Short-term travel flow prediction method based on FCM-clustering and ELM
Short-term travel flow prediction has been the core of the intelligent transport systems (ITS). An advanced method based on fuzzy C-means (FCM) and extreme learning machine (ELM) has been discussed by analyzing prediction model. First, this model takes advantages of ability to adapt to nonlinear sys...
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| Veröffentlicht in: | Journal of Central South University 2017-06, Vol.24 (6), p.1344-1350 |
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| container_title | Journal of Central South University |
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| creator | 王星超 胡坚明 梁伟 张毅 |
| description | Short-term travel flow prediction has been the core of the intelligent transport systems (ITS). An advanced method based on fuzzy C-means (FCM) and extreme learning machine (ELM) has been discussed by analyzing prediction model. First, this model takes advantages of ability to adapt to nonlinear systems and the fast speed of ELM algorithm. Second, with FCM-clustering function,this novel model can get the clusters and the membership in the same cluster, which means that the associated observation points have been chosen. Therefore, the spatial relations can be used by giving the weight to every observation points when the model trains and tests the ELM. Third, by analyzing the actual data in Haining City in 2016, the feasibility and advantages of FCM-ELM prediction model have been shown when compared with other prediction algorithms. |
| doi_str_mv | 10.1007/s11771-017-3538-1 |
| format | Article |
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An advanced method based on fuzzy C-means (FCM) and extreme learning machine (ELM) has been discussed by analyzing prediction model. First, this model takes advantages of ability to adapt to nonlinear systems and the fast speed of ELM algorithm. Second, with FCM-clustering function,this novel model can get the clusters and the membership in the same cluster, which means that the associated observation points have been chosen. Therefore, the spatial relations can be used by giving the weight to every observation points when the model trains and tests the ELM. Third, by analyzing the actual data in Haining City in 2016, the feasibility and advantages of FCM-ELM prediction model have been shown when compared with other prediction algorithms.</description><identifier>ISSN: 2095-2899</identifier><identifier>EISSN: 2227-5223</identifier><identifier>DOI: 10.1007/s11771-017-3538-1</identifier><language>eng</language><publisher>Changsha: Central South University</publisher><subject>Clustering ; Clusters ; Dynamical systems ; ELM ; Engineering ; extreme ; FCM-clustering;spatio-temporal ; Feasibility studies ; flow ; Fuzzy systems ; intelligent ; Intelligent transportation systems ; ITS ; learning ; machine ; Mathematical models ; Metallic Materials ; Neural networks ; Nonlinear dynamics ; prediction ; relation ; systems ; transportation ; travel</subject><ispartof>Journal of Central South University, 2017-06, Vol.24 (6), p.1344-1350</ispartof><rights>Central South University Press and Springer-Verlag GmbH Germany 2017</rights><rights>Copyright Springer Science & Business Media 2017</rights><rights>Copyright © Wanfang Data Co. 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Cent. South Univ</addtitle><addtitle>Journal of Central South University of Technology</addtitle><description>Short-term travel flow prediction has been the core of the intelligent transport systems (ITS). An advanced method based on fuzzy C-means (FCM) and extreme learning machine (ELM) has been discussed by analyzing prediction model. First, this model takes advantages of ability to adapt to nonlinear systems and the fast speed of ELM algorithm. Second, with FCM-clustering function,this novel model can get the clusters and the membership in the same cluster, which means that the associated observation points have been chosen. Therefore, the spatial relations can be used by giving the weight to every observation points when the model trains and tests the ELM. Third, by analyzing the actual data in Haining City in 2016, the feasibility and advantages of FCM-ELM prediction model have been shown when compared with other prediction algorithms.</description><subject>Clustering</subject><subject>Clusters</subject><subject>Dynamical systems</subject><subject>ELM</subject><subject>Engineering</subject><subject>extreme</subject><subject>FCM-clustering;spatio-temporal</subject><subject>Feasibility studies</subject><subject>flow</subject><subject>Fuzzy systems</subject><subject>intelligent</subject><subject>Intelligent transportation systems</subject><subject>ITS</subject><subject>learning</subject><subject>machine</subject><subject>Mathematical models</subject><subject>Metallic Materials</subject><subject>Neural networks</subject><subject>Nonlinear dynamics</subject><subject>prediction</subject><subject>relation</subject><subject>systems</subject><subject>transportation</subject><subject>travel</subject><issn>2095-2899</issn><issn>2227-5223</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp9kTFPwzAQhS0EElXpD2CLxIgMPl_qJCOqWorUigGYLTtx0qDUae2Utvx6XAUBE5N90vve3b0j5BrYHTCW3HuAJAHKIKE4xpTCGRlwzhM65hzPw59lY8rTLLskI-9rzRC4QJGJAZm_rFrX0c64ddQ59WGaqGzafbRxpqjzrm5ttDbdqi0irbwpolDPJkuaNzsfmNpWkbJFNF0sr8hFqRpvRt_vkLzNpq-TOV08Pz5NHhY0x5R1VOeIHFHzUhiThkIXmCpRaoEAecwYy2JRCCxjKFAbbXicMcEQsVQsj1Mcktved69sqWwl39uds6Gj_LTVsTgctDQ8JBEgwKC-6dUb1253xne_csggAZGyMM6QQK_KXeu9M6XcuHqt3FECk6eEZZ-wDL7ylLCEwPCe8ZtTDMb9cf4H-h4nX7W22gbup1MSVg8nYWHFLxgvh6Y</recordid><startdate>20170601</startdate><enddate>20170601</enddate><creator>王星超;胡坚明;梁伟;张毅</creator><general>Central South University</general><general>Springer Nature B.V</general><general>Department of Control Science and Engineering, Tongji University, Shanghai 201804, China%Department of Automation, Tsinghua University, Beijing 100084, China</general><scope>2RA</scope><scope>92L</scope><scope>CQIGP</scope><scope>W92</scope><scope>~WA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>2B.</scope><scope>4A8</scope><scope>92I</scope><scope>93N</scope><scope>PSX</scope><scope>TCJ</scope></search><sort><creationdate>20170601</creationdate><title>Short-term travel flow prediction method based on FCM-clustering and ELM</title><author>王星超;胡坚明;梁伟;张毅</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c380t-bc33233b2f6ee8c33bd38a6fb6311c4000946d63f41d3bebe249060333fa0c483</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Clustering</topic><topic>Clusters</topic><topic>Dynamical systems</topic><topic>ELM</topic><topic>Engineering</topic><topic>extreme</topic><topic>FCM-clustering;spatio-temporal</topic><topic>Feasibility studies</topic><topic>flow</topic><topic>Fuzzy systems</topic><topic>intelligent</topic><topic>Intelligent transportation systems</topic><topic>ITS</topic><topic>learning</topic><topic>machine</topic><topic>Mathematical models</topic><topic>Metallic Materials</topic><topic>Neural networks</topic><topic>Nonlinear dynamics</topic><topic>prediction</topic><topic>relation</topic><topic>systems</topic><topic>transportation</topic><topic>travel</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>王星超;胡坚明;梁伟;张毅</creatorcontrib><collection>中文科技期刊数据库</collection><collection>中文科技期刊数据库-CALIS站点</collection><collection>中文科技期刊数据库-7.0平台</collection><collection>中文科技期刊数据库-工程技术</collection><collection>中文科技期刊数据库- 镜像站点</collection><collection>CrossRef</collection><collection>Wanfang Data Journals - Hong Kong</collection><collection>WANFANG Data Centre</collection><collection>Wanfang Data Journals</collection><collection>万方数据期刊 - 香港版</collection><collection>China Online Journals (COJ)</collection><collection>China Online Journals (COJ)</collection><jtitle>Journal of Central South University</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>王星超;胡坚明;梁伟;张毅</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Short-term travel flow prediction method based on FCM-clustering and ELM</atitle><jtitle>Journal of Central South University</jtitle><stitle>J. Cent. South Univ</stitle><addtitle>Journal of Central South University of Technology</addtitle><date>2017-06-01</date><risdate>2017</risdate><volume>24</volume><issue>6</issue><spage>1344</spage><epage>1350</epage><pages>1344-1350</pages><issn>2095-2899</issn><eissn>2227-5223</eissn><abstract>Short-term travel flow prediction has been the core of the intelligent transport systems (ITS). An advanced method based on fuzzy C-means (FCM) and extreme learning machine (ELM) has been discussed by analyzing prediction model. First, this model takes advantages of ability to adapt to nonlinear systems and the fast speed of ELM algorithm. Second, with FCM-clustering function,this novel model can get the clusters and the membership in the same cluster, which means that the associated observation points have been chosen. Therefore, the spatial relations can be used by giving the weight to every observation points when the model trains and tests the ELM. Third, by analyzing the actual data in Haining City in 2016, the feasibility and advantages of FCM-ELM prediction model have been shown when compared with other prediction algorithms.</abstract><cop>Changsha</cop><pub>Central South University</pub><doi>10.1007/s11771-017-3538-1</doi><tpages>7</tpages></addata></record> |
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| ispartof | Journal of Central South University, 2017-06, Vol.24 (6), p.1344-1350 |
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| source | SpringerLink Journals; Alma/SFX Local Collection |
| subjects | Clustering Clusters Dynamical systems ELM Engineering extreme FCM-clustering spatio-temporal Feasibility studies flow Fuzzy systems intelligent Intelligent transportation systems ITS learning machine Mathematical models Metallic Materials Neural networks Nonlinear dynamics prediction relation systems transportation travel |
| title | Short-term travel flow prediction method based on FCM-clustering and ELM |
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