知识与财富的链接
常规交通量预测模型耗时、工作量大.针对美国印第安纳州交通量趋于稳定的特点,借助ArcGIS地统分析软件,分别采用Ordinary Kriging、距离加权倒数(IDW)、径向基函数插值(RBF)等方法对交通量进行预测,并与动态称重系统(WIM)的交通资料进行比对,结果表明采用径向基函数插值中的反高次曲面函数(IMS)插值预测方法适合美国印第安纳州交通量的分布实际,预测误差最小.
Conventional traffic prediction models are time-consuming and arduous.Since the traffic of Indiana is stabilized,the Ordinary Kriging,inverse distance weighting(IDW) and radial basis functions(RBF)interpolation methods were used to predict the traffic of Indiana based on ArcGIS software.With the comparison of the prediction and weigh-in-motion(WIM) Systems,the results show that the inverse multiquadric spline(IMS)interpolation of RBF interpolations is suitable for Indiana traffic distribution and the actual prediction error is minimum.