Research on the Route Pricing Optimization Model of the Car-Free Carrier Platform Based on the BP Neural Network Algorithm

• Published in: Complexity (New York, N.Y.) Vol. 2021; no. 1
• Main Author: Du, Yu-Ang
• Format: Journal Article
• Language: English
• Published: Hoboken Hindawi 2021; Hindawi Limited; Hindawi-Wiley
• Subjects: Algorithms; Back propagation networks; Cluster analysis; Clustering; Cost recovery; Efficiency; Game theory; Goodness of fit; Logistics; Machine learning; Neural networks; Optimization algorithms; Optimization models; Predictions; Pricing; Regression analysis; Transportation industry; Transportation services; Values; Vector quantization; China
• ISSN: 1076-2787; 1099-0526
• DOI: 10.1155/2021/8204214

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.