{"id":"https://openalex.org/W4404611911","doi":"https://doi.org/10.1145/3678717.3700826","title":"Optimal Location of Electric Vehicle Charging Stations Using Geospatial Big Data","display_name":"Optimal Location of Electric Vehicle Charging Stations Using Geospatial Big Data","publication_year":2024,"publication_date":"2024-10-29","ids":{"openalex":"https://openalex.org/W4404611911","doi":"https://doi.org/10.1145/3678717.3700826"},"language":"en","primary_location":{"id":"doi:10.1145/3678717.3700826","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3678717.3700826","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3678717.3700826","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101890300","display_name":"Zhihang Liu","orcid":"https://orcid.org/0009-0000-6281-418X"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Zhihang Liu","raw_affiliation_strings":["The Chinese University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009185228","display_name":"Mei\u2010Po Kwan","orcid":"https://orcid.org/0000-0001-8602-9258"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Mei-Po Kwan","raw_affiliation_strings":["The Chinese University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101884823","display_name":"Jinlin Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I76214153","display_name":"Lanzhou University","ror":"https://ror.org/01mkqqe32","country_code":"CN","type":"education","lineage":["https://openalex.org/I76214153"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinlin Wu","raw_affiliation_strings":["Lanzhou University, Lanzhou, Gansu, China"],"affiliations":[{"raw_affiliation_string":"Lanzhou University, Lanzhou, Gansu, China","institution_ids":["https://openalex.org/I76214153"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101890300"],"corresponding_institution_ids":["https://openalex.org/I177725633"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.2212166,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"729","last_page":"732"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11942","display_name":"Transportation and Mobility Innovations","score":0.9937000274658203,"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":0.9937000274658203,"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/T10768","display_name":"Electric Vehicles and Infrastructure","score":0.993399977684021,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9907000064849854,"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/geospatial-analysis","display_name":"Geospatial analysis","score":0.9166503548622131},{"id":"https://openalex.org/keywords/electric-vehicle","display_name":"Electric vehicle","score":0.5998111367225647},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5994828343391418},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5812411308288574},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.3257400393486023},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.30898404121398926},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2090073525905609},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.12414661049842834},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.0719037652015686}],"concepts":[{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.9166503548622131},{"id":"https://openalex.org/C2776422217","wikidata":"https://www.wikidata.org/wiki/Q13629441","display_name":"Electric vehicle","level":3,"score":0.5998111367225647},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5994828343391418},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5812411308288574},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.3257400393486023},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.30898404121398926},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2090073525905609},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.12414661049842834},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0719037652015686},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3678717.3700826","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3678717.3700826","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3678717.3700826","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3678717.3700826","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2948338267","https://openalex.org/W3131853315","https://openalex.org/W3167281577","https://openalex.org/W4212857572","https://openalex.org/W4362719753","https://openalex.org/W4388844140","https://openalex.org/W4394993685"],"related_works":["https://openalex.org/W4367313141","https://openalex.org/W2004086023","https://openalex.org/W2733999579","https://openalex.org/W2110217573","https://openalex.org/W4283374591","https://openalex.org/W2910751785","https://openalex.org/W4390100400","https://openalex.org/W4366547507","https://openalex.org/W4362512700","https://openalex.org/W4390608645"],"abstract_inverted_index":{"This":[0,119],"study":[1],"focuses":[2],"on":[3],"optimizing":[4],"the":[5,14,23,30,38,46,95,106,135],"location":[6],"of":[7,16,40,48,97,108,128,138],"electric":[8,139],"vehicle":[9],"(EV)":[10],"charging":[11,51,68,98,129],"stations":[12],"in":[13,131],"state":[15],"Georgia":[17],"using":[18],"geospatial":[19],"big":[20],"data.":[21],"With":[22],"growing":[24],"concern":[25],"about":[26],"climate":[27],"change":[28],"and":[29,57,77,115,125],"need":[31],"for":[32],"a":[33,49,88],"transition":[34],"to":[35,60,74,93,122],"sustainable":[36],"energy,":[37],"adoption":[39,63,137],"EVs":[41],"is":[42,91],"rapidly":[43],"increasing,":[44],"necessitating":[45],"development":[47,127],"robust":[50],"infrastructure.":[52],"We":[53],"use":[54],"historical":[55],"data":[56,73],"predictive":[58],"modeling":[59],"forecast":[61],"EV":[62],"by":[64],"2030,":[65],"assessing":[66],"future":[67],"demand":[69],"through":[70],"travel":[71,79],"survey":[72],"understand":[75],"spatial":[76],"temporal":[78],"patterns.":[80],"A":[81],"Mixed-Integer":[82],"Programming":[83],"(MIP)":[84],"approach,":[85],"combined":[86],"with":[87],"genetic":[89],"algorithm,":[90],"employed":[92],"optimize":[94],"layout":[96],"stations,":[99,109],"balancing":[100,116],"multiple":[101],"objectives":[102],"such":[103],"as":[104],"minimizing":[105],"number":[107],"maximizing":[110],"coverage,":[111],"ensuring":[112],"social":[113],"equity,":[114],"urban-rural":[117],"distribution.":[118],"framework":[120],"aims":[121],"support":[123],"efficient":[124],"equitable":[126],"infrastructure":[130],"Georgia,":[132],"ultimately":[133],"facilitating":[134],"wider":[136],"vehicles.":[140]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}