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Research on the Prediction Model of the Used Car Price in View of the PSO-GRA-BP Neural Network

Author

Listed:
  • Enci Liu

    (School of Economics and Management, Shandong University of Science and Technology, Qingdao 266590, China)

  • Jie Li

    (School of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Anni Zheng

    (School of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Haoran Liu

    (School of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Tao Jiang

    (School of Economics and Management, Shandong University of Science and Technology, Qingdao 266590, China)

Abstract

As the mobile Internet improves by leaps and bounds, the model of traditional offline used car trading has gradually lost the ability to live up to the needs of consumers, and online used car trading platforms have emerged as the times require. Second-hand car price assessment is the premise of second-hand car trading, and a reasonable price can reflect the objective, fair, and true nature of the second-hand car market. In order to standardize the evaluation standards of used car prices and improve the accuracy of used car price forecasts, the linear correlation between vehicle parameters, vehicle conditions, and transaction factors and used car price was comprehensively investigated, grey relational analysis was applied to filter the feature variables of factors affecting used car price, the traditional BP neural network was also optimized by combining the particle swarm optimization algorithm, and a used car price prediction method based on PSO-GRA-BPNN was proposed. The results show that only the correlation coefficient of new car price, engine power, and used car price is greater than 0.6, which has a certain linear correlation. The correlation between new car price, displacement, mileage, gearbox type, fuel consumption, and registration time on used car prices is greater than 0.7, and the impact of other indicators on used car prices is negligible. Compared with the traditional BPNN model and the multiple linear regression, random forest, and support vector machine regression models proposed by other researchers, the MAPE of the PSO-GRA-BPNN model proposed in this paper is 3.936%, which is 30.041% smaller than the error of the other three models. The MAE of the PSO-GRA-BPNN model is 0.475, which is a maximum reduction of 0.622 compared to the other three models. R can reach up to 0.998, and R 2 can reach 0.984. Although the longest training time is 94.153 s, the overall prediction effect is significantly better than other used car price prediction models, providing a new idea and method for used car evaluation.

Suggested Citation

  • Enci Liu & Jie Li & Anni Zheng & Haoran Liu & Tao Jiang, 2022. "Research on the Prediction Model of the Used Car Price in View of the PSO-GRA-BP Neural Network," Sustainability, MDPI, vol. 14(15), pages 1-19, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:8993-:d:868992
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    1. repec:zib:zbmbmj:v:3:y:2023:i:1:p:01-05 is not listed on IDEAS
    2. A’aeshah Alhakamy & Areej Alhowaity & Anwar Abdullah Alatawi & Hadeel Alsaadi, 2023. "Are Used Cars More Sustainable? Price Prediction Based on Linear Regression," Sustainability, MDPI, vol. 15(2), pages 1-17, January.
    3. Min Zhao & Yu Fang & Debao Dai, 2023. "Forecast of the Evolution Trend of Total Vehicle Sales and Power Structure of China under Different Scenarios," Sustainability, MDPI, vol. 15(5), pages 1-22, February.

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