Abstract
The car-free carrier platform is a product of the rapid development of the modern logistics industry and has a vital strategic value for promoting the construction of a country’s comprehensive transportation. However, due to the unreasonable platform pricing model, the industry is currently in a bottleneck period. In order to solve this problem, we established a gray correlation model to calculate the degree of correlation between each characteristic index and platform pricing based on the massive historical transaction data of a certain platform and performed K-means clustering on the results to discover the main factors affecting platform pricing. Based on the abovementioned results, we created a pricing optimization model based on the BP neural network, with the structure of 8-13-1 to predict the freight pricing of the order and test the prediction results. The test shows that the goodness of fit (R2) of the predicted value is close to 1, and the prediction error range is less than 3.7%, which proves the accuracy and effectiveness of the BP neural network model and provides an effective reference for the optimization of the pricing model of the car-free carrier platform.
Highlights
Road freight is a key part of comprehensive transportation construction and the main carrier for the rapid development of the modern logistics industry
On the basis of the current mainstream pricing model, we propose to establish a transportation pricing optimization algorithm based on machine learning
According to the calculated gray correlation degree between each characteristic indicator and platform pricing and the Kmeans clustering result, from Table 2and Figure 1, we can see that the total mileage of the route (x1), planned time (x5), vehicle tonnage (x2), vehicle length (x3), and urgency of demand(x19)all have gray correlation coefficients higher than 0.75 with platform pricing and are grouped into one category, indicating that these five indicators are significantly related to platform pricing, which affects the platform line pricing major factor, but the gray relational algorithm cannot deeply analyze the inherent nonlinear relationship between variables. erefore, based on the above conclusions, with the help of machine learning algorithms, we have deeply explored the internal connection between various indicators and platform transportation pricing
Summary
Road freight is a key part of comprehensive transportation construction and the main carrier for the rapid development of the modern logistics industry. Erefore, how to scientifically price the freight tasks of the car-free carrier platforms has strong research value and practical significance [1,2,3,4,5]. Erefore, we take China as an example to study the Complexity status quo of the development of car-free carrier platforms and for in-depth analysis of the main factors that affect the pricing mechanism of the platform. In order to ensure the rigor of the research, we need to make the following assumptions to eliminate the interference of other factors on the research results: (i) at the current stage, the car-free carrier platform aims to accelerate transactions and lower carrier costs.
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