{"id":"https://openalex.org/W3008725429","doi":"https://doi.org/10.1109/isspit47144.2019.9001881","title":"Spatiotemporal Traffic Flow Forecasting for PHETs Based on Data Mining and Deep Learning","display_name":"Spatiotemporal Traffic Flow Forecasting for PHETs Based on Data Mining and Deep Learning","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3008725429","doi":"https://doi.org/10.1109/isspit47144.2019.9001881","mag":"3008725429"},"language":"en","primary_location":{"id":"doi:10.1109/isspit47144.2019.9001881","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isspit47144.2019.9001881","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","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/A5014156600","display_name":"Luo Xiangzhou","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiangzhou Luo","raw_affiliation_strings":["Glasgow College, University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Glasgow College, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049494220","display_name":"Xushan Qing","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xushan Qing","raw_affiliation_strings":["Glasgow College, University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Glasgow College, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010968303","display_name":"Huimiao Chen","orcid":"https://orcid.org/0000-0001-8356-2866"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huimiao Chen","raw_affiliation_strings":["Sparkzone Institute, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Sparkzone Institute, Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5014156600"],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.23117418,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T12095","display_name":"Vehicle emissions and performance","score":0.9988999962806702,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9983000159263611,"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/taxis","display_name":"Taxis","score":0.7167402505874634},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6672717332839966},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5731896758079529},{"id":"https://openalex.org/keywords/beijing","display_name":"Beijing","score":0.5604450702667236},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.5516312718391418},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4925096929073334},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4500857889652252},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4273329973220825},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.42478665709495544},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4131726026535034},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.34432974457740784},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.29664134979248047},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.21962487697601318},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18136298656463623},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09591218829154968}],"concepts":[{"id":"https://openalex.org/C183373512","wikidata":"https://www.wikidata.org/wiki/Q949618","display_name":"Taxis","level":2,"score":0.7167402505874634},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6672717332839966},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5731896758079529},{"id":"https://openalex.org/C2778304055","wikidata":"https://www.wikidata.org/wiki/Q657474","display_name":"Beijing","level":3,"score":0.5604450702667236},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.5516312718391418},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4925096929073334},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4500857889652252},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4273329973220825},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.42478665709495544},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4131726026535034},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.34432974457740784},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29664134979248047},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.21962487697601318},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18136298656463623},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09591218829154968},{"id":"https://openalex.org/C191935318","wikidata":"https://www.wikidata.org/wiki/Q148","display_name":"China","level":2,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isspit47144.2019.9001881","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isspit47144.2019.9001881","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","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":19,"referenced_works":["https://openalex.org/W1567302070","https://openalex.org/W1686810756","https://openalex.org/W1991770012","https://openalex.org/W2021153764","https://openalex.org/W2028808891","https://openalex.org/W2043451124","https://openalex.org/W2050974717","https://openalex.org/W2057918527","https://openalex.org/W2106296901","https://openalex.org/W2156014214","https://openalex.org/W2194775991","https://openalex.org/W2579495707","https://openalex.org/W2580326292","https://openalex.org/W2613331518","https://openalex.org/W2896507969","https://openalex.org/W2963358464","https://openalex.org/W6637373629","https://openalex.org/W6661227368","https://openalex.org/W6682696941"],"related_works":["https://openalex.org/W2731640799","https://openalex.org/W3145095895","https://openalex.org/W2594548639","https://openalex.org/W4387544810","https://openalex.org/W2978498151","https://openalex.org/W2782837293","https://openalex.org/W1946755446","https://openalex.org/W2388377527","https://openalex.org/W565532978","https://openalex.org/W2372614270"],"abstract_inverted_index":{"Plug-in":[0],"electric":[1,64],"vehicles":[2],"(PEVs)":[3],"are":[4],"promoted":[5],"as":[6,130],"environmental-friendly":[7],"future":[8],"vehicles.":[9,179],"Comparing":[10],"to":[11,42,56,139,158,172,178],"the":[12,44,58,71,78,90,92,97,122,125,140,143,147,153],"conventional":[13],"vehicles,":[14],"they":[15],"refuse":[16],"emissions":[17],"of":[18,51,61,100,115,124],"carbon":[19],"dioxide":[20],"and":[21,37,47,75,85,103,120,132,165,174],"harmful":[22],"gases":[23],"while":[24],"consume":[25],"no":[26,105],"fossil":[27],"fuel.":[28],"This":[29],"paper":[30],"presents":[31],"a":[32,38],"spatiotemporal":[33,39],"flow":[34,60,80,102,149],"3D":[35,68],"model":[36,69],"filter":[40],"method":[41],"clean":[43],"raw":[45],"data":[46,94],"adapt":[48],"several":[49],"types":[50],"convolutional":[52],"neural":[53,127],"networks":[54,128],"(CNNs)":[55],"predict":[57],"traffic":[59,79,101,148],"plug-in":[62],"hybrid":[63],"taxis":[65,119],"(PHETs).":[66],"The":[67],"divides":[70],"area":[72],"into":[73],"cells":[74],"directly":[76],"show":[77],"levels":[81],"in":[82,151],"each":[83,86],"minute":[84],"cell.":[87],"By":[88],"adapting":[89],"filter,":[91],"cleaned":[93],"clearly":[95],"indicates":[96],"main":[98],"trend":[99],"have":[104],"obvious":[106],"outliers.":[107],"Besides,":[108],"based":[109],"on":[110],"almost":[111],"30":[112,117],"million":[113],"orders":[114],"over":[116],"thousand":[118],"with":[121,161],"help":[123],"recent":[126],"such":[129],"VGG":[131],"Resnet,":[133],"our":[134],"prediction":[135,150],"is":[136,156],"very":[137],"close":[138],"reality.":[141],"Although":[142],"research":[144],"only":[145],"considers":[146],"Beijing,":[152],"methodological":[154],"framework":[155],"adaptive":[157],"other":[159],"cities":[160],"similar":[162],"dataset":[163],"available":[164],"provides":[166],"reference":[167],"for":[168],"dispatching":[169],"charging":[170],"opportunities":[171],"PHET":[173],"planning":[175],"time-saving":[176],"routes":[177]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
