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

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 R2 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.

vGRF analysis based on GRA-BP model to predict volleyball smash landing

“…As can be seen from Table 2, the accuracy of the GRA-BP model proposed in this paper has a high similarity with the accuracy of the PCA-WPNN model [4] and the PCA-BP model [27], and the model in this paper is more accurate and precise compared to the Wouda model [26], and there is a certain gap between the PSO-GRA-BPNN model [24] and the GRA-BP model proposed in this paper in terms of accuracy.…”

Are Used Cars More Sustainable? Price Prediction Based on Linear Regression

“…The longer the vehicle is used, the more likely it is that wear and tear will lower the value of the car. Because used car buyers are focused on fuel economy, small-displacement cars are more likely to retain their value [58,59]. Thus, using artificial intelligence for price predication in the form of linear regression can help us understand the impact of sustainability based on acquiring used cars.…”

The Framework of Car Price Prediction and Damage Detection Technique

“…Desktop Applications and Mobile Applications display the records related to the parameter captured by the corresponding screenshots [20]. The correlation coefficient of the new car price, engine power, and used car price is greater than 0.6, which has a certain linear correlation [21]. III.…”

“…In this scenario [20], significant progress has been made in learning how and where to address classification tasks when a small dataset is inadequate to train a CNN model. This issue can be addressed using data mining algorithms, which entails proactively gathering and labelling information on the web [21].…”