{"id":"https://openalex.org/W4409158433","doi":"https://doi.org/10.1145/3690624.3709441","title":"Effective AOI-level Parcel Volume Prediction: When Lookahead Parcels Matter","display_name":"Effective AOI-level Parcel Volume Prediction: When Lookahead Parcels Matter","publication_year":2025,"publication_date":"2025-04-04","ids":{"openalex":"https://openalex.org/W4409158433","doi":"https://doi.org/10.1145/3690624.3709441"},"language":"en","primary_location":{"id":"doi:10.1145/3690624.3709441","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3690624.3709441","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","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/A5056217600","display_name":"Yinfeng Xiang","orcid":"https://orcid.org/0009-0005-9561-2644"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yinfeng Xiang","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102992554","display_name":"J. H. Fang","orcid":"https://orcid.org/0009-0003-1772-5454"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiangyi Fang","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101680944","display_name":"Chao Li","orcid":"https://orcid.org/0000-0002-8886-1547"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Li","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002395365","display_name":"Haitao Yuan","orcid":"https://orcid.org/0000-0001-6721-065X"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Haitao Yuan","raw_affiliation_strings":["National Technological University, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"National Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101863006","display_name":"Yiwei Song","orcid":"https://orcid.org/0009-0001-0747-364X"},"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":"Yiwei Song","raw_affiliation_strings":["JD Logisitcs, Beijing, China"],"affiliations":[{"raw_affiliation_string":"JD Logisitcs, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100726041","display_name":"Jiming Chen","orcid":"https://orcid.org/0000-0003-3155-3145"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiming Chen","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5056217600"],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":2.7246,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.88511489,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2703","last_page":"2712"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.992900013923645,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.992900013923645,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9907000064849854,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9853000044822693,"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/volume","display_name":"Volume (thermodynamics)","score":0.6906102299690247},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5149203538894653},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.4237365126609802},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08017167448997498},{"id":"https://openalex.org/keywords/thermodynamics","display_name":"Thermodynamics","score":0.07156229019165039}],"concepts":[{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.6906102299690247},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5149203538894653},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.4237365126609802},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08017167448997498},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.07156229019165039}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3690624.3709441","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3690624.3709441","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W2034535233","https://openalex.org/W2057571486","https://openalex.org/W2119634512","https://openalex.org/W2747329762","https://openalex.org/W2785583251","https://openalex.org/W2883002311","https://openalex.org/W2904832339","https://openalex.org/W3049241938","https://openalex.org/W3109254449","https://openalex.org/W3111217629","https://openalex.org/W3156972038","https://openalex.org/W3173213819","https://openalex.org/W3175648361","https://openalex.org/W3195032234","https://openalex.org/W4225725834","https://openalex.org/W4290876926","https://openalex.org/W4290877962","https://openalex.org/W4290945572","https://openalex.org/W4309651778","https://openalex.org/W4379540078","https://openalex.org/W4382203079","https://openalex.org/W4385521774","https://openalex.org/W4385567932","https://openalex.org/W4385568343","https://openalex.org/W4385767662","https://openalex.org/W4387846636","https://openalex.org/W4396735673","https://openalex.org/W4401856724","https://openalex.org/W4402012689","https://openalex.org/W4403582450"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Last-mile":[0],"Delivery":[1],"Parcel":[2],"Volume":[3],"(LDPV)":[4],"quantifies":[5],"the":[6,29,36,55,78,147,166,171,190,204,221],"number":[7,139],"of":[8,23,32,39,52,125,140,192,206,218],"parcels":[9],"destined":[10],"for":[11,28,54,208],"a":[12,16,50,62,98,108,113,131,137,199],"specific":[13],"region,":[14],"particularly":[15],"manually":[17],"divided":[18],"Area-Of-Interest":[19],"(AOI).":[20],"Accurate":[21],"prediction":[22,41],"AOI-level":[24,126],"LDPV":[25],"is":[26],"crucial":[27],"efficient":[30],"management":[31],"logistics":[33],"resources.":[34],"However,":[35],"straightforward":[37],"adaptation":[38],"existing":[40],"models":[42],"often":[43],"falls":[44],"short,":[45],"primarily":[46],"due":[47],"to":[48,135,164,188],"(I)":[49],"lack":[51],"consideration":[53],"intuition":[56],"behind":[57],"AOI":[58,83],"divisions,":[59],"and":[60,85,112,121,156,184],"(II)":[61],"reliance":[63],"solely":[64],"on":[65],"fully":[66,154],"observed":[67,155],"historical":[68],"data,":[69],"which":[70],"may":[71],"not":[72],"inform":[73],"future":[74],"trends.":[75],"To":[76],"overcome":[77],"above":[79],"pitfalls,":[80],"leveraging":[81],"rich":[82],"data":[84],"advanced":[86],"parcel":[87],"travel":[88],"time":[89],"estimation":[90],"services":[91],"in":[92,178,220],"JD":[93],"Logistics,":[94],"this":[95],"paper":[96],"introduces":[97],"novel":[99],"framework":[100],"called":[101],"Dual-view":[102],"Prediction":[103],"Networks":[104],"(DualPNs).":[105],"It":[106],"combines":[107],"Vector-Quantified":[109],"AutoEncoder":[110],"(VQ-AE)":[111],"Template-Augmented":[114],"Zero-Inflated":[115],"Poisson":[116],"(TA-ZIP),":[117],"enabling":[118],"both":[119],"point":[120],"probabilistic":[122,167],"distribution":[123],"predictions":[124],"LDPV.":[127],"Specifically,":[128],"VQ-AE":[129],"utilizes":[130],"vector":[132],"quantization":[133],"technique":[134],"distill":[136],"large":[138],"AOIs":[141,186],"into":[142],"representative":[143],"templates,":[144],"thereby":[145],"addressing":[146],"first":[148],"pitfall.":[149,173],"Subsequently,":[150],"TA-ZIP":[151],"dynamically":[152],"integrates":[153],"lookahead":[157],"features,":[158],"aligning":[159],"them":[160],"with":[161],"template-specific":[162],"decoders":[163],"parameterize":[165],"distributions,":[168],"thus":[169],"resolving":[170],"second":[172],"We":[174],"conduct":[175],"extensive":[176],"experiments":[177],"two":[179],"cities,":[180],"comprising":[181],"over":[182,195],"47,000":[183],"126,000":[185],"respectively,":[187],"demonstrate":[189],"superiority":[191],"our":[193],"DualPNs":[194,207],"other":[196],"baselines.":[197],"Moreover,":[198],"real-world":[200],"case":[201],"study":[202],"highlights":[203],"effectiveness":[205],"enhancing":[209],"downstream":[210],"courier":[211],"allocation":[212],"by":[213],"yielding":[214],"an":[215],"average":[216],"improvement":[217],"1.51%":[219],"on-time":[222],"delivery":[223],"rate.":[224]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
