Study on Road Network Traffic Coordination Control Technique With Bus Priority
On the basis of distributed traffic control framework, fuzzy theory, and artificial neural networks technique, the road network traffic intelligent coordination control technique with bus priority was proposed. The whole road network was regarded as a large-scale system, and the subsystems were the...
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Veröffentlicht in: | IEEE transactions on human-machine systems 2009-05, Vol.39 (3), p.343-351 |
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creator | Guojiang Shen, Guojiang Shen Xiangjie Kong, Xiangjie Kong |
description | On the basis of distributed traffic control framework, fuzzy theory, and artificial neural networks technique, the road network traffic intelligent coordination control technique with bus priority was proposed. The whole road network was regarded as a large-scale system, and the subsystems were the intersections. Multiphase intelligent signal controller that controlled its own traffic and cooperated with its neighbors was installed at each intersection. By exchanging information collected from its social vehicle detectors and the bus detection and location devices, and cooperating with adjacent signal controllers, social vehicle coordination and bus priority in the whole road network were realized. Bus priority module, green observation module, and phase switch module comprised the hard core of the controller. In each module, the fuzzy rule base system was designed in detail. To improve the control system's robusticity, the fuzzy relations of the three modules were implemented by one neural network. The target of this proposed method was to maximize the possibility for vehicles to depart from the upstream intersection, and the traveling bus nearby the local intersection to pass the local intersection without stoppage while the utility efficiency of the green signal time was at a relatively high level. The actual application shows that the proposed method can decrease the average vehicle delay and average travel time effectively. |
doi_str_mv | 10.1109/TSMCC.2008.2005842 |
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The whole road network was regarded as a large-scale system, and the subsystems were the intersections. Multiphase intelligent signal controller that controlled its own traffic and cooperated with its neighbors was installed at each intersection. By exchanging information collected from its social vehicle detectors and the bus detection and location devices, and cooperating with adjacent signal controllers, social vehicle coordination and bus priority in the whole road network were realized. Bus priority module, green observation module, and phase switch module comprised the hard core of the controller. In each module, the fuzzy rule base system was designed in detail. To improve the control system's robusticity, the fuzzy relations of the three modules were implemented by one neural network. The target of this proposed method was to maximize the possibility for vehicles to depart from the upstream intersection, and the traveling bus nearby the local intersection to pass the local intersection without stoppage while the utility efficiency of the green signal time was at a relatively high level. The actual application shows that the proposed method can decrease the average vehicle delay and average travel time effectively.</description><identifier>ISSN: 1094-6977</identifier><identifier>ISSN: 2168-2291</identifier><identifier>EISSN: 1558-2442</identifier><identifier>EISSN: 2168-2305</identifier><identifier>DOI: 10.1109/TSMCC.2008.2005842</identifier><identifier>CODEN: ITCRFH</identifier><language>eng</language><publisher>New-York, NY: IEEE</publisher><subject>Applied sciences ; Artificial intelligence ; Artificial neural networks ; Artificial neural networks (ANNs) ; bus priority ; Buses (vehicles) ; Communication system traffic control ; Computer science; control theory; systems ; Connectionism. Neural networks ; coordination ; Exact sciences and technology ; Fuzzy ; Fuzzy control ; Fuzzy neural networks ; Fuzzy systems ; Ground, air and sea transportation, marine construction ; Intersections ; Modules ; Networks ; Neural networks ; Priorities ; road network ; Road transportation and traffic ; Road vehicles ; Roads ; Studies ; Switches ; Switching theory ; Traffic control ; Vehicle detection</subject><ispartof>IEEE transactions on human-machine systems, 2009-05, Vol.39 (3), p.343-351</ispartof><rights>2009 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2009</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c356t-902b920f2970d202dbfd227856d9bc779f30b2a2b038b88a8e118868366994453</citedby><cites>FETCH-LOGICAL-c356t-902b920f2970d202dbfd227856d9bc779f30b2a2b038b88a8e118868366994453</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4799127$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4799127$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=21733249$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Guojiang Shen, Guojiang Shen</creatorcontrib><creatorcontrib>Xiangjie Kong, Xiangjie Kong</creatorcontrib><title>Study on Road Network Traffic Coordination Control Technique With Bus Priority</title><title>IEEE transactions on human-machine systems</title><addtitle>TSMCC</addtitle><description>On the basis of distributed traffic control framework, fuzzy theory, and artificial neural networks technique, the road network traffic intelligent coordination control technique with bus priority was proposed. The whole road network was regarded as a large-scale system, and the subsystems were the intersections. Multiphase intelligent signal controller that controlled its own traffic and cooperated with its neighbors was installed at each intersection. By exchanging information collected from its social vehicle detectors and the bus detection and location devices, and cooperating with adjacent signal controllers, social vehicle coordination and bus priority in the whole road network were realized. Bus priority module, green observation module, and phase switch module comprised the hard core of the controller. In each module, the fuzzy rule base system was designed in detail. To improve the control system's robusticity, the fuzzy relations of the three modules were implemented by one neural network. The target of this proposed method was to maximize the possibility for vehicles to depart from the upstream intersection, and the traveling bus nearby the local intersection to pass the local intersection without stoppage while the utility efficiency of the green signal time was at a relatively high level. The actual application shows that the proposed method can decrease the average vehicle delay and average travel time effectively.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Artificial neural networks (ANNs)</subject><subject>bus priority</subject><subject>Buses (vehicles)</subject><subject>Communication system traffic control</subject><subject>Computer science; control theory; systems</subject><subject>Connectionism. Neural networks</subject><subject>coordination</subject><subject>Exact sciences and technology</subject><subject>Fuzzy</subject><subject>Fuzzy control</subject><subject>Fuzzy neural networks</subject><subject>Fuzzy systems</subject><subject>Ground, air and sea transportation, marine construction</subject><subject>Intersections</subject><subject>Modules</subject><subject>Networks</subject><subject>Neural networks</subject><subject>Priorities</subject><subject>road network</subject><subject>Road transportation and traffic</subject><subject>Road vehicles</subject><subject>Roads</subject><subject>Studies</subject><subject>Switches</subject><subject>Switching theory</subject><subject>Traffic control</subject><subject>Vehicle detection</subject><issn>1094-6977</issn><issn>2168-2291</issn><issn>1558-2442</issn><issn>2168-2305</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkE1LxDAQhoMouK7-Ab0EQTx1TaZpkxy1-AV-4a54DGmbslm7jSYtsv_erLt48DIzMM-8DA9Cx5RMKCXyYjZ9LIoJECLWJRMMdtCIZplIgDHYjTORLMkl5_voIIQFIZQxmY7Q07Qf6hV2HX51usZPpv92_gPPvG4aW-HCOV_bTvc2EoXreu9aPDPVvLNfg8Hvtp_jqyHgF2-dt_3qEO01ug3maNvH6O3melbcJQ_Pt_fF5UNSpVneJ5JAKYE0IDmpgUBdNjUAF1ley7LiXDYpKUFDSVJRCqGFoVSIXKR5LiVjWTpG55vcT-_iI6FXSxsq07a6M24ISvCMgMwzFsnTf-TCDb6LzykRPeWS8TRCsIEq70LwplGf3i61XylK1Fqw-hWs1oLVVnA8Otsm61DptvG6q2z4uwQagyFKHqOTDWeNMX9rxqWkwNMf46eCCQ</recordid><startdate>20090501</startdate><enddate>20090501</enddate><creator>Guojiang Shen, Guojiang Shen</creator><creator>Xiangjie Kong, Xiangjie Kong</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope></search><sort><creationdate>20090501</creationdate><title>Study on Road Network Traffic Coordination Control Technique With Bus Priority</title><author>Guojiang Shen, Guojiang Shen ; Xiangjie Kong, Xiangjie Kong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c356t-902b920f2970d202dbfd227856d9bc779f30b2a2b038b88a8e118868366994453</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Artificial neural networks</topic><topic>Artificial neural networks (ANNs)</topic><topic>bus priority</topic><topic>Buses (vehicles)</topic><topic>Communication system traffic control</topic><topic>Computer science; control theory; systems</topic><topic>Connectionism. Neural networks</topic><topic>coordination</topic><topic>Exact sciences and technology</topic><topic>Fuzzy</topic><topic>Fuzzy control</topic><topic>Fuzzy neural networks</topic><topic>Fuzzy systems</topic><topic>Ground, air and sea transportation, marine construction</topic><topic>Intersections</topic><topic>Modules</topic><topic>Networks</topic><topic>Neural networks</topic><topic>Priorities</topic><topic>road network</topic><topic>Road transportation and traffic</topic><topic>Road vehicles</topic><topic>Roads</topic><topic>Studies</topic><topic>Switches</topic><topic>Switching theory</topic><topic>Traffic control</topic><topic>Vehicle detection</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Guojiang Shen, Guojiang Shen</creatorcontrib><creatorcontrib>Xiangjie Kong, Xiangjie Kong</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE transactions on human-machine systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Guojiang Shen, Guojiang Shen</au><au>Xiangjie Kong, Xiangjie Kong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Study on Road Network Traffic Coordination Control Technique With Bus Priority</atitle><jtitle>IEEE transactions on human-machine systems</jtitle><stitle>TSMCC</stitle><date>2009-05-01</date><risdate>2009</risdate><volume>39</volume><issue>3</issue><spage>343</spage><epage>351</epage><pages>343-351</pages><issn>1094-6977</issn><issn>2168-2291</issn><eissn>1558-2442</eissn><eissn>2168-2305</eissn><coden>ITCRFH</coden><abstract>On the basis of distributed traffic control framework, fuzzy theory, and artificial neural networks technique, the road network traffic intelligent coordination control technique with bus priority was proposed. The whole road network was regarded as a large-scale system, and the subsystems were the intersections. Multiphase intelligent signal controller that controlled its own traffic and cooperated with its neighbors was installed at each intersection. By exchanging information collected from its social vehicle detectors and the bus detection and location devices, and cooperating with adjacent signal controllers, social vehicle coordination and bus priority in the whole road network were realized. Bus priority module, green observation module, and phase switch module comprised the hard core of the controller. In each module, the fuzzy rule base system was designed in detail. To improve the control system's robusticity, the fuzzy relations of the three modules were implemented by one neural network. The target of this proposed method was to maximize the possibility for vehicles to depart from the upstream intersection, and the traveling bus nearby the local intersection to pass the local intersection without stoppage while the utility efficiency of the green signal time was at a relatively high level. The actual application shows that the proposed method can decrease the average vehicle delay and average travel time effectively.</abstract><cop>New-York, NY</cop><pub>IEEE</pub><doi>10.1109/TSMCC.2008.2005842</doi><tpages>9</tpages></addata></record> |
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subjects | Applied sciences Artificial intelligence Artificial neural networks Artificial neural networks (ANNs) bus priority Buses (vehicles) Communication system traffic control Computer science control theory systems Connectionism. Neural networks coordination Exact sciences and technology Fuzzy Fuzzy control Fuzzy neural networks Fuzzy systems Ground, air and sea transportation, marine construction Intersections Modules Networks Neural networks Priorities road network Road transportation and traffic Road vehicles Roads Studies Switches Switching theory Traffic control Vehicle detection |
title | Study on Road Network Traffic Coordination Control Technique With Bus Priority |
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