Bioinspired Computational Intelligence and Transportation Systems: A Long Road Ahead
This paper capitalizes on the increasingly high relevance gained by data-intensive technologies in the development of intelligent transportation system, which calls for the progressive adoption of adaptive, self-learning methods for solving modeling, simulation, and optimization problems. In this re...
Gespeichert in:
Veröffentlicht in: | IEEE transactions on intelligent transportation systems 2020-02, Vol.21 (2), p.466-495 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 495 |
---|---|
container_issue | 2 |
container_start_page | 466 |
container_title | IEEE transactions on intelligent transportation systems |
container_volume | 21 |
creator | Del Ser, Javier Osaba, Eneko Sanchez-Medina, Javier J. Fister, Iztok Fister, Iztok |
description | This paper capitalizes on the increasingly high relevance gained by data-intensive technologies in the development of intelligent transportation system, which calls for the progressive adoption of adaptive, self-learning methods for solving modeling, simulation, and optimization problems. In this regard, certain mechanisms and processes observed in nature, including the animal brain, have proved themselves to excel not only in terms of efficiently capturing time-evolving stimuli, but also at undertaking complex tasks by virtue of mechanisms that can be extrapolated to computer algorithms and methods. This paper comprehensively reviews the state-of-the-art around the application of bioinspired methods to the challenges arising in the broad field of intelligent transportation system (ITS). This systematic survey is complemented by an initiatory taxonomic introduction to bioinspired computational intelligence, along with the basics of its constituent techniques. A focus is placed on which research niches are still unexplored by the community in different ITS subareas. The open issues and research directions for the practical implementation of ITS endowed with bioinspired computational intelligence are also discussed in detail. |
doi_str_mv | 10.1109/TITS.2019.2897377 |
format | Article |
fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_ieee_primary_8661647</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8661647</ieee_id><sourcerecordid>2352193223</sourcerecordid><originalsourceid>FETCH-LOGICAL-c341t-9122cdb76f500f4c8665d31c644b3bc50f64ceae1a867ecdca8258e23618e3cc3</originalsourceid><addsrcrecordid>eNo9kE1Lw0AQhhdRsFZ_gHhZ8Jy6s19JvNXiR6Eg2HhetptJTWmzcTc99N-bkOJphuF5h5eHkHtgMwCWPxXLYj3jDPIZz_JUpOkFmYBSWcIY6Mth5zLJmWLX5CbGXX-VCmBCipfa101s64AlXfhDe-xsV_vG7umy6XC_r7fYOKS2KWkRbE_6MBJ0fYodHuIzndOVb7b0y9uSzn_QlrfkqrL7iHfnOSXfb6_F4iNZfb4vF_NV4oSELsmBc1duUl0pxirpMq1VKcBpKTdi4xSrtHRoEWymU3SlsxlXGXKhIUPhnJiSx_FvG_zvEWNndv4Y-u7RcKE45IJz0VMwUi74GANWpg31wYaTAWYGeWaQZwZ55iyvzzyMmRoR__m-IGiZij92k2td</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2352193223</pqid></control><display><type>article</type><title>Bioinspired Computational Intelligence and Transportation Systems: A Long Road Ahead</title><source>IEEE Electronic Library (IEL)</source><creator>Del Ser, Javier ; Osaba, Eneko ; Sanchez-Medina, Javier J. ; Fister, Iztok ; Fister, Iztok</creator><creatorcontrib>Del Ser, Javier ; Osaba, Eneko ; Sanchez-Medina, Javier J. ; Fister, Iztok ; Fister, Iztok</creatorcontrib><description>This paper capitalizes on the increasingly high relevance gained by data-intensive technologies in the development of intelligent transportation system, which calls for the progressive adoption of adaptive, self-learning methods for solving modeling, simulation, and optimization problems. In this regard, certain mechanisms and processes observed in nature, including the animal brain, have proved themselves to excel not only in terms of efficiently capturing time-evolving stimuli, but also at undertaking complex tasks by virtue of mechanisms that can be extrapolated to computer algorithms and methods. This paper comprehensively reviews the state-of-the-art around the application of bioinspired methods to the challenges arising in the broad field of intelligent transportation system (ITS). This systematic survey is complemented by an initiatory taxonomic introduction to bioinspired computational intelligence, along with the basics of its constituent techniques. A focus is placed on which research niches are still unexplored by the community in different ITS subareas. The open issues and research directions for the practical implementation of ITS endowed with bioinspired computational intelligence are also discussed in detail.</description><identifier>ISSN: 1524-9050</identifier><identifier>EISSN: 1558-0016</identifier><identifier>DOI: 10.1109/TITS.2019.2897377</identifier><identifier>CODEN: ITISFG</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Adaptation models ; Adaptive systems ; Algorithms ; Artificial intelligence ; Atmospheric modeling ; autonomous and cooperative driving ; Bioinspired computational intelligence ; Biological system modeling ; Computational intelligence ; Computational modeling ; Computer simulation ; driver characterization ; Intelligence ; Intelligent transportation systems ; Optimization ; Predictive models ; route planning ; smart mobility ; State-of-the-art reviews ; Task complexity ; traffic forecasting</subject><ispartof>IEEE transactions on intelligent transportation systems, 2020-02, Vol.21 (2), p.466-495</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c341t-9122cdb76f500f4c8665d31c644b3bc50f64ceae1a867ecdca8258e23618e3cc3</citedby><cites>FETCH-LOGICAL-c341t-9122cdb76f500f4c8665d31c644b3bc50f64ceae1a867ecdca8258e23618e3cc3</cites><orcidid>0000-0001-7863-9910 ; 0000-0002-6418-1272 ; 0000-0003-2530-3182 ; 0000-0002-1260-9775</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8661647$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27903,27904,54736</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8661647$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Del Ser, Javier</creatorcontrib><creatorcontrib>Osaba, Eneko</creatorcontrib><creatorcontrib>Sanchez-Medina, Javier J.</creatorcontrib><creatorcontrib>Fister, Iztok</creatorcontrib><creatorcontrib>Fister, Iztok</creatorcontrib><title>Bioinspired Computational Intelligence and Transportation Systems: A Long Road Ahead</title><title>IEEE transactions on intelligent transportation systems</title><addtitle>TITS</addtitle><description>This paper capitalizes on the increasingly high relevance gained by data-intensive technologies in the development of intelligent transportation system, which calls for the progressive adoption of adaptive, self-learning methods for solving modeling, simulation, and optimization problems. In this regard, certain mechanisms and processes observed in nature, including the animal brain, have proved themselves to excel not only in terms of efficiently capturing time-evolving stimuli, but also at undertaking complex tasks by virtue of mechanisms that can be extrapolated to computer algorithms and methods. This paper comprehensively reviews the state-of-the-art around the application of bioinspired methods to the challenges arising in the broad field of intelligent transportation system (ITS). This systematic survey is complemented by an initiatory taxonomic introduction to bioinspired computational intelligence, along with the basics of its constituent techniques. A focus is placed on which research niches are still unexplored by the community in different ITS subareas. The open issues and research directions for the practical implementation of ITS endowed with bioinspired computational intelligence are also discussed in detail.</description><subject>Adaptation models</subject><subject>Adaptive systems</subject><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Atmospheric modeling</subject><subject>autonomous and cooperative driving</subject><subject>Bioinspired computational intelligence</subject><subject>Biological system modeling</subject><subject>Computational intelligence</subject><subject>Computational modeling</subject><subject>Computer simulation</subject><subject>driver characterization</subject><subject>Intelligence</subject><subject>Intelligent transportation systems</subject><subject>Optimization</subject><subject>Predictive models</subject><subject>route planning</subject><subject>smart mobility</subject><subject>State-of-the-art reviews</subject><subject>Task complexity</subject><subject>traffic forecasting</subject><issn>1524-9050</issn><issn>1558-0016</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE1Lw0AQhhdRsFZ_gHhZ8Jy6s19JvNXiR6Eg2HhetptJTWmzcTc99N-bkOJphuF5h5eHkHtgMwCWPxXLYj3jDPIZz_JUpOkFmYBSWcIY6Mth5zLJmWLX5CbGXX-VCmBCipfa101s64AlXfhDe-xsV_vG7umy6XC_r7fYOKS2KWkRbE_6MBJ0fYodHuIzndOVb7b0y9uSzn_QlrfkqrL7iHfnOSXfb6_F4iNZfb4vF_NV4oSELsmBc1duUl0pxirpMq1VKcBpKTdi4xSrtHRoEWymU3SlsxlXGXKhIUPhnJiSx_FvG_zvEWNndv4Y-u7RcKE45IJz0VMwUi74GANWpg31wYaTAWYGeWaQZwZ55iyvzzyMmRoR__m-IGiZij92k2td</recordid><startdate>20200201</startdate><enddate>20200201</enddate><creator>Del Ser, Javier</creator><creator>Osaba, Eneko</creator><creator>Sanchez-Medina, Javier J.</creator><creator>Fister, Iztok</creator><creator>Fister, Iztok</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0001-7863-9910</orcidid><orcidid>https://orcid.org/0000-0002-6418-1272</orcidid><orcidid>https://orcid.org/0000-0003-2530-3182</orcidid><orcidid>https://orcid.org/0000-0002-1260-9775</orcidid></search><sort><creationdate>20200201</creationdate><title>Bioinspired Computational Intelligence and Transportation Systems: A Long Road Ahead</title><author>Del Ser, Javier ; Osaba, Eneko ; Sanchez-Medina, Javier J. ; Fister, Iztok ; Fister, Iztok</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c341t-9122cdb76f500f4c8665d31c644b3bc50f64ceae1a867ecdca8258e23618e3cc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adaptation models</topic><topic>Adaptive systems</topic><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Atmospheric modeling</topic><topic>autonomous and cooperative driving</topic><topic>Bioinspired computational intelligence</topic><topic>Biological system modeling</topic><topic>Computational intelligence</topic><topic>Computational modeling</topic><topic>Computer simulation</topic><topic>driver characterization</topic><topic>Intelligence</topic><topic>Intelligent transportation systems</topic><topic>Optimization</topic><topic>Predictive models</topic><topic>route planning</topic><topic>smart mobility</topic><topic>State-of-the-art reviews</topic><topic>Task complexity</topic><topic>traffic forecasting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Del Ser, Javier</creatorcontrib><creatorcontrib>Osaba, Eneko</creatorcontrib><creatorcontrib>Sanchez-Medina, Javier J.</creatorcontrib><creatorcontrib>Fister, Iztok</creatorcontrib><creatorcontrib>Fister, Iztok</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>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on intelligent transportation systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Del Ser, Javier</au><au>Osaba, Eneko</au><au>Sanchez-Medina, Javier J.</au><au>Fister, Iztok</au><au>Fister, Iztok</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bioinspired Computational Intelligence and Transportation Systems: A Long Road Ahead</atitle><jtitle>IEEE transactions on intelligent transportation systems</jtitle><stitle>TITS</stitle><date>2020-02-01</date><risdate>2020</risdate><volume>21</volume><issue>2</issue><spage>466</spage><epage>495</epage><pages>466-495</pages><issn>1524-9050</issn><eissn>1558-0016</eissn><coden>ITISFG</coden><abstract>This paper capitalizes on the increasingly high relevance gained by data-intensive technologies in the development of intelligent transportation system, which calls for the progressive adoption of adaptive, self-learning methods for solving modeling, simulation, and optimization problems. In this regard, certain mechanisms and processes observed in nature, including the animal brain, have proved themselves to excel not only in terms of efficiently capturing time-evolving stimuli, but also at undertaking complex tasks by virtue of mechanisms that can be extrapolated to computer algorithms and methods. This paper comprehensively reviews the state-of-the-art around the application of bioinspired methods to the challenges arising in the broad field of intelligent transportation system (ITS). This systematic survey is complemented by an initiatory taxonomic introduction to bioinspired computational intelligence, along with the basics of its constituent techniques. A focus is placed on which research niches are still unexplored by the community in different ITS subareas. The open issues and research directions for the practical implementation of ITS endowed with bioinspired computational intelligence are also discussed in detail.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TITS.2019.2897377</doi><tpages>30</tpages><orcidid>https://orcid.org/0000-0001-7863-9910</orcidid><orcidid>https://orcid.org/0000-0002-6418-1272</orcidid><orcidid>https://orcid.org/0000-0003-2530-3182</orcidid><orcidid>https://orcid.org/0000-0002-1260-9775</orcidid></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | ISSN: 1524-9050 |
ispartof | IEEE transactions on intelligent transportation systems, 2020-02, Vol.21 (2), p.466-495 |
issn | 1524-9050 1558-0016 |
language | eng |
recordid | cdi_ieee_primary_8661647 |
source | IEEE Electronic Library (IEL) |
subjects | Adaptation models Adaptive systems Algorithms Artificial intelligence Atmospheric modeling autonomous and cooperative driving Bioinspired computational intelligence Biological system modeling Computational intelligence Computational modeling Computer simulation driver characterization Intelligence Intelligent transportation systems Optimization Predictive models route planning smart mobility State-of-the-art reviews Task complexity traffic forecasting |
title | Bioinspired Computational Intelligence and Transportation Systems: A Long Road Ahead |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-25T08%3A09%3A08IST&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=Bioinspired%20Computational%20Intelligence%20and%20Transportation%20Systems:%20A%20Long%20Road%20Ahead&rft.jtitle=IEEE%20transactions%20on%20intelligent%20transportation%20systems&rft.au=Del%20Ser,%20Javier&rft.date=2020-02-01&rft.volume=21&rft.issue=2&rft.spage=466&rft.epage=495&rft.pages=466-495&rft.issn=1524-9050&rft.eissn=1558-0016&rft.coden=ITISFG&rft_id=info:doi/10.1109/TITS.2019.2897377&rft_dat=%3Cproquest_RIE%3E2352193223%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=2352193223&rft_id=info:pmid/&rft_ieee_id=8661647&rfr_iscdi=true |