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
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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