# Multi-Source Data Fusion for Vehicle Maintenance Project Prediction
## 3 Method
### Ablation Study  
In order to conduct a thorough analysis of each module's effectiveness in our proposed approach, we compared ten ablation variants of the model, each with distinct settings. These variants are as follows:  
* MsDFN-B\({}_{1}\): This model aims to underscore the importance of location in predicting vehicle maintenance projects by removing the input of location.
* MsDFN-B\({}_{2}\): The elimination of the manufacturer input in this model enables us to assess the significance of the manufacturer in predicting vehicle maintenance projects.
* MsDFN-B\({}_{3}\): By excluding the input of vehicle type, this model allows us to evaluate the contribution of vehicle type to the prediction of maintenance projects.
* MsDFN-B\({}_{4}\): The elimination of engine type as an input in this model enables us to ascertain the impact of engine type on predicting maintenance projects.
* MsDFN-B\({}_{5}\): This model investigates the significance of engine displacement in predicting vehicle maintenance projects by removing the input of engine displacement.
* MsDFN-B\({}_{6}\): The exclusion of the model year input in this model allows us to evaluate the importance of the vehicle model year in predicting maintenance projects.
* MsDFN-B\({}_{7}\): By eliminating the input of the year of vehicle production, this model enables us to analyze the impact of production year on predicting maintenance projects.
* MsDFN-B\({}_{8}\): This model investigates the contribution of vehicle assembly plants to the prediction of maintenance projects by removing the input of vehicle assembly plants.
* MsDFN-B: The exclusion of all base information in this model allows for the assessment of its significance in predicting vehicle maintenance projects.
* MsDFN-M: The removal of the mileage fusion process in this model enables the evaluation of the importance of mileage in predicting vehicle maintenance projects.
* MsDFN-Co: This model assesses the significance of project correlation representation in predicting vehicle maintenance projects by eliminating the process of project correlation representation.  
Figure 4: Accuracy of predictions of MsDFN and baseline with varying training data.  
The results of the experiments for each variant model are presented in Table 5, indicating that the performance of each variant model of MsDFN is inferior to that of the original MsDFN, thereby validating the efficacy of each component within the MsDFN model.  
The variant models MsDFN-B\({}_{1}\), MsDFN-B\({}_{2}\),..., MsDFN-B\({}_{8}\) evince that the inclusion of vehicle base information enhances the accuracy of vehicle maintenance project predictions, underscoring the necessity of integrating multiple data sources into the model.  
MsDFN-B demonstrates the impact of the lack of base information on the experimental results at a holistic level, again showing that the development of vehicle maintenance projects is influenced by vehicle base information.  
Remarkably, the MsDFN-M variant reveals the substantive impact of vehicle maintenance mileage on the formulation of maintenance projects, affirming a robust association between the advancement of vehicle maintenance projects and vehicle mileage.  
Additionally, the MsDFN-Co variant demonstrates that the adequate representation of maintenance project correlations significantly enhances the precision of vehicle maintenance project predictions, highlighting the interplay between different projects in the development of maintenance projects and the imperative of incorporating project correlation representation within the model.