JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2025, Vol. 60 ›› Issue (12): 11-20.doi: 10.6040/j.issn.1671-9352.0.2024.243

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Evaluation method of used truck value based on GA-BP neural network model

YAN Shengyu1, LIU Yang1, LIU Jixiang2, CHEN Haifeng3*, ZHENG Yuanwang4, WEN Fuhua1, WANG Hongyu5   

  1. 1. School of Automobile, Changan University, Xian 710018, Shaanxi, China;
    2. The First Company of China Eighth Engineering Bureau Ltd., Jinan 250100, Shandong, China;
    3. China Automotive Technology and Research Center Co., Ltd., Tianjin 300300, China;
    4. Shandong Guangan Connected Vehicle Technology Co., Ltd., Jining 272000, Shandong, China;
    5. SAIC-GM-Wuling Automobile Co., Ltd., Chongqing 401135, China
  • Published:2025-12-10

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Abstract: To accurately evaluate the value of the used trucks, a GA-BP(genetic algorithm-backpropagation)neural network evaluation method is proposed, based on the historical transaction data of e-commerce platform. Considering the use features of the used trucks, 11 key indicators affecting the transaction value are selected and the quantitative standards of the indicators are put forward. The consistency of each index is tested by Pearson correlation coefficient method to verify the rationality of index selection. By compromising Genetic Algorithm and BP neural network, the evaluation model of the used trucks value by GA-BP neural network is proposed, and the model is trained and verified by using 9 016 transaction data. Compared with the unoptimized BP neural network model, the mean square error(MSE)of the model is reduced by 74.85%. Through the analysis of the average impact value, it is found that the cumulative mileage, cumulative driving time, emission standard, specific power and loading situation show the greatest influence on the value evaluation of used trucks, and the emission standard has gradually become an important indicator affecting used trucks.

Key words: automobile value evaluation, used truck, genetic algorithm, BP neural network, emission standard

CLC Number: 

  • TP392
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