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<bibitem type="J">   <ARLID>0506861</ARLID> <utime>20240903170546.1</utime><mtime>20190725235959.9</mtime>   <SCOPUS>84987679930</SCOPUS> <WOS>000351252000003</WOS>  <DOI>10.14311/NNW.2015.25.002</DOI>           <title language="eng" primary="1">Modelling Occupancy-Queue Relation Using Gaussian Process</title>  <specification> <page_count>18 s.</page_count> <media_type>P</media_type> </specification>   <serial><ARLID>cav_un_epca*0290321</ARLID><ISSN>1210-0552</ISSN><title>Neural Network World</title><part_num/><part_title/><volume_id>25</volume_id><volume>1 (2015)</volume><page_num>35-52</page_num><publisher><place/><name>Ústav informatiky AV ČR, v. v. i.</name><year/></publisher></serial>    <keyword>queue estimation</keyword>   <keyword>uncertainty</keyword>   <keyword>traffic model</keyword>   <keyword>Gaussian process</keyword>    <author primary="1"> <ARLID>cav_un_auth*0205734</ARLID> <full_dept language="cz">Adaptivní systémy</full_dept> <full_dept language="eng">Department of Adaptive Systems</full_dept> <department language="cz">AS</department> <department language="eng">AS</department>  <share>80</share> <name1>Přikryl</name1> <name2>Jan</name2> <institution>UTIA-B</institution> <country>CZ</country> <garant>K</garant> <fullinstit>Ústav teorie informace a automatizace AV ČR, v. v. i.</fullinstit> </author> <author primary="0"> <ARLID>cav_un_auth*0255838</ARLID>  <share>20</share> <name1>Kocijan</name1> <name2>J.</name2> <country>SI</country> </author>   <source> <url>http://library.utia.cas.cz/separaty/2019/AS/prikryl-0506861.pdf</url> </source>         <cas_special> <project> <ARLID>cav_un_auth*0001814</ARLID> <project_id>1M0572</project_id> <agency>GA MŠk</agency> </project> <project> <ARLID>cav_un_auth*0263551</ARLID> <project_id>MEB091015</project_id> <agency>GA MŠk</agency> <country>CZ</country> </project>  <abstract language="eng" primary="1">One of the key indicators of the quality of service for urban transportation control systems is the queue length. Even in unsaturated conditions, longer queues indicate longer travel delays and higher fuel consumption. With the exception of some expensive surveillance equipment, the queue length itself cannot be measured automatically, and manual measurement is both impractical and costly in a long term scenario. Hence, many mathematical models that express the queue length as a function of detector measurements are used in engineering practice, ranging from simple to elaborate ones. The method proposed in this paper makes use of detector time-occupancy, a complementary quantity to vehicle count, provided by most of the traffic detectors at no cost and disregarded by majority of existing approaches for various reasons. Our model is designed as a complement to existing methods. It is based on Gaussian-process model of the occupancy-queue relationship, it can handle data uncertainties, and it provides more information about the quality of the queue length prediction.</abstract>     <result_subspec>WOS</result_subspec> <RIV>BB</RIV> <FORD0>10000</FORD0> <FORD1>10100</FORD1> <FORD2>10103</FORD2>   <reportyear>2020</reportyear>      <num_of_auth>2</num_of_auth>  <inst_support> RVO:67985556 </inst_support>  <permalink>http://hdl.handle.net/11104/0298023</permalink>  <cooperation> <ARLID>cav_un_auth*0328371</ARLID> <name>Jozef Stefan Institute</name> <country>SI</country> </cooperation> <cooperation> <ARLID>cav_un_auth*0377558</ARLID> <name>ČVUT v Praze, Fakulta dopravní</name> <institution>ČVUT FD</institution> <country>CZ</country> </cooperation>  <confidential>S</confidential>          <unknown tag="mrcbT16-e">COMPUTERSCIENCE.ARTIFICIALINTELLIGENCE</unknown> <unknown tag="mrcbT16-f">0.500</unknown> <unknown tag="mrcbT16-g">0.059</unknown> <unknown tag="mrcbT16-h">6.2</unknown> <unknown tag="mrcbT16-i">0.0003</unknown> <unknown tag="mrcbT16-j">0.099</unknown> <unknown tag="mrcbT16-k">216</unknown> <unknown tag="mrcbT16-s">0.229</unknown> <unknown tag="mrcbT16-4">Q3</unknown> <unknown tag="mrcbT16-5">0.452</unknown> <unknown tag="mrcbT16-6">34</unknown> <unknown tag="mrcbT16-7">Q4</unknown> <unknown tag="mrcbT16-B">2.559</unknown> <unknown tag="mrcbT16-C">13.5</unknown> <unknown tag="mrcbT16-D">Q4</unknown> <unknown tag="mrcbT16-E">Q4</unknown> <unknown tag="mrcbT16-P">13.462</unknown> <arlyear>2015</arlyear>       <unknown tag="mrcbU14"> 84987679930 SCOPUS </unknown> <unknown tag="mrcbU24"> PUBMED </unknown> <unknown tag="mrcbU34"> 000351252000003 WOS </unknown> <unknown tag="mrcbU63"> cav_un_epca*0290321 Neural Network World 1210-0552 Roč. 25 č. 1 2015 35 52 Ústav informatiky AV ČR, v. v. i. </unknown> </cas_special> </bibitem>