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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on human-machine systems 2009-05, Vol.39 (3), p.343-351
Hauptverfasser: Guojiang Shen, Guojiang Shen, Xiangjie Kong, Xiangjie Kong
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 351
container_issue 3
container_start_page 343
container_title IEEE transactions on human-machine systems
container_volume 39
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
format Article
fullrecord <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_ieee_primary_4799127</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4799127</ieee_id><sourcerecordid>2301892291</sourcerecordid><originalsourceid>FETCH-LOGICAL-c356t-902b920f2970d202dbfd227856d9bc779f30b2a2b038b88a8e118868366994453</originalsourceid><addsrcrecordid>eNpdkE1LxDAQhoMouK7-Ab0EQTx1TaZpkxy1-AV-4a54DGmbslm7jSYtsv_erLt48DIzMM-8DA9Cx5RMKCXyYjZ9LIoJECLWJRMMdtCIZplIgDHYjTORLMkl5_voIIQFIZQxmY7Q07Qf6hV2HX51usZPpv92_gPPvG4aW-HCOV_bTvc2EoXreu9aPDPVvLNfg8Hvtp_jqyHgF2-dt_3qEO01ug3maNvH6O3melbcJQ_Pt_fF5UNSpVneJ5JAKYE0IDmpgUBdNjUAF1ley7LiXDYpKUFDSVJRCqGFoVSIXKR5LiVjWTpG55vcT-_iI6FXSxsq07a6M24ISvCMgMwzFsnTf-TCDb6LzykRPeWS8TRCsIEq70LwplGf3i61XylK1Fqw-hWs1oLVVnA8Otsm61DptvG6q2z4uwQagyFKHqOTDWeNMX9rxqWkwNMf46eCCQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>858469473</pqid></control><display><type>article</type><title>Study on Road Network Traffic Coordination Control Technique With Bus Priority</title><source>IEEE Electronic Library (IEL)</source><creator>Guojiang Shen, Guojiang Shen ; Xiangjie Kong, Xiangjie Kong</creator><creatorcontrib>Guojiang Shen, Guojiang Shen ; Xiangjie Kong, Xiangjie Kong</creatorcontrib><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><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>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&amp;idt=21733249$$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 &amp; Communications Abstracts</collection><collection>Mechanical &amp; 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 &amp; 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>
fulltext fulltext_linktorsrc
identifier ISSN: 1094-6977
ispartof IEEE transactions on human-machine systems, 2009-05, Vol.39 (3), p.343-351
issn 1094-6977
2168-2291
1558-2442
2168-2305
language eng
recordid cdi_ieee_primary_4799127
source IEEE Electronic Library (IEL)
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
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-26T12%3A38%3A06IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Study%20on%20Road%20Network%20Traffic%20Coordination%20Control%20Technique%20With%20Bus%20Priority&rft.jtitle=IEEE%20transactions%20on%20human-machine%20systems&rft.au=Guojiang%20Shen,%20Guojiang%20Shen&rft.date=2009-05-01&rft.volume=39&rft.issue=3&rft.spage=343&rft.epage=351&rft.pages=343-351&rft.issn=1094-6977&rft.eissn=1558-2442&rft.coden=ITCRFH&rft_id=info:doi/10.1109/TSMCC.2008.2005842&rft_dat=%3Cproquest_RIE%3E2301892291%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=858469473&rft_id=info:pmid/&rft_ieee_id=4799127&rfr_iscdi=true