{"id":"https://openalex.org/W3195033267","doi":"https://doi.org/10.1145/3448891.3448893","title":"Learned Taxi Fare for real-life trip trajectories via Temporal ResNet Exploration","display_name":"Learned Taxi Fare for real-life trip trajectories via Temporal ResNet Exploration","publication_year":2020,"publication_date":"2020-12-07","ids":{"openalex":"https://openalex.org/W3195033267","doi":"https://doi.org/10.1145/3448891.3448893","mag":"3195033267"},"language":"en","primary_location":{"id":"doi:10.1145/3448891.3448893","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3448891.3448893","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"MobiQuitous 2020 - 17th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services","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/A5029222217","display_name":"Sayda Elmi","orcid":"https://orcid.org/0000-0002-9091-2307"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sayda Elmi","raw_affiliation_strings":["Singapore and 10"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Singapore and 10","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113959731","display_name":"Tan Kian-Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tan Kian-Lee","raw_affiliation_strings":["Singapore and 10"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Singapore and 10","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.28455882,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"86","last_page":"95"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9998999834060669,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9994000196456909,"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":0.9986000061035156,"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":0.7547487020492554},{"id":"https://openalex.org/keywords/trips-architecture","display_name":"TRIPS architecture","score":0.4659496247768402},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.45850569009780884},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4583330750465393},{"id":"https://openalex.org/keywords/popularity","display_name":"Popularity","score":0.4370424747467041},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4182104766368866},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37238016724586487},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.18981939554214478},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.11934652924537659},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09774699807167053}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7547487020492554},{"id":"https://openalex.org/C157085824","wikidata":"https://www.wikidata.org/wiki/Q2384809","display_name":"TRIPS architecture","level":2,"score":0.4659496247768402},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.45850569009780884},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4583330750465393},{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.4370424747467041},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4182104766368866},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37238016724586487},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.18981939554214478},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.11934652924537659},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09774699807167053},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3448891.3448893","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3448891.3448893","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"MobiQuitous 2020 - 17th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5099999904632568,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2004073866","https://openalex.org/W2107878631","https://openalex.org/W2112738128","https://openalex.org/W2162203086","https://openalex.org/W2480327167","https://openalex.org/W2528639018","https://openalex.org/W2579495707","https://openalex.org/W2741460999","https://openalex.org/W2788134583","https://openalex.org/W2797789629","https://openalex.org/W2807894308","https://openalex.org/W2809128166","https://openalex.org/W2912500072","https://openalex.org/W2962834725","https://openalex.org/W2964098640","https://openalex.org/W3033070893","https://openalex.org/W4247613105","https://openalex.org/W4254951711"],"related_works":["https://openalex.org/W2368605798","https://openalex.org/W2518037665","https://openalex.org/W2348524959","https://openalex.org/W2477036161","https://openalex.org/W2368049389","https://openalex.org/W2384861574","https://openalex.org/W2170801710","https://openalex.org/W2952704802","https://openalex.org/W4294565801","https://openalex.org/W2142306706"],"abstract_inverted_index":{"Accurate":[0],"taxi":[1,29,43,48,100,128,145,187],"fare":[2,44,49,101,146,176,188,209],"forecasting":[3,57],"in":[4,19,37,189],"complex":[5,64,190],"and":[6,34,67,130,138,192,215],"crowded":[7],"scenarios":[8,191],"is":[9,183,204],"an":[10],"important":[11],"building":[12],"block":[13],"to":[14,40,61,110,142,185],"enabling":[15],"intelligent":[16],"transportation":[17],"systems":[18],"a":[20,76,118],"smart":[21],"city.":[22],"Given":[23],"the":[24,63,132,135,207],"observation,":[25],"increasing":[26],"popularity":[27],"of":[28,90,134,218],"services":[30],"such":[31,152],"as":[32,153],"Uber":[33],"Didi":[35],"Chuxing":[36],"China,":[38],"unable":[39],"collect":[41],"large-scale":[42],"data":[45],"continuously.":[46],"Traditional":[47],"prediction":[50,177],"methods":[51],"mostly":[52],"rely":[53],"on":[54,167],"time":[55,105],"series":[56],"techniques,":[58],"which":[59],"fail":[60],"model":[62,88,111],"non-linear":[65],"spatial":[66,108,113,119],"temporal":[68,94],"relations.":[69],"To":[70],"address":[71],"those":[72],"issues,":[73],"we":[74,115,148],"propose":[75],"Deep":[77],"Multi-View":[78],"Network":[79],"called":[80],"Temporal":[81],"ResNet":[82],"(TRES-Net)":[83],"framework.":[84],"Specifically,":[85],"our":[86,181,202],"proposed":[87],"consists":[89],"three":[91],"views:":[92],"(i)":[93],"view:":[95,109,141],"modeling":[96],"correlations":[97],"between":[98],"future":[99],"values":[102],"with":[103],"near":[104],"points,":[106],"(ii)":[107],"deep":[112],"correlations,":[114],"further":[116],"introduce":[117],"similarity":[120],"matrix":[121],"that":[122,180,201],"can":[123],"learn":[124],"from":[125],"spatially":[126],"similar":[127],"trips":[129,173],"capture":[131],"multi-modality":[133],"motion":[136],"patterns,":[137,147],"(iii)":[139],"semantic":[140],"extract":[143],"more":[144,150,168],"integrate":[149],"factors":[151],"trip":[154],"distance,":[155],"travel":[156],"time,":[157],"passenger":[158],"count,":[159],"tolls":[160],"amount,":[161,163],"tip":[162],"etc..":[164],"Extensive":[165],"experiments":[166],"than":[169],"700":[170],"millions":[171],"NYC":[172],"over":[174],"several":[175],"benchmarks":[178],"demonstrate":[179],"method":[182],"able":[184],"predict":[186],"achieves":[193],"state-of-the-art":[194],"performance.":[195,220],"Our":[196],"large":[197],"scale":[198],"evaluation":[199],"demonstrates":[200],"system":[203],"(a)":[205],"accurate\u2014with":[206],"mean":[208],"error":[210],"under":[211],"1":[212],"US":[213],"dollar":[214],"(b)":[216],"capable":[217],"real-time":[219]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}