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ID:37340783
大小:6.65 MB
页数:64页
时间:2019-05-22
《优化速度模型的密度波与能耗研究及车辆启动时间实测分析》由会员上传分享,免费在线阅读,更多相关内容在学术论文-天天文库。
1、广西大学硕士学位论文优化速度模型的密度波与能耗研究及车辆启动时间实测分析姓名:莫业柳申请学位级别:硕士专业:理论物理指导教师:薛郁20080626并考虑实际交通中合作驾驶车辆对速度差的敏感性,在Wilson跟驰模型基础上,进一步提出多重“向前看”多速度差跟驰模型。应用线性稳定性分析分别导出两个改进模型的稳定性判据,分析了两个改进模型处于稳定区域由速度波动引起的附加能耗,以及在非稳定区域,利用动能定理考察了车流拥堵区域内单辆车的平均能耗情况,理论上证明了车流愈稳定能耗愈低的结论。(3)考虑司机反应延迟时间对交通流的影响,选取城市十
2、字路口进行不同车型车辆启动时间的实测,得到不同车型车辆的平均启动时间以及启动时间分布。最后,对全文进行了总结,并指出需要进一步研究的问题。关键词:交通流跟驰模型优化速度ml【dv方程智能交通系统(ITS)稳定性能耗交通实测ⅡDENSITYWAVEANDENERGYCONSUMPTIONOFoPTIMALVELoCITYMoDELANDEMPIRICALDATAoFSTARTINGTIMEFORVEHICLESABSTRACTWithincreasingtrafficjams,energyconsumptionandtraffic
3、pollutionareexertinggreatpressureonsocietyandcausingenormouseconomiclosses.Howtomakeeffectiveuseofexistingtransportationresourcessoastoimprovetherelationshipbetweentrafficdemandandsupply,andhowtoguidetransportdevelopmentandoperationwiththeaidofscientifictheorieshaveb
4、ecomeanimportantissuewhichdrawsgeneralconcernoftheinternationalcommunity.Overthepastdecades,alotofscholarsindifferentfieldshaveconductedextensiveresearchonthissubjectandproposedavarietyoftrafficflowtheoriesandmodels,yieldedanenormousamountofresults.Inthisdissertation
5、,basedontheexistingcar-followingmodelsoftrafficflow,twoimprovedmultiplecar-followingmodelsarepresented.Itisplacedstressontheanalysisofstabilityandenergyconsumption.Thenumericalsimulationisgoodconsistentwiththecorrespondingtheoreticalanalysis.Moreover,startingtimeford
6、ifferentvehiclesismeasuredandanalyzed.Thisdissertationconsistsofthefollowingthreemainparts:IH(1)Onetypeof”MultipleLook-ahead¨optimalvelocitycar-followingmodelsproposedbyWilsoneta1.isresearchedwithperturbationtheory.Inunstableregion,thenon-linearcharacteristicsofdensi
7、tywavearedescribedbythemKdVequation.Inphasediagramthetheoreticalanalysisiscoincident、加mthesimulationvalue.Recently,Yuk/aL[991presentedthetrafficjamswithoutbottlenecks-experimentalevidencethattheemergenceofatrafficjamisacollectivephenomenon,andajamisgeneratedspontaneo
8、uslyonlyiftheaveragevehicledensityexceedsthecriticalvalue.Weoftenencounterajamevenifwehavenoapparentreasonssuchastrafficaccidentsor
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