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dc.contributor.authorLan, Lawrence W.en_US
dc.contributor.authorChiou, Yu-Chiunen_US
dc.contributor.authorLin, Zih-Shinen_US
dc.contributor.authorHsu, Chih-Chengen_US
dc.date.accessioned2014-12-08T15:06:54Z-
dc.date.available2014-12-08T15:06:54Z-
dc.date.issued2010-05-15en_US
dc.identifier.issn0378-4371en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.physa.2010.01.028en_US
dc.identifier.urihttp://hdl.handle.net/11536/5403-
dc.description.abstractModeling mixed traffic composed of motorcycles can be a challenging issue because many erratic motorcyclists may not follow the lane disciplines, particularly when traffic is congested. Based upon the refined cellular automaton (CA) model recently developed by the authors [LW. Lan, Y C Chiou. Z.S. Lin, C.C. Hsu, Physica A 388 (2009) 3917-3930], this paper further proposed a sophisticated CA model to elucidate the erratic motorcycle behaviours in mixed traffic contexts. In addition to the conventional moving forward and lane-change rules, the sophisticated CA model also explicated the lateral drift behaviour for cars moving in the same lane, the lateral drift behaviour for motorcycles breaking into two moving cars. and the transverse crossing behaviour for motorcycles through the gap between two stationary cars in the same lane. Fundamental diagrams and space-time trajectories for vehicles with various car-motorcycle mixed ratios are demonstrated (C) 2010 Elsevier B.V. All rights reserveden_US
dc.language.isoen_USen_US
dc.subjectCellular automatonen_US
dc.subjectMixed trafficen_US
dc.subjectMotorcycleen_US
dc.titleCellular automaton simulations for mixed traffic with erratic motorcycles' behavioursen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.physa.2010.01.028en_US
dc.identifier.journalPHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONSen_US
dc.citation.volume389en_US
dc.citation.issue10en_US
dc.citation.spage2077en_US
dc.citation.epage2089en_US
dc.contributor.department運輸與物流管理系 註:原交通所+運管所zh_TW
dc.contributor.departmentDepartment of Transportation and Logistics Managementen_US
dc.identifier.wosnumberWOS:000276421100013-
dc.citation.woscount8-
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