货运价格转换及基于揭示偏好数据的公铁竞争模型

Freight Price Conversion and Road/Rail Competition Model Based on Revealed Preference Data

为深入推进货物运输“公转铁”,铁路运输企业需掌握竞争方式同口径可比价格,并充分理解货运方式选择行为。本文提出一种不同装载和运输方式之间的运价转换方法,解决了采用RP(Revealed Preference)数据进行离散选择建模时备选项属性数据缺失的问题。通过改进的PPS(Probability Proportionate to Size Sampling)方法,有效组合多源RP数据,构建货运方式选择行为模型。结果表明,模型能正确预测90%以上的观测值。轻货的VOT(Value of Time)相比重货更高。价格弹性的推导和计算表明,提高公路价格比降低铁路价格能使铁路分担率有更大的提升,降低当前铁路价格可以增加运输收入。当铁路价格下降到收入最大化目标的最优定价点时,不仅会带来铁路分担率、运量和收入的显著增加,还有望获得一定的碳减排效益。

To further promote the modal shift from road to rail,rail transport enterprises need to grasp the comparable prices of the same caliber of competitive modes and fully understand the freight mode choice behavior.This paper proposes a method for converting freight prices among various loading and transport modes,and solves the missing data problem on alternative attributes when adopting RP(revealed preference)data for discrete choice modeling.The freight mode choice behavior model was developed by effectively combining multi-source RP data through an improved PPS(probability proportionate to size sampling)method.The results show that the model can successfully predict more than 90%of the observations.Light cargo's VOT(value of time)is higher than its heavy cargo counterparts.The derivation and calculation of price elasticities show that increasing road prices can lead to a more significant increase in rail market share than reducing rail prices.Reducing current rail prices can increase rail transport income.When rail prices fall to the optimal pricing of the income maximization goal,it will not only bring a significant increase in rail share,volume and income,but is also expected to yield some carbon reduction benefits.