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ID:52886201
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页数:8页
时间:2020-03-31
《基于离散选择理论的跟驰模型.pdf》由会员上传分享,免费在线阅读,更多相关内容在工程资料-天天文库。
1、第12卷第5期交通运输系统工程与信息Vol.12No.52012年10月JournalofTransportationSystemsEngineeringandInformationTechnologyOctober2012文章编号:1009-6744(2012)05-0031-08基于离散选择理论的跟驰模型*郑建,铃木弘司,藤田素弘(名古屋工业大学工学院,名古屋466-8555,日本)摘要:基于离散选择理论,提出了车辆跟驰模型.考虑七个选择肢中的加减速度,采用选择肢特定参数反映出不同选择肢的吸引程度.为了避免换道
2、行为的干扰,利用HOV车道的车辆数据对模型进行标定和验证.结果显示,大多数的标定参数在95%的置信水平下都是较为显著的.此外,模型反映出的不同的车头间距及速度下的驾驶员对不同加减速度的选择趋势与日常驾驶行为完全吻合.验证结果表明,对于实际状况下被选择的选择肢,大于1/7(每个选择肢的平均预测概率)的预测概率的比例大于87%.从观测比例的角度看,实际观测值与模型的预测值之间差异并不显著.最后应用模型模拟30分钟的单车道交通状况.关键词:智能交通;交通仿真;跟驰模型;离散选择理论;HOV车道中图分类号:U268.6文献
3、标识码:AACar-followingModelBasedonDiscreteChoiceTheoryZHENGJian,SUZUKIKoji,FUJITAMotohiro(GraduateSchoolofEngineering,NagayaInstituteofTechnology,Nagoya466-8555,Japan)Abstract:Inthecar-followingmodelproposedinthispaper,theaccelerationanddecelerationareincorporate
4、dintosevenalternatives,andalternative-specificparametersareadoptedtocapturedifferentattractivenessofeachalternative.Toavoidinterferencesfromlane-changingbehavior,themodelisestimatedandvalidatedbytrajectorydatainthehighoccupancyvehicle(HOV)lane.Fromtheestimatio
5、nresults,mostestimatedparametersaresignificantlydifferentfromzeroat95%confidencelevel,whichindicatesthatthechosenvariablesandtheclassificationofalternativesareeffectiveandreasonable.Inaddition,accordingtotheproposedmodel,thechangingtendenciesofchoosingdifferen
6、talternativesatdifferentspeedsandgapsarecompletelyconsistentwithcommondrivingbehaviors.Thevalidationresultsshowthat,forthechosenalternatives,thepercentagesofpredictedprobabilitiesthatarelargerthan1/7(theaverageprobabilityofchoosingeachalternative)aremorethan87
7、%intheuseddatasets.Fromthesharesofobservationspointofview,thereareonlyminordifferencesbetweentheobservedandpredictedresults.Finally,thismodelisappliedtosimulatethe30-minutesinglelanetrafficconditions.Throughsimulationresults,itcanbefoundthattheproposedmodelcan
8、accuratelyrepresenttherealtrafficconditionsonthemacroscopiclevel,while,onthemicroscopiclevel,therearesomedefectsneededtobefurtherimproved.Keywords:intelligenttransportation;traffic
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