{"id":"https://openalex.org/W4403582412","doi":"https://doi.org/10.1145/3627673.3680046","title":"CourIRL: Predicting Couriers' Behavior in Last-Mile Delivery Using Crossed-Attention Inverse Reinforcement Learning","display_name":"CourIRL: Predicting Couriers' Behavior in Last-Mile Delivery Using Crossed-Attention Inverse Reinforcement Learning","publication_year":2024,"publication_date":"2024-10-20","ids":{"openalex":"https://openalex.org/W4403582412","doi":"https://doi.org/10.1145/3627673.3680046"},"language":"en","primary_location":{"id":"doi:10.1145/3627673.3680046","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627673.3680046","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3627673.3680046","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","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/10.1145/3627673.3680046","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100328284","display_name":"Shuai Wang","orcid":"https://orcid.org/0000-0002-3609-2205"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuai Wang","raw_affiliation_strings":["Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-3609-2205","affiliations":[{"raw_affiliation_string":"Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114339258","display_name":"Tongtong Kong","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tongtong Kong","raw_affiliation_strings":["Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0003-2600-5136","affiliations":[{"raw_affiliation_string":"Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091361888","display_name":"Baoshen Guo","orcid":"https://orcid.org/0000-0002-7435-8238"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baoshen Guo","raw_affiliation_strings":["Southeast University &amp; JD Logistics, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-7435-8238","affiliations":[{"raw_affiliation_string":"Southeast University &amp; JD Logistics, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101551598","display_name":"Lin Li","orcid":"https://orcid.org/0000-0002-3511-5559"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Lin","raw_affiliation_strings":["Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-3511-5559","affiliations":[{"raw_affiliation_string":"Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101710780","display_name":"Haotian Wang","orcid":"https://orcid.org/0000-0001-9783-6389"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haotian Wang","raw_affiliation_strings":["JD Logistics, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9783-6389","affiliations":[{"raw_affiliation_string":"JD Logistics, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18092096,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4957","last_page":"4965"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11942","display_name":"Transportation and Mobility Innovations","score":0.9991999864578247,"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":0.9991999864578247,"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/T12546","display_name":"Smart Parking Systems Research","score":0.9990000128746033,"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/T12288","display_name":"Optimization and Search Problems","score":0.9832000136375427,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.7123552560806274},{"id":"https://openalex.org/keywords/mile","display_name":"Mile","score":0.6826119422912598},{"id":"https://openalex.org/keywords/last-mile","display_name":"Last mile (transportation)","score":0.628881573677063},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.5648965239524841},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.556373655796051},{"id":"https://openalex.org/keywords/inverse","display_name":"Inverse","score":0.48053818941116333},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4168110191822052},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.20881476998329163},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15212678909301758},{"id":"https://openalex.org/keywords/structural-engineering","display_name":"Structural engineering","score":0.12330013513565063},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1206718385219574}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7123552560806274},{"id":"https://openalex.org/C186379835","wikidata":"https://www.wikidata.org/wiki/Q253276","display_name":"Mile","level":2,"score":0.6826119422912598},{"id":"https://openalex.org/C45440154","wikidata":"https://www.wikidata.org/wiki/Q6494801","display_name":"Last mile (transportation)","level":3,"score":0.628881573677063},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.5648965239524841},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.556373655796051},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.48053818941116333},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4168110191822052},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.20881476998329163},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15212678909301758},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.12330013513565063},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1206718385219574},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3627673.3680046","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627673.3680046","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3627673.3680046","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3627673.3680046","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627673.3680046","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3627673.3680046","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.4399999976158142,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G7463114173","display_name":null,"funder_award_id":"BK20230815","funder_id":"https://openalex.org/F4320322769","funder_display_name":"Natural Science Foundation of Jiangsu Province"}],"funders":[{"id":"https://openalex.org/F4320321605","display_name":"Government of Jiangsu Province","ror":"https://ror.org/004svx814"},{"id":"https://openalex.org/F4320322769","display_name":"Natural Science Foundation of Jiangsu Province","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320329860","display_name":"National Science and Technology Major Project","ror":null},{"id":"https://openalex.org/F4320329878","display_name":"Central University Basic Research Fund of China","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4403582412.pdf","grobid_xml":"https://content.openalex.org/works/W4403582412.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W2008414295","https://openalex.org/W2062360625","https://openalex.org/W2084583462","https://openalex.org/W2171033594","https://openalex.org/W2295598076","https://openalex.org/W2296609147","https://openalex.org/W2560674852","https://openalex.org/W2783346913","https://openalex.org/W2944211469","https://openalex.org/W2996936831","https://openalex.org/W2997718247","https://openalex.org/W3006432778","https://openalex.org/W3012943949","https://openalex.org/W3086419239","https://openalex.org/W3087935424","https://openalex.org/W3090835024","https://openalex.org/W3093988073","https://openalex.org/W3114224083","https://openalex.org/W3116790506","https://openalex.org/W3119906317","https://openalex.org/W3174924523","https://openalex.org/W3176724216","https://openalex.org/W3197998683","https://openalex.org/W4206035315","https://openalex.org/W4226254568","https://openalex.org/W4283805364","https://openalex.org/W4290927838","https://openalex.org/W4308738233","https://openalex.org/W4309651778","https://openalex.org/W4382202832","https://openalex.org/W4385567562","https://openalex.org/W4385568343"],"related_works":["https://openalex.org/W4224920455","https://openalex.org/W2974961843","https://openalex.org/W4387715617","https://openalex.org/W2736109931","https://openalex.org/W2075280196","https://openalex.org/W4402947517","https://openalex.org/W4391768511","https://openalex.org/W4387147337","https://openalex.org/W2263936574","https://openalex.org/W2997910527"],"abstract_inverted_index":{"Human":[0],"behavior":[1,19,58],"prediction":[2,37,74],"is":[3,178],"an":[4,203],"essential":[5],"AI-based":[6],"task,":[7],"which":[8,96],"has":[9],"inspired":[10],"many":[11],"real-world":[12,72,191],"applications.":[13],"In":[14,29,123],"last-mile":[15,78,218],"logistics,":[16,79],"predicting":[17],"couriers'":[18,23,100,120,151],"can":[20],"benefit":[21],"the":[22,35,46,57,80,99,110,119,150,195,199,210],"preference":[24,169],"learning":[25,63],"and":[26,41,88,108,139,153,161,212],"workflow":[27,111],"optimization.":[28],"this":[30,124],"paper,":[31,125],"we":[32,126],"devote":[33],"to":[34,70,85,117,135,142,166],"behavioral":[36],"of":[38,53,60,186,205,215],"courier":[39],"workload":[40,44],"quantify":[42],"their":[43,144],"by":[45,202],"working":[47,121,146],"time":[48],"spent":[49],"at":[50],"each":[51,86],"area":[52],"interest":[54],"(AOI).":[55],"Considering":[56],"interpretability":[59],"inverse":[61],"reinforcement":[62],"(IRL),":[64],"existing":[65],"studies":[66],"have":[67],"applied":[68],"IRL":[69,165],"some":[71],"transportation":[73],"scenarios.":[75],"However,":[76],"in":[77,94,105,217],"platform":[81],"assigns":[82],"multiple":[83],"orders":[84],"courier,":[87],"couriers":[89],"also":[90],"receive":[91],"new":[92],"tasks":[93],"real-time,":[95],"additionally":[97],"influence":[98],"subsequent":[101],"decisions.":[102],"The":[103,184],"uncertainty":[104],"decision":[106],"spaces":[107],"dynamic":[109],"distribution":[112],"make":[113],"it":[114],"more":[115],"challenging":[116],"predict":[118,143],"time.":[122,147],"propose":[127],"CourIRL,":[128],"a":[129,157,163],"practical":[130],"IRL-based":[131],"framework":[132],"leveraging":[133],"cross-attention":[134,173],"integrate":[136],"Couriers'":[137],"historical":[138],"spatio-temporal":[140],"features":[141],"future":[145],"CourIRL":[148,197,216],"formulates":[149],"pick-up":[152],"delivery":[154],"tour":[155],"as":[156],"sequential":[158],"decision-making":[159,168],"process":[160],"designs":[162],"model-free":[164],"learn":[167],"vectors.":[170],"A":[171],"multi-head":[172],"mechanism-based":[174],"deep":[175],"regression":[176],"model":[177],"proposed":[179,196],"for":[180],"fine-grained":[181],"working-time":[182],"prediction.":[183],"results":[185],"extensive":[187],"experiments":[188],"on":[189],"two":[190],"datasets":[192],"demonstrate":[193],"that":[194],"surpasses":[198],"state-of-the-art":[200],"baselines":[201],"average":[204],"6.11%":[206],"across":[207],"settings,":[208],"showing":[209],"efficacy":[211],"potential":[213],"contributions":[214],"logistics.":[219]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
