{"id":"https://openalex.org/W4390100381","doi":"https://doi.org/10.1145/3589132.3625607","title":"Explainable Trajectory Representation through Dictionary Learning","display_name":"Explainable Trajectory Representation through Dictionary Learning","publication_year":2023,"publication_date":"2023-11-13","ids":{"openalex":"https://openalex.org/W4390100381","doi":"https://doi.org/10.1145/3589132.3625607"},"language":"en","primary_location":{"id":"doi:10.1145/3589132.3625607","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3589132.3625607","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3589132.3625607","source":null,"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3589132.3625607","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103055855","display_name":"Yuanbo Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanbo Tang","raw_affiliation_strings":["Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-8118-9874","affiliations":[{"raw_affiliation_string":"Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011294188","display_name":"Zhiyuan Peng","orcid":"https://orcid.org/0009-0008-7034-936X"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyuan Peng","raw_affiliation_strings":["Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0008-7034-936X","affiliations":[{"raw_affiliation_string":"Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114377934","display_name":"Yang Li","orcid":"https://orcid.org/0000-0002-2053-6393"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Li","raw_affiliation_strings":["Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0002-2053-6393","affiliations":[{"raw_affiliation_string":"Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, Guangdong, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1441,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.49970136,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9945999979972839,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9944999814033508,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8331387042999268},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7416999340057373},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.668928861618042},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6284428238868713},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5811166763305664},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5618889331817627},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5115278959274292},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4950043559074402},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48763027787208557},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.46054619550704956},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.455778568983078}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8331387042999268},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7416999340057373},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.668928861618042},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6284428238868713},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5811166763305664},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5618889331817627},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5115278959274292},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4950043559074402},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48763027787208557},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.46054619550704956},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.455778568983078},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3589132.3625607","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3589132.3625607","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3589132.3625607","source":null,"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3589132.3625607","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3589132.3625607","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3589132.3625607","source":null,"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4390100381.pdf","grobid_xml":"https://content.openalex.org/works/W4390100381.grobid-xml"},"referenced_works_count":9,"referenced_works":["https://openalex.org/W1985027321","https://openalex.org/W2021620612","https://openalex.org/W2135822894","https://openalex.org/W2245612378","https://openalex.org/W2844600553","https://openalex.org/W3013563398","https://openalex.org/W3173572290","https://openalex.org/W3183749634","https://openalex.org/W3214908026"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W2888392564","https://openalex.org/W4310278675","https://openalex.org/W4388422664","https://openalex.org/W4390569940","https://openalex.org/W4361193272","https://openalex.org/W2963326959","https://openalex.org/W4388685194","https://openalex.org/W4312407344","https://openalex.org/W4390871823"],"abstract_inverted_index":{"Trajectory":[0],"representation":[1,51,88],"learning":[2,23,26,52,56,121],"on":[3,64,110,131,144],"a":[4,60,65,69,107,136],"network":[5],"enhances":[6],"our":[7,100,158],"understanding":[8],"of":[9,62,72,99,114,177],"vehicular":[10],"traffic":[11],"patterns":[12],"and":[13,35,40,92,163,188],"benefits":[14],"numerous":[15],"downstream":[16,43,181],"applications.":[17],"Existing":[18],"approaches":[19],"using":[20],"classic":[21],"machine":[22],"or":[24],"deep":[25],"embed":[27],"trajectories":[28,63],"as":[29],"dense":[30],"vectors,":[31],"which":[32,78],"lack":[33],"interpretability":[34],"are":[36],"inefficient":[37],"to":[38,105,126,135,151],"store":[39],"analyze":[41],"in":[42,180],"tasks.":[44],"In":[45],"this":[46,178],"paper,":[47],"an":[48],"explainable":[49],"trajectory":[50,82,138],"framework":[53,141],"through":[54],"dictionary":[55,71,120,155],"is":[57,89,103,123,142,160],"proposed.":[58],"Given":[59],"collection":[61],"network,":[66],"it":[67],"extracts":[68],"compact":[70,162],"commonly":[73],"used":[74],"subpaths":[75],"called":[76],"\"pathlets\",":[77],"optimally":[79],"reconstruct":[80],"each":[81],"by":[83,157],"simple":[84],"concatenations.":[85],"The":[86],"resulting":[87],"naturally":[90],"sparse":[91],"encodes":[93],"strong":[94],"spatial":[95],"semantics.":[96],"Theoretical":[97],"analysis":[98],"proposed":[101,125],"algorithm":[102],"conducted":[104],"provide":[106],"probabilistic":[108],"bound":[109],"the":[111,115,128,154,174],"estimation":[112],"error":[113],"optimal":[116],"dictionary.":[117],"A":[118],"hierarchical":[119],"scheme":[122],"also":[124,172],"ensure":[127],"algorithm's":[129],"scalability":[130],"large":[132],"networks,":[133],"leading":[134],"multi-scale":[137],"representation.":[139],"Our":[140],"evaluated":[143],"two":[145],"large-scale":[146],"real-world":[147],"taxi":[148],"datasets.":[149],"Compared":[150],"previous":[152],"work,":[153],"learned":[156],"method":[159,179],"more":[161],"has":[164],"better":[165],"reconstruction":[166],"rate":[167],"for":[168],"new":[169],"trajectories.":[170],"We":[171],"demonstrate":[173],"promising":[175],"performance":[176],"tasks":[182],"including":[183],"trip":[184],"time":[185],"prediction":[186],"task":[187],"data":[189],"compression.":[190]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
