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简介：Automobileaccidentsareasfamiliarasthecommoncoldbutfarmoredeadly.

简介：英美国家交通十分发达，需要去某地，坐哪路车，相当便利。可以问路人、警察．也可以打电话询问。在国外．尽管候车的人不多，但他们总是会自赏排队等车、上车。英美人遵循“Firstcome，firstserved．”的行为准则。如果有人不守秩序，会遭到人们的蔑视。

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简介：Trafficruleisakeyfactoraffectingtrafficflowandsafety.Wedevelopourmodels,includingthecellularautomatatrafficflowmodelaswellasthelinearregressionone,aimingatcalculatingtrafficflowandevaluatingsafetyconditionswithvariedtrafficrules.Then,wethoroughlyinvestigatefourtypesofpathsinafreeway,namelytwostraightlanes,threestraightlanes,ramps,androundaboutsascasestudiesanddiscussthedifferenttrafficrulesascomparison.Theresultsdemonstratethat“Keep-Right-Except-To-Pass”ruleisnotaseffectiveasthefreeruleinpromotingtrafficflow;however,thisruleensuressafetyfordriversbetterthanthefreerule.Additionally,anewtrafficrule,whichsetsdifferentpostedspeedlimitsforadjacentlanes,isproposedtopromotebettertrafficflowwithsafetyrequirementssatisfied.Furthermore,weapplyeffectiverulesandalternatives,leftdrivingnormsaswellasintelligentsystemasextensionandobtainbetterresults.Finally,model’ssensitivityanalysisregardingtoprobabilityofdeceleratingandpostedspeedlimitsprovesthestabilityofourresults.

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简介：这篇文章集中于识别分享文件对等(P2P)(例如BitTorrent(BT))在一个树桩网络的边阶的交通。由分析应用程序的协议和交通，一个单身的用户的分享文件的P2P交通与极大地不同，这被发现传统并且另外的P2P(例如QQ)申请在深奥远程主机和遥远的端口的分发的交通。因此，一个方法基于远程主机(RHD)的组件和遥远的港口(RPD)的组件被建议识别象BT一样交通。这个方法仅仅在一个树桩网络依靠每台用户主机的流动信息，并且没有包收费载重需要被监视。在间隔，为并发的传播控制协议的立刻的RHD和为每台主机的用户数据包协议流动通过由每流动的远程主机属于的树桩网络组织流动分别地是计算的。在给定的条件上，立刻的RPD通过由遥远的港口组织流动修改立刻的RHD被计算。一位主人是否一直在使用象BT一样应用，能为时间的一个时期从立刻的RHD或平均RHD被推出。基于交通，特征是对比基于内容的方法识别变化多端的分享文件的P2P交通合适的更多的建议方法。试验性的结果证明这个方法与高精确性是有效的。

简介：Ｉ．ＩｎｔｒｏｄｕｃｔｉｏｎＩｔｈａｓｌｏｎｇｂｅｅｎｒｅｃｏｇｎｉｚｅｄｔｈａｔｒｏａｄａｃｉｄｅｎｔｓａｒｅｔｈｅｒｅｓｕｌｔｏｆｔｈｅｃｏｍｂｉｎｅｄｅｆｅｃｔｓｏｆｂｅｈａｖｉｏｒａｌ，ｔｅｃｈｎｏｌｏｇｉｃａｌａｎｄｅｎｖｉｒｏｎｍｅｎｔａ...

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简介：Macro-trafficmodelisaneffectivetoolforsupportingtrafficprogrammertoThisarticleisbasedonthesoftwareproductsVISEM(demandmodel)andVISUM(supplymodel)fromPTVCompanyofGermany,andstudiesmacro-trafficmodelonthebasisofconsideringthattrafficsystemisaninteractivesystemofbetweenasupplysystemandademandsystem.

简介：不同路由策略可以在因特网上导致交通的不同行为。我们由thedetrended变化分析(DFA)为三典型路由策略分析交通数据的关联并且发现数据的关联的度能被划分成三个区域，即弱、中等、强壮的关联。DFAscalings是在两个的常数弱、强壮的关联，但是monotonically的区域在中等关联的区域增加。我们建议在复杂网络上把交通看作三个阶段更好，即免费，就当二个阶段以前相信了，缓冲区，和拥挤分阶段执行，即免费并且拥挤阶段。

简介：ThereisnodoubtthattheurbanpublictrafficoperationsinChinasufferlossesandhenceitisnecessarytostudyresonablesubsidymechanismsfortheurbanpublictraffic.Onthebasisofanalyzingthemodesoftheurbanpublictrafficmanagement,weclassifythefinanciallossesintothepolicylossandthemanagementloss,accordingtowhetherthecostcanbecompensatedornot.Inaddition,aseriesofcriteriaareintroducedinordertoefficientlydecidetheamountofsubsidy.Finally,thispaperalsoexaminesthewayofbiddingadministration,whichisconsideredtobepromisinginthefutureofurbanpublictraffic.

简介：Objective:Thereisnosaferwaytotransportachildthanaschoolbus.FatalcrashesinvolvingoccupantsareextremelyrareeventsintheUS.Inrecentyears,schoolbustransportationbegantodevelopinChina.WewanttobringadvancedexperienceonschoolbussafetyinWesterncountriessuchastheUStodevelopingcountries.Methods:WesearchedthepapersrelatedtoschoolbussafetyfromMedline,ChineseScientificJournalsDatabaseandtheWeboftheNationalHighwayTrafficSafetyAdministration(NHTSA).Results:Therewereonly9papersrelatedtoschoolbussafety,whichshowedthathigherlevelsofsafetystandardsonschoolbuses,schoolbus-relatedtransportationandenvironmentallawsandinjurypreventionweretheprimaryreasonsforthedesiredoutcome.Fewschoolbusisrelatedtodeathsandinjuriesinthedevelopedcountries.Conclusions:Thedevelopingcountriesshouldmakestrictenvironmentallawsandstandardsonschoolbussafetytopreventchildren'sinjuryanddeath.

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简介：Thebasictheoryandmethodofthecombinationforecastingareintroduced.Basedontheactualdatainanairline,thecasestudywaspresented.Inthecasestudy,twobasicforecastingmodelsaresetup,whicharethetime-regressionplusseasonalfactormodelandthelogarithmadditiveWintersmodel.Andtwocombinationmodelsareestablishedwiththebasicmodels,whicharetheoptimalcombinationmodelandtheregressivecombinationmodel.Theresultsofthestudyareguidabletothepractice.

简介：Networktrafficclassificationaimsatidentifyingtheapplicationtypesofnetworkpackets.ItisimportantforInternetserviceproviders(ISPs)tomanagebandwidthresourcesandensurethequalityofservicefordifferentnetworkapplications.However,mostclassificationtechniquesusingmachinelearningonlyfocusonhighflowaccuracyandignorebyteaccuracy.TheclassifierwouldobtainlowclassificationperformanceforelephantflowsastheimbalancebetweenelephantflowsandmiceflowsonInternet.Theelephantflows,however,consumemuchmorebandwidththanmiceflows.Whentheclassifierisdeployedfortrafficpolicing,thenetworkmanagementsystemcannotpenalizeelephantflowsandavoidnetworkcongestioneffectively.Thisarticleexploresthefactorsrelatedtolowbyteaccuracy,andsecondly,itpresentsanewtrafficclassificationmethodtoimprovebyteaccuracyattheaidofdatacleaning.Experimentsarecarriedoutonthreegroupsofreal-worldtrafficdatasets,andthemethodiscomparedwithexistingworkontheperformanceofimprovingbyteaccuracy.Experimentshowsthatbyteaccuracyincreasedbyabout22.31%onaverage.Themethodoutperformstheexistingoneinmostcases.

简介：Byusingthemodernlydevelopedtechniquesofpossibilityandset-valuedstatistics,thispaperdeter-minedthethresholdsofvariouscategoriesofnoiseannoyanceanddefinedthedose-responserelationbe-tweenthelevelsofnoiseandtheirannoyance.Threefactorscontrolledintheexperimentweretypesofnoise:impulseandtraffic;levelofnoise:5Leqfrom45—85dB（A）;andperformancetask:withandwithoutspeechrecognition.NoiseinterferencewithspeechrecognitionwasmeasuredwiththeSDTmethod.Ourex-perimentalresultsshowedthatLeqofimpulsenoiseshouldbehigherthanthatoftrafficnoisetogetequalannoyance;thespeechrecognitiontaskmightlightentheexperienceofannoyance;thehigherthelevelofnoise,themoreitsinterferencewiththespeechrecognition,thatis,thelessthevalueofd′.

简介：Thisletterreportstrafficflowsensitivitytovisco-elasticity,withthetrafficflowmodelingbrieflydescribedatfirstandthenusedtodotrafficflowsimulationswhoseresultscanreflectthepropertiesofspatial–temporalevolutionofringtrafficflow.Itrevealsthatvisco-elasticityplayscrucialroleinformationoftrafficflowpatterns,implyingthatself-organizationoftrafficflowiscrucialindeterminingtrafficflowstatus.