{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T00:02:43Z","timestamp":1746230563071},"reference-count":16,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2011,8]]},"abstract":"<jats:p> In the metropolitan region, most congestion or traffic jams are caused by the uneven distribution of traffic flow that creates bottleneck points where the traffic volume exceeds the road capacity. Additionally, unexpected incidents are the next most probable cause of these bottleneck regions. Moreover, most drivers are driving based on their empirical experience without awareness of real-time traffic situations. This unintelligent traffic behavior can make the congestion problem worse. Prediction based route guidance systems show great improvements in solving the inefficient diversion strategy problem by estimating future travel time when calculating accurate travel time is difficult. However, performances of machine learning based prediction models that are based on the historical data set degrade sharply during a congestion situation. This paper develops a new navigation system for reducing travel time of an individual driver and distributing the flow of urban traffic efficiently in order to reduce the occurrence of congestion. Compared with previous route guidance systems, the results reveal that our system, applying the advanced multi-lane prediction based real-time fastest path (AMPRFP) algorithm, can significantly reduce the travel time especially when drivers travel in a complex route environment and face frequent congestion problems. Unlike the previous system,<jats:sup>1<\/jats:sup> it can be applied either for single lane or multi-lane urban traffic networks where the reason for congestion is significantly complex. We also demonstrate the advantages of this system and verify the results using real highway traffic data and a synthetic experiment. <\/jats:p>","DOI":"10.1142\/s0218213011000346","type":"journal-article","created":{"date-parts":[[2011,5,23]],"date-time":"2011-05-23T01:32:18Z","timestamp":1306114338000},"page":"753-781","source":"Crossref","is-referenced-by-count":1,"title":["MACHINE LEARNING ON CONGESTION ANALYSIS BASED REAL-TIME NAVIGATION SYSTEM"],"prefix":"10.1142","volume":"20","author":[{"given":"KAI","family":"CHEN","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Florida International University, 10555 W. Flagler St., Miami, Florida, 33174, USA"}]},{"given":"KIA","family":"MAKKI","sequence":"additional","affiliation":[{"name":"College of Engineering and Information Technology, Technological University of America, 3700 Coconut Creek Parkway, Coconut Creek, Florida, 33066, USA"}]},{"given":"NIKI","family":"PISSINOU","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Florida International University, 10555 W. Flagler St., Miami, Florida, 33174, USA"}]}],"member":"219","published-online":{"date-parts":[[2012,4,30]]},"reference":[{"key":"rf2","volume":"12","author":"Faouzi N. E.","journal-title":"Information Fusion"},{"key":"rf4","volume":"32","author":"Zhan F. B.","journal-title":"Transportation Science"},{"key":"rf6","author":"Bhavsar P.","journal-title":"Transportation Research Board"},{"key":"rf7","author":"Messer C. J.","journal-title":"Transportation Research Board"},{"key":"rf11","volume":"9","author":"van Lint J. W. C.","journal-title":"IEEE Trans. Intelligent Transportation System"},{"key":"rf12","first-page":"163","author":"Park D. J.","journal-title":"Transportation Research Record 1617"},{"key":"rf13","volume":"5","author":"Wu C. H.","journal-title":"IEEE Trans. Intelligent Transportation Systems"},{"key":"rf15","volume":"9","author":"Du J.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"rf16","doi-asserted-by":"publisher","DOI":"10.1080\/01441640500361108"},{"key":"rf18","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2009.02.004"},{"key":"rf21","volume":"32","author":"Zhan F. B.","journal-title":"Transportation Science"},{"key":"rf22","doi-asserted-by":"publisher","DOI":"10.1007\/BF01386390"},{"key":"rf23","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"rf24","doi-asserted-by":"publisher","DOI":"10.1109\/72.788640"},{"key":"rf26","volume-title":"mySVM\u2013Manual","author":"Ruping S.","year":"2000"},{"key":"rf29","unstructured":"C. D. \u00a0 Cooper  \n         and  D. K. \u00a0 Keely   ( Civil & Environmental Engineering Department, University of Central Florida ,  2004 ) ."}],"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213011000346","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T03:40:37Z","timestamp":1565149237000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218213011000346"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011,8]]},"references-count":16,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2012,4,30]]},"published-print":{"date-parts":[[2011,8]]}},"alternative-id":["10.1142\/S0218213011000346"],"URL":"https:\/\/doi.org\/10.1142\/s0218213011000346","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"value":"0218-2130","type":"print"},{"value":"1793-6349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2011,8]]}}}