{"id":"https://openalex.org/W3168794627","doi":"https://doi.org/10.1145/3447548.3467068","title":"A Deep Learning Method for Route and Time Prediction in Food Delivery Service","display_name":"A Deep Learning Method for Route and Time Prediction in Food Delivery Service","publication_year":2021,"publication_date":"2021-08-13","ids":{"openalex":"https://openalex.org/W3168794627","doi":"https://doi.org/10.1145/3447548.3467068","mag":"3168794627"},"language":"en","primary_location":{"id":"doi:10.1145/3447548.3467068","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447548.3467068","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & 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/A5036452672","display_name":"Chengliang Gao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengliang Gao","raw_affiliation_strings":["Meituan, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100403389","display_name":"Fan Zhang","orcid":"https://orcid.org/0000-0001-6623-9936"},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Zhang","raw_affiliation_strings":["Meituan, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009313347","display_name":"Guan-qun Wu","orcid":"https://orcid.org/0000-0002-3536-0626"},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guanqun Wu","raw_affiliation_strings":["Meituan, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002949317","display_name":"Qiwan Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiwan Hu","raw_affiliation_strings":["Meituan, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010000394","display_name":"Qiang Ru","orcid":null},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Ru","raw_affiliation_strings":["Meituan, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049376421","display_name":"Jinghua Hao","orcid":"https://orcid.org/0009-0002-3577-018X"},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinghua Hao","raw_affiliation_strings":["Meituan, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028060197","display_name":"Renqing He","orcid":"https://orcid.org/0000-0001-7788-7584"},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Renqing He","raw_affiliation_strings":["Meituan, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024427917","display_name":"Zhizhao Sun","orcid":"https://orcid.org/0000-0002-5783-5944"},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhizhao Sun","raw_affiliation_strings":["Meituan, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.787,"has_fulltext":false,"cited_by_count":51,"citation_normalized_percentile":{"value":0.95862623,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2879","last_page":"2889"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9988999962806702,"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"}},"topics":[{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9988999962806702,"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"}},{"id":"https://openalex.org/T12016","display_name":"Web Data Mining and Analysis","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"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":0.9934999942779541,"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":0.7798447608947754},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.7037081718444824},{"id":"https://openalex.org/keywords/service","display_name":"Service (business)","score":0.6155625581741333},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.5289773344993591},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4406058192253113},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42515742778778076},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38408949971199036},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3326336145401001},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10532337427139282}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7798447608947754},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7037081718444824},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.6155625581741333},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.5289773344993591},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4406058192253113},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42515742778778076},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38408949971199036},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3326336145401001},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10532337427139282},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C136264566","wikidata":"https://www.wikidata.org/wiki/Q159810","display_name":"Economy","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3447548.3467068","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447548.3467068","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Zero hunger","score":0.5799999833106995,"id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1902237438","https://openalex.org/W2023279748","https://openalex.org/W2097268493","https://openalex.org/W2141596757","https://openalex.org/W2144475703","https://openalex.org/W2295598076","https://openalex.org/W2353778398","https://openalex.org/W2589099544","https://openalex.org/W2604662567","https://openalex.org/W2741460999","https://openalex.org/W2809128166","https://openalex.org/W2910453440","https://openalex.org/W2963620441","https://openalex.org/W2981664222","https://openalex.org/W3038304141","https://openalex.org/W3080956811","https://openalex.org/W3081469395","https://openalex.org/W3102476541","https://openalex.org/W3106332918"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4224009465","https://openalex.org/W4286629047","https://openalex.org/W4306321456","https://openalex.org/W4285260836","https://openalex.org/W3046775127","https://openalex.org/W2081647779","https://openalex.org/W3170094116","https://openalex.org/W4205958290"],"abstract_inverted_index":{"Online":[0],"food":[1,32,37,110],"ordering":[2],"and":[3,47,60,78,127,163,182],"delivery":[4,33,38,45,111,124],"service":[5],"has":[6,15],"widely":[7],"served":[8],"people's":[9],"daily":[10],"demands":[11],"worldwide,":[12],"e.g.,":[13],"it":[14],"reached":[16],"a":[17,80,106,135],"number":[18],"of":[19,28,42,95,109,131,139,154,165,189],"34.9":[20],"million":[21],"online":[22,183],"orders":[23],"per":[24],"day":[25],"in":[26,30,123,145],"Q3":[27],"2020":[29],"Meituan":[31],"platform.":[34],"For":[35],"the":[36,43,51,64,71,75,93,99,115,120,128,148,155,160,173,178,187],"service,":[39],"accurate":[40],"estimation":[41],"driver's":[44,161],"route":[46,125],"time,":[48,77],"defined":[49],"as":[50],"FD-RTP":[52,72],"task,":[53],"is":[54],"very":[55],"significant":[56],"to":[57,70,172],"customer":[58],"satisfaction":[59],"driver":[61,100],"experience.":[62],"In":[63],"paper,":[65],"we":[66,91],"apply":[67],"deep":[68,81],"learning":[69],"task":[73],"for":[74],"first":[76],"propose":[79],"network":[82],"named":[83],"FDNET.":[84,192],"Different":[85],"from":[86],"traditional":[87],"heuristic":[88],"search":[89,121],"algorithms,":[90],"predict":[92],"probability":[94],"each":[96],"feasible":[97],"location":[98],"will":[101],"visit":[102],"next,":[103],"through":[104],"mining":[105],"large":[107],"amount":[108],"data.":[112],"Guided":[113],"by":[114],"probabilities,":[116],"FDNET":[117,146],"greatly":[118],"reduces":[119],"space":[122],"generation,":[126],"calculation":[129],"times":[130],"time":[132],"prediction.":[133],"As":[134],"result,":[136],"various":[137],"kinds":[138],"information":[140,169],"can":[141],"be":[142],"fully":[143],"utilized":[144],"within":[147],"limited":[149],"computation":[150],"time.":[151],"Careful":[152],"consideration":[153],"factors":[156],"having":[157],"effect":[158],"on":[159],"behaviors":[162],"introduction":[164],"more":[166],"abundant":[167],"spatiotemporal":[168],"both":[170],"contribute":[171],"improvements.":[174],"Offline":[175],"experiments":[176],"over":[177],"large-scale":[179],"real-world":[180],"dataset,":[181],"A/B":[184],"test":[185],"demonstrate":[186],"effectiveness":[188],"our":[190],"proposed":[191]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}