无车承运人平台承运线路的定价问题

Pricing Issues of Carriage Routes on Car-Free Carrier Platforms

摘要: 随着我国无车承运行业的逐步兴起，国内公路运输市场存在着小、散、乱的特点。承运线路的科学定价问题成了众多无车承运人平台亟待解决的问题。在讨论影响无车承运人平台货运线路定价的主要因素的时候，本文通过2020年第十届MathorCup高校数学建模挑战赛A题目附件1、附件2给出的历史的数据，采用相关性分析的方法对影响无车承运人平台进行货运线路定价的主要因素进行了讨论。为提高结果的准确性，本文将按照历史数据的特点，分区间、分类型讨论。得到了影响无车承运人平台进行货运线路定价的主要因素有线路总成本、总里程、车辆长度、业务类型和调价类型。其中调价类型和业务类型也是影响无车承运人平台进行货运线路价格变化(上调、下降、无变化)的主要因素。在定价评价模型中，使用模糊综合评价法对附件1的历史定价评论，分别以平台角度和货车司机角度进行满意度评价。在线路调价模型中，为降低变量之间的相关性，得到更准确的预测结果。在线路总成本和第一次报价(线路指导价(不含税))的预测时将数据按类型和区间分别分成12组和9组，以提高曲线拟合度。在对线路价格调价时分析附件1的数据建立调价模型，设置价格变化的判别系数a和调价系数t1和t2对线路价格进行后续报价。

Abstract: With the gradual rise of my country’s Car-free transportation industry, the domestic road transportation market has the characteristics of small, scattered and chaotic. The scientific pricing of carrier routes has become an urgent problem for many car-free carrier platforms. When discussing the main factors that affect the pricing of the freight route of the car-free carrier platform, this article adopts the historical data given in Annex 1 and Annex 2 of the 10th MathorCup College Mathematical Modeling Challenge in 2020, using correlation analysis methods. The main factors affecting the pricing of freight routes on the platform of car-free carriers are discussed. In order to improve the accuracy of the results, this article will discuss the divisions and types according to the characteristics of historical data. The main factors affecting the pricing of freight routes on the car-free carrier platform are the total cost of the route, the total mileage, the length of the vehicle, the type of business and the type of price adjustment. Among them, the type of price adjustment and the type of business are also the main factors that affect the price changes (up, down, and no change) of freight lines performed by the car-free carrier platform. In the pricing evaluation model, the fuzzy comprehensive evaluation method is used to evaluate the historical pricing reviews in Annex 1 from the perspective of the platform and the truck driver. In the route price adjustment model, in order to reduce the correlation between variables, more accurate prediction results can be obtained. When predicting the total cost of the route and the first quotation (guided route price (excluding tax)), the data is divided into 12 groups and 9 groups according to type and interval to improve the curve fit. When adjusting the price of the line, the paper analyzes the data in Annex 1 to establish a price adjustment model, sets the discriminant coefficient a of price changes and the price adjustment coefficients t1 and t2 to make follow-up offer.