基于RBF神经网络的不利天气道路通行能力计算(4)

RBF神经网络的实现过程 RBF人工神经网络 RBF人工神经网络结构

参考文献

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查总报告.上海:上海市城市规划管理局,2005

SimulationandEvaluationofBRTApplicationinMetropolis

LIUBingen　JUANZhicai　JIAHongfei

(JilinUniversity,Changchun130025,China)1

(ShanghaiJiaotongUniversity,Shanghai200052,China)2

Abstract:BRTisanimportantmeasuretosolvethetandplementsustainable

.Basedontheoriesofsystemdynamicsonbetweenlanduseanddevelopmentinbigcities

trafficsystemwasestablished.ThemodelwasactualtravelflowwithitsfittingdataofShanghaiCity.BoththeresultoftheofsofShanghaiCityinthefutureunderrecenttendency,withoutanywaysbeingltwithBRTbeingcarriedout,wereforecastedbyusingthe.TheTonassiestablishedmodelmulatedandevaluatedbycomparingthevariationsoftheservicelevelofroadtattractionratiosofBRTtotravelflow.

1

2

1

Keywordssimulationandevaluation;landuse;trafficsystem;systemdynamics

(上接第23页)

RoadCapacityCalculationunderAdverseWeather

BasedonRBFNeuralNetwork

112

ZHANGXiqiao　SHENGHongfei　YAOYanxue

(HarbinInstituteofTechnology,Harbin150090,China)1

(HarbinNormalUniversity,Harbin150500,China)2

Abstract:Variousinfluencingfactorsofurbanroadcapacityunderadverseweatherarestochasticandnonlinear.

Therefore,itisunsuitabletoadoptnormalamendmentmethodtocalculatetheroadcapacity.BasedontheRBFNeuralNetwork,whichcananalyzethestochasticandnonlinearcharacteristics,thecomponentsofroadnetworkwerere2dividedandtheinfluencingfactorswerere2selected.Then,theRBFNeuralNetworkmodelwasbuiltforcalculationofroadcapacity.AnexamplewasprovidedbasedontheactualsituationofHarbinCityunderrainstorm.Themaximumerrorbetweenthecalculationresultsandtheobservationdatawas-1.16%.Thus,thefeasibilityandvalidityofthemodelwerevalidated.

Keywords:adverseweather;roadcapacity;RBFneuralnetwork

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