时间价值分布预测的混合密度模型

Finite Weibull Mixture Distribution Model of VOT Distribution Forecasting

针对使用常规简单分布密度函数难以拟合复杂时间价值数据的问题,提出了基于混合分布的建模方法,采用期望最大化(EM)算法推导出了Weibull混合分布参数估计的迭代算式,并将其用于时间价值数据的拟合。为考察混合分布模型对复杂数据分布的拟合性能,进行了仿真对比研究,并将基于EM算法的Weibull混合分布时间价值模型应用于城市客运交通方式分担率的分析和计算。结果表明:基于EM算法的混合分布模型是最有效的;Weibull混合分布模型能够有效用于复杂时间价值数据统计分布建模和交通方式分担率预测研究。

Aimed at problem that data of complicated value of time （VOT） can not be fitted by simple distribution mixture functions frequently, the finite Weibull mixture distribution model for modelling VOT distribution was proposed. Expectation maximum （EM） algorithm was used to deduce the interative formulae of the model and the formulae were applied to fitting of VOT. In order to explore fitting characteristics of the model on complicated data distribution, simulated comparisons were carried out and the model based on EM algorithm was applied to analysis and calculation of urban passenger traffic mode sharing ratio. Results show that the model based on the EM algorithm is the optimal choice. The model can be effectively applied to VOT distribution modelling and forecasting of traffic mode sharing ratio.