{"id":"https://openalex.org/W2896448079","doi":"https://doi.org/10.1109/wimob.2018.8589126","title":"Pedestrians Complex Behavior Understanding and Prediction with Hybrid Markov Chain","display_name":"Pedestrians Complex Behavior Understanding and Prediction with Hybrid Markov Chain","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2896448079","doi":"https://doi.org/10.1109/wimob.2018.8589126","mag":"2896448079"},"language":"en","primary_location":{"id":"doi:10.1109/wimob.2018.8589126","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wimob.2018.8589126","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)","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/A5079512546","display_name":"Mostafa Karimzadeh","orcid":"https://orcid.org/0000-0002-1949-6857"},"institutions":[{"id":"https://openalex.org/I118564535","display_name":"University of Bern","ror":"https://ror.org/02k7v4d05","country_code":"CH","type":"education","lineage":["https://openalex.org/I118564535"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Mostafa Karimzadeh","raw_affiliation_strings":["Institute of Computer Science, University of Bern, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Computer Science, University of Bern, Switzerland","institution_ids":["https://openalex.org/I118564535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078322039","display_name":"Zhongliang Zhao","orcid":"https://orcid.org/0000-0002-0979-9272"},"institutions":[{"id":"https://openalex.org/I118564535","display_name":"University of Bern","ror":"https://ror.org/02k7v4d05","country_code":"CH","type":"education","lineage":["https://openalex.org/I118564535"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Zhongliang Zhao","raw_affiliation_strings":["Institute of Computer Science, University of Bern, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Computer Science, University of Bern, Switzerland","institution_ids":["https://openalex.org/I118564535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090709200","display_name":"Florian Gerber","orcid":"https://orcid.org/0000-0001-8545-5263"},"institutions":[{"id":"https://openalex.org/I118564535","display_name":"University of Bern","ror":"https://ror.org/02k7v4d05","country_code":"CH","type":"education","lineage":["https://openalex.org/I118564535"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Florian Gerber","raw_affiliation_strings":["Institute of Computer Science, University of Bern, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Computer Science, University of Bern, Switzerland","institution_ids":["https://openalex.org/I118564535"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087658596","display_name":"Torsten Braun","orcid":"https://orcid.org/0000-0001-5968-7108"},"institutions":[{"id":"https://openalex.org/I118564535","display_name":"University of Bern","ror":"https://ror.org/02k7v4d05","country_code":"CH","type":"education","lineage":["https://openalex.org/I118564535"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Torsten Braun","raw_affiliation_strings":["Institute of Computer Science, University of Bern, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Computer Science, University of Bern, Switzerland","institution_ids":["https://openalex.org/I118564535"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I118564535"],"apc_list":null,"apc_paid":null,"fwci":0.9004,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.75360177,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"200","last_page":"207"},"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":1.0,"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":1.0,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9887999892234802,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9876000285148621,"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/computer-science","display_name":"Computer science","score":0.7988057136535645},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.7609494924545288},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6440138816833496},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.46651187539100647},{"id":"https://openalex.org/keywords/order","display_name":"Order (exchange)","score":0.4162619411945343},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.4107922911643982},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3925358057022095},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36090952157974243},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.12011027336120605},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.10706481337547302}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7988057136535645},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.7609494924545288},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6440138816833496},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.46651187539100647},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.4162619411945343},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4107922911643982},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3925358057022095},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36090952157974243},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.12011027336120605},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10706481337547302},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wimob.2018.8589126","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wimob.2018.8589126","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.7699999809265137,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W800432607","https://openalex.org/W1983522293","https://openalex.org/W2006342384","https://openalex.org/W2015115084","https://openalex.org/W2039392404","https://openalex.org/W2077451659","https://openalex.org/W2123872940","https://openalex.org/W2171267091","https://openalex.org/W2519706319","https://openalex.org/W2563209335","https://openalex.org/W2598492678","https://openalex.org/W2618275257","https://openalex.org/W2735348515","https://openalex.org/W2753140366","https://openalex.org/W2777336709","https://openalex.org/W2791836213","https://openalex.org/W2963049813","https://openalex.org/W3098333493","https://openalex.org/W6740997305","https://openalex.org/W6785020755"],"related_works":["https://openalex.org/W2379651310","https://openalex.org/W2113019827","https://openalex.org/W1541249122","https://openalex.org/W2084326697","https://openalex.org/W2027903142","https://openalex.org/W2354322608","https://openalex.org/W2804608325","https://openalex.org/W2077211377","https://openalex.org/W2186675474","https://openalex.org/W2387462590"],"abstract_inverted_index":{"The":[0,20,120],"prevalence":[1],"of":[2,22,47,61,79,98,125,131,140,143,166,183,190],"smartphones":[3],"equipped":[4],"with":[5,170],"global":[6],"positioning":[7],"system":[8],"has":[9],"enabled":[10],"researchers":[11],"to":[12,57,75,82,102,115],"excavate":[13],"users":[14,126],"mobility":[15,84,138],"patterns":[16,139],"in":[17,33,63,127,163,168],"the":[18,96,123,136,155,164,193],"cities.":[19],"knowledge":[21],"users'":[23],"behavior,":[24],"such":[25],"as":[26],"their":[27],"locations,":[28],"plays":[29],"a":[30,70,113,128,132,141,151,176],"significant":[31],"role":[32],"location-based":[34],"services,":[35],"resource":[36],"management,":[37],"logistic":[38],"administration":[39],"and":[40,185],"urban":[41,64],"planning.":[42],"To":[43],"understand":[44],"complex":[45],"behavior":[46,92],"humans":[48],"we":[49,68,111],"utilize":[50],"spatio-temporal":[51],"analysis":[52],"on":[53,95],"collected":[54,162],"geo-location":[55],"points":[56],"exploit":[58],"Individual":[59],"Zone":[60],"Interests":[62],"areas.":[65],"In":[66],"addition,":[67],"designed":[69],"hybrid":[71],"Markov":[72,108],"chain":[73],"model":[74,114,121],"forecast":[76],"future":[77,179],"locations":[78],"pedestrians.":[80],"Compared":[81],"existing":[83,99],"prediction":[85,181,188],"methodologies,":[86],"our":[87],"predictor":[88],"can":[89],"adapt":[90],"it's":[91],"constantly":[93],"based":[94],"quality":[97],"traced":[100],"data":[101],"switch":[103],"between":[104],"first-order":[105],"or":[106],"second-order":[107],"chain.":[109],"Moreover,":[110],"propose":[112],"predict":[116],"city":[117,133,165],"area":[118,130,186],"congestion.":[119],"predicts":[122],"number":[124],"specific":[129],"by":[134],"discovering":[135],"regular":[137],"group":[142],"users.":[144,194],"We":[145,174],"conducted":[146],"comprehensive":[147],"empirical":[148],"experiments":[149],"using":[150],"real-life":[152],"dataset,":[153,159],"namely":[154],"Mobile":[156],"Data":[157],"Challenge":[158],"which":[160],"was":[161],"Lausanne":[167],"Switzerland":[169],"around":[171],"180":[172],"participants.":[173],"found":[175],"satisfactory":[177],"user":[178],"location":[180],"accuracy":[182,189],"70-84%":[184],"congestion":[187],"65-73%":[191],"for":[192]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
