{"id":"https://openalex.org/W4392849759","doi":"https://doi.org/10.1145/3652859","title":"DeepMeshCity: A Deep Learning Model for Urban Grid Prediction","display_name":"DeepMeshCity: A Deep Learning Model for Urban Grid Prediction","publication_year":2024,"publication_date":"2024-03-15","ids":{"openalex":"https://openalex.org/W4392849759","doi":"https://doi.org/10.1145/3652859"},"language":"en","primary_location":{"id":"doi:10.1145/3652859","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3652859","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-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/A5089277973","display_name":"Chi Zhang","orcid":"https://orcid.org/0000-0002-6668-2329"},"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":"Chi Zhang","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-6668-2329","affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101815660","display_name":"L. Cai","orcid":"https://orcid.org/0009-0007-0548-6918"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Linhao Cai","raw_affiliation_strings":["Beijing Juefei Technology Co. Ltd, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0007-0548-6918","affiliations":[{"raw_affiliation_string":"Beijing Juefei Technology Co. Ltd, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100357846","display_name":"Meng Chen","orcid":"https://orcid.org/0000-0001-7179-2568"},"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":"Meng Chen","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-7179-2568","affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101701960","display_name":"Xiucheng Li","orcid":"https://orcid.org/0009-0006-4145-6698"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiucheng Li","raw_affiliation_strings":["Harbin Institute of Technology, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0006-4145-6698","affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology, Shenzhen, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045198704","display_name":"Gao Cong","orcid":"https://orcid.org/0000-0002-4430-6373"},"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":"Gao Cong","raw_affiliation_strings":["Nanyang Technological University, Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0002-4430-6373","affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0648,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.73401191,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":"18","issue":"6","first_page":"1","last_page":"26"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9944000244140625,"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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental 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":0.7781538367271423},{"id":"https://openalex.org/keywords/traverse","display_name":"Traverse","score":0.7463233470916748},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.667331874370575},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.6516696810722351},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5682772994041443},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5323296785354614},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49138393998146057},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.47584769129753113},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.43851444125175476},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3964453339576721},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.11491689085960388},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.0890200138092041}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7781538367271423},{"id":"https://openalex.org/C176809094","wikidata":"https://www.wikidata.org/wiki/Q15401496","display_name":"Traverse","level":2,"score":0.7463233470916748},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.667331874370575},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.6516696810722351},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5682772994041443},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5323296785354614},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49138393998146057},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47584769129753113},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.43851444125175476},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3964453339576721},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.11491689085960388},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0890200138092041},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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},{"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3652859","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3652859","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8500000238418579,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G7586934835","display_name":null,"funder_award_id":"202106010184","funder_id":"https://openalex.org/F4320322725","funder_display_name":"China Scholarship Council"},{"id":"https://openalex.org/G8939351342","display_name":null,"funder_award_id":"62206074","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"},{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W1969865391","https://openalex.org/W1971402834","https://openalex.org/W1988580225","https://openalex.org/W2082633923","https://openalex.org/W2286929393","https://openalex.org/W2518108298","https://openalex.org/W2519887557","https://openalex.org/W2530386080","https://openalex.org/W2561568083","https://openalex.org/W2612690371","https://openalex.org/W2743316574","https://openalex.org/W2756203131","https://openalex.org/W2788134583","https://openalex.org/W2808377988","https://openalex.org/W2808862972","https://openalex.org/W2809035759","https://openalex.org/W2901165057","https://openalex.org/W2901504064","https://openalex.org/W2903871660","https://openalex.org/W2904813135","https://openalex.org/W2904832339","https://openalex.org/W2910892140","https://openalex.org/W2911535719","https://openalex.org/W2915480215","https://openalex.org/W2922146383","https://openalex.org/W2950099298","https://openalex.org/W2952734551","https://openalex.org/W2962790412","https://openalex.org/W2963263347","https://openalex.org/W2963840672","https://openalex.org/W2964015378","https://openalex.org/W2964319113","https://openalex.org/W2965341826","https://openalex.org/W2996847713","https://openalex.org/W2996936831","https://openalex.org/W2997848713","https://openalex.org/W2998559444","https://openalex.org/W3000386982","https://openalex.org/W3004285114","https://openalex.org/W3022044744","https://openalex.org/W3034749137","https://openalex.org/W3094502228","https://openalex.org/W3103720336","https://openalex.org/W3138340468","https://openalex.org/W3157414468","https://openalex.org/W3171692457","https://openalex.org/W3174022889","https://openalex.org/W3193281533","https://openalex.org/W3207461654","https://openalex.org/W3208915345","https://openalex.org/W4210732711","https://openalex.org/W4221058635","https://openalex.org/W4320024300","https://openalex.org/W4360822846","https://openalex.org/W4365395071","https://openalex.org/W4382239616","https://openalex.org/W6762978078"],"related_works":["https://openalex.org/W2377402383","https://openalex.org/W2380835401","https://openalex.org/W2381912691","https://openalex.org/W2350381577","https://openalex.org/W2353618196","https://openalex.org/W2348074676","https://openalex.org/W2385033175","https://openalex.org/W2374043190","https://openalex.org/W2363298784","https://openalex.org/W2367402697"],"abstract_inverted_index":{"Urban":[0],"grid":[1,213],"prediction":[2,10,214],"can":[3],"be":[4],"applied":[5],"to":[6,29,61,70,118,143,153,163,186],"many":[7,39],"classic":[8],"spatial-temporal":[9,75,133,157],"tasks":[11],"such":[12],"as":[13,185],"air":[14],"quality":[15],"prediction,":[16,19,23],"crowd":[17],"density":[18],"and":[20,46,67,105,124,169],"traffic":[21],"flow":[22],"which":[24],"is":[25,128,141,230],"of":[26,35,101,196,222],"great":[27],"importance":[28],"smart":[30],"city":[31],"building.":[32],"In":[33,135,159],"light":[34],"its":[36],"practical":[37],"values,":[38],"methods":[40,205],"have":[41,47],"been":[42],"developed":[43],"for":[44,130,210],"it":[45],"achieved":[48],"promising":[49],"results.":[50],"Despite":[51],"their":[52],"successes,":[53],"two":[54,80,211],"main":[55],"challenges":[56],"remain":[57],"open:":[58],"(a)":[59],"how":[60,69],"well":[62],"capture":[63,119,154],"the":[64,73,120,125,132,155,165,170,176,180,189,194,220,225],"global":[65,121],"dependencies":[66],"(b)":[68],"effectively":[71],"model":[72,190,199],"multi-scale":[74,138,156,171],"correlations?":[76],"To":[77],"address":[78],"these":[79],"challenges,":[81],"we":[82,161],"propose":[83,162],"a":[84,90,102,106,137,150],"novel":[85],"method\u2014":[86],"DeepMeshCity":[87,223],",":[88],"with":[89,202],"carefully-designed":[91],"Self-Attention":[92],"Citywide":[93,107],"Grid":[94,108],"Learner":[95,109],"(":[96,110],"SA-CGL":[97,147],")":[98,112],"block":[99,116],"comprising":[100],"self-attention":[103,115],"unit":[104,127,140],"CGL":[111,126],"unit.":[113],"The":[114,216,228],"aims":[117],"spatial":[122],"dependencies,":[123],"responsible":[129],"learning":[131],"correlations.":[134,158],"particular,":[136],"memory":[139,167,172],"proposed":[142,198],"traverse":[144],"all":[145],"stacked":[146],"blocks":[148],"along":[149],"zigzag":[151],"path":[152],"addition,":[160],"initialize":[164],"single-scale":[166],"units":[168,173],"by":[174,200],"using":[175],"corresponding":[177],"ones":[178],"in":[179],"previous":[181],"fragment":[182],"stack,":[183],"so":[184],"speed":[187],"up":[188],"training.":[191],"We":[192],"evaluate":[193],"performance":[195],"our":[197],"comparing":[201],"several":[203],"state-of-the-art":[204],"on":[206],"four":[207],"real-world":[208],"datasets":[209],"urban":[212],"applications.":[215],"experimental":[217],"results":[218],"verify":[219],"superiority":[221],"over":[224],"existing":[226],"ones.":[227],"code":[229],"available":[231],"at":[232],"https://github.com/ILoveStudying/DeepMeshCity.":[233]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
