"https://ror.org/0220qvk04"},{"id":"https://openalex.org/F4320338295","display_name":"Army Research Laboratory","ror":"https://ror.org/011hc8f90"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1554944419","https://openalex.org/W1810702064","https://openalex.org/W1982300822","https://openalex.org/W1987228002","https://openalex.org/W2008620264","https://openalex.org/W2013037850","https://openalex.org/W2020642377","https://openalex.org/W2021674713","https://openalex.org/W2074739868","https://openalex.org/W2084182494","https://openalex.org/W2087246061","https://openalex.org/W2101823987","https://openalex.org/W2108950108","https://openalex.org/W2118154549","https://openalex.org/W2118269922","https://openalex.org/W2125838338","https://openalex.org/W2132968912","https://openalex.org/W2133718038","https://openalex.org/W2152204876","https://openalex.org/W2170918595","https://openalex.org/W2171157447","https://openalex.org/W2787894218","https://openalex.org/W2793965255","https://openalex.org/W6910403728"],"related_works":["https://openalex.org/W2807758032","https://openalex.org/W4224254130","https://openalex.org/W2379651310","https://openalex.org/W2113019827","https://openalex.org/W1541249122","https://openalex.org/W2413828414","https://openalex.org/W2367222340","https://openalex.org/W2109657376","https://openalex.org/W2371300232","https://openalex.org/W2758692100"],"abstract_inverted_index":{"Taxicabs":[0],"equipped":[1],"with":[2,144],"real-time":[3],"location":[4,12,80],"sensing":[5],"devices":[6],"are":[7,14,74,83],"increasingly":[8],"becoming":[9],"popular.":[10],"Such":[11],"traces":[13],"a":[15,62,100,108,116,121,150,159],"rich":[16],"source":[17],"of":[18,90,99,148,163],"information":[19],"and":[20,29,58,92,118,138,146,161],"can":[21,127],"be":[22],"used":[23],"for":[24,61],"congestion":[25],"pricing,":[26],"taxicab":[27,48,117,134],"placement,":[28],"improved":[30],"city":[31],"planning.":[32],"An":[33],"important":[34],"problem":[35],"to":[36,41,52,55,111],"enable":[37],"these":[38],"application":[39],"is":[40],"identify":[42,56,112,128],"human":[43],"mobility":[44],"patterns":[45],"from":[46,133,142],"the":[47,97],"traces,":[49,81],"which":[50],"translates":[51],"being":[53],"able":[54],"pickup":[57,91],"dropoff":[59,93],"points":[60],"particular":[63],"trip.":[64],"In":[65],"this":[66],"paper,":[67],"we":[68],"show":[69,119],"that":[70,120,156],"while":[71],"past":[72,154],"approaches":[73,155],"effective":[75],"in":[76,86,107,136,158],"detecting":[77],"hotspots":[78],"using":[79],"they":[82],"largely":[84],"ineffective":[85],"identifying":[87],"trips":[88,129],"(pairs":[89],"points).":[94],"We":[95],"propose":[96],"use":[98],"graph":[101],"theory":[102],"concept":[103],"-":[104],"stretch":[105],"factor":[106],"novel":[109],"manner":[110],"trip(s)":[113],"made":[114],"by":[115],"Hidden":[122],"Markov":[123],"Model":[124],"based":[125],"algorithm":[126],"(using":[130],"real":[131],"datasets":[132,141],"deployments":[135],"Shanghai":[137],"partially":[139],"simulated":[140],"Stockholm)":[143],"precision":[145,160],"recall":[147,162],"90-94%,":[149],"significant":[151],"improvement":[152],"over":[153],"result":[157],"about":[164],"50-60%.":[165]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":7},{"year":2016,"cited_by_count":5},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}