{"id":"https://openalex.org/W4385567562","doi":"https://doi.org/10.1145/3580305.3599811","title":"DRL4Route: A Deep Reinforcement Learning Framework for Pick-up and Delivery Route Prediction","display_name":"DRL4Route: A Deep Reinforcement Learning Framework for Pick-up and Delivery Route Prediction","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385567562","doi":"https://doi.org/10.1145/3580305.3599811"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599811","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599811","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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/A5079133483","display_name":"Xiaowei Mao","orcid":"https://orcid.org/0009-0008-1748-7376"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaowei Mao","raw_affiliation_strings":["Beijing Jiaotong University & Cainiao Network, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University & Cainiao Network, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018234604","display_name":"Haomin Wen","orcid":"https://orcid.org/0000-0001-6130-126X"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haomin Wen","raw_affiliation_strings":["Beijing Jiaotong University & Cainiao Network, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University & Cainiao Network, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028926935","display_name":"Hengrui Zhang","orcid":"https://orcid.org/0000-0002-0989-5803"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hengrui Zhang","raw_affiliation_strings":["Beijing Jiaotong University & Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University & Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065949777","display_name":"Huaiyu Wan","orcid":"https://orcid.org/0000-0002-0501-9363"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huaiyu Wan","raw_affiliation_strings":["Beijing Jiaotong University & Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University & Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Lixia Wu","orcid":"https://orcid.org/0000-0003-1863-2316"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lixia Wu","raw_affiliation_strings":["Cainiao Network, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Cainiao Network, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102782287","display_name":"Jianbin Zheng","orcid":"https://orcid.org/0000-0003-0636-3905"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jianbin Zheng","raw_affiliation_strings":["Cainiao Network, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Cainiao Network, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068596543","display_name":"Haoyuan Hu","orcid":"https://orcid.org/0000-0003-0464-7736"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haoyuan Hu","raw_affiliation_strings":["Cainiao Network, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Cainiao Network, Hangzhou, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102790320","display_name":"Youfang Lin","orcid":"https://orcid.org/0000-0002-1611-4323"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Youfang Lin","raw_affiliation_strings":["Beijing Jiaotong University & Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University & Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5079133483"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":2.5844,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.88595582,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"4628","last_page":"4637"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9890999794006348,"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":0.9890999794006348,"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":0.9751999974250793,"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/T12306","display_name":"Urban and Freight Transport Logistics","score":0.9650999903678894,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8561818599700928},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7607506513595581},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6639351844787598},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6097456812858582},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.566256046295166},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.551461935043335},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49362504482269287},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.48854026198387146},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.43861401081085205},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.42916417121887207},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09223112463951111},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08067959547042847}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8561818599700928},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7607506513595581},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6639351844787598},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6097456812858582},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.566256046295166},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.551461935043335},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49362504482269287},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.48854026198387146},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.43861401081085205},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.42916417121887207},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09223112463951111},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08067959547042847},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3580305.3599811","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599811","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.49000000953674316,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[{"id":"https://openalex.org/G2802911279","display_name":null,"funder_award_id":"Young","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3085993365","display_name":null,"funder_award_id":"(Grant No.","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G37568934","display_name":null,"funder_award_id":"Grant","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5249178904","display_name":null,"funder_award_id":"Grant No. 6","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7726157001","display_name":null,"funder_award_id":"Grant No.","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8840441106","display_name":null,"funder_award_id":"62272033","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W1601649239","https://openalex.org/W1985514943","https://openalex.org/W2295598076","https://openalex.org/W2795142517","https://openalex.org/W2803933017","https://openalex.org/W2944299967","https://openalex.org/W2949376505","https://openalex.org/W2953171463","https://openalex.org/W2962762898","https://openalex.org/W2963084599","https://openalex.org/W2968831808","https://openalex.org/W2975802312","https://openalex.org/W2997718247","https://openalex.org/W3022842046","https://openalex.org/W3103022576","https://openalex.org/W3174924523","https://openalex.org/W4206035315","https://openalex.org/W4289533820","https://openalex.org/W4290927838","https://openalex.org/W4290943511"],"related_works":["https://openalex.org/W96612179","https://openalex.org/W2770234245","https://openalex.org/W2566006169","https://openalex.org/W2987774938","https://openalex.org/W632915154","https://openalex.org/W4229499248","https://openalex.org/W4378874356","https://openalex.org/W2055733372","https://openalex.org/W2369811061","https://openalex.org/W3008584592"],"abstract_inverted_index":{"Pick-up":[0],"and":[1,79,188,204,218],"Delivery":[2],"Route":[3],"Prediction":[4],"(PDRP),":[5],"which":[6,175],"aims":[7],"to":[8,50,63,74,103,108,114,147],"estimate":[9],"the":[10,39,43,65,70,95,100,109,123,132,149,156,172,186,191,205,227],"future":[11],"service":[12],"route":[13,110],"of":[14,46,126,137,190],"a":[15,75,115,144,161,179,197],"worker":[16],"given":[17],"his":[18],"current":[19],"task":[20,44],"pool,":[21],"has":[22],"received":[23],"rising":[24],"attention":[25],"in":[26,77,90,167],"recent":[27],"years.":[28],"Deep":[29],"neural":[30],"networks":[31],"based":[32],"on":[33,155,226],"supervised":[34],"learning":[35,129,152],"have":[36],"emerged":[37],"as":[38,143],"dominant":[40],"model":[41,162],"for":[42,165],"because":[45],"their":[47,86],"powerful":[48],"ability":[49,136],"capture":[51],"workers'":[52],"behavior":[53],"patterns":[54],"from":[55],"massive":[56],"historical":[57],"data.":[58],"Though":[59],"promising,":[60],"they":[61],"fail":[62],"introduce":[64],"non-differentiable":[66,133],"test":[67,80],"criteria":[68],"into":[69],"training":[71,78],"process,":[72],"leading":[73,113],"mismatch":[76],"criteria.":[81],"Which":[82],"considerably":[83],"trims":[84],"down":[85],"performance":[87],"when":[88],"applied":[89],"practical":[91],"systems.":[92],"To":[93],"tackle":[94],"above":[96],"issue,":[97],"we":[98,158],"present":[99],"first":[101],"attempt":[102],"generalize":[104],"Reinforcement":[105],"Learning":[106],"(RL)":[107],"prediction":[111],"task,":[112],"novel":[116],"RL-based":[117],"framework":[118],"called":[119],"DRL4Route.":[120],"It":[121,170],"combines":[122],"behavior-learning":[124],"abilities":[125],"previous":[127],"deep":[128,151],"models":[130],"with":[131,178],"objective":[134],"optimization":[135],"reinforcement":[138],"learning.":[139],"DRL4Route":[140],"can":[141,184],"serve":[142],"plug-and-play":[145],"component":[146],"boost":[148],"existing":[150,224],"models.":[153],"Based":[154],"framework,":[157],"further":[159],"implement":[160],"named":[163],"DRL4Route-GAE":[164,210],"PDRP":[166],"logistic":[168],"service.":[169],"follows":[171],"actor-critic":[173],"architecture":[174],"is":[176],"equipped":[177],"Generalized":[180],"Advantage":[181],"Estimator":[182],"that":[183,209],"balance":[185],"bias":[187],"variance":[189],"policy":[192],"gradient":[193],"estimates,":[194],"thus":[195],"achieving":[196],"more":[198],"optimal":[199],"policy.":[200],"Extensive":[201],"offline":[202],"experiments":[203],"online":[206],"deployment":[207],"show":[208],"improves":[211],"Location":[212],"Square":[213],"Deviation":[214],"(LSD)":[215],"by":[216,221],"0.9%-2.7%,":[217],"Accuracy@3":[219],"(ACC@3)":[220],"2.4%-3.2%":[222],"over":[223],"methods":[225],"real-world":[228],"dataset.":[229]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":6}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}