# Multi-Source Data Fusion for Vehicle Maintenance Project Prediction
## 1 Introduction  
With the rapid evolution of the vehicle industry, vehicles have become an indispensable and fundamental aspect of modern life. Consequently, the vehicle maintenance industry has emerged as a crucial service sector, directly impacting individuals' well-being and overall quality of life. To ensure a secure and reliable driving experience while minimizing maintenance expenses, accurate vehicle maintenance prediction has garnered significant attention from both researchers and industry professionals in this domain. Unfortunately, current maintenance practices primarily rely on fixed time intervals or mileage, which represent a conventional yet restricted approach to planned maintenance. This approach overlooks the varying usage patterns and driving environments of different vehicles, consequently hampering maintenance flexibility and resulting in excessive costs and unnecessary maintenance procedures. Throughout the vehicle maintenance procedure, multiple maintenance tasks exhibit interconnectedness, indicating strong correlations between them. For instance, to maintain the engine's lubrication and cooling functionalities, it becomes imperative to simultaneously replace both the oil and oil filter. To sustain optimal combustion efficiency and emissions performance, the simultaneous replacement of both the air filter and fuel filter ensures a continuous supply of clean air and fuel to the engine. In order to uphold the ignitionsystem's functionality and brake operation, replacing the spark plugs and ignition coils after reaching a predetermined mileage is crucial. Mileage is a pivotal factor in determining the requirement of vehicle maintenance. The number of common maintenance projects (Here, common maintenance projects are those that need to be performed more than five times a year.) for vehicles with different annual mileage, as depicted in Figure 1, varies based on experimental data. Heightened mileage intensifies the wear and aging process of vehicle components. Frequently used parts such as the engine, suspension, and braking systems endure heightened strain, fatigue, or even damage as mileage escalates. Moreover, long journeys result in increased fuel consumption, compromised lubricant quality, and associated complications, increasing the maintenance risk factor. Therefore, vehicles require more frequent and varied engagement with maintenance as mileage accumulates. The vehicle base information, including location, manufacturer, type, engine type, engine displacement, model year, production year, and assembly plant, is closely related to the development of maintenance projects. Varied regions present dissimilar climates, road conditions, and environments, which in turn impact the demand for specific vehicle maintenance endeavors. Manufacturers vary in terms of vehicle design, performance benchmarks, and the developmental methodologies employed for their respective maintenance products. Additionally, disparate vehicle types involve unique high-stress components during operation, requiring specialized maintenance approaches. Diverse engine classifications and capacities require distinct maintenance procedures due to their inherent design and functional differences. Notably, advancing vehicle technology leads to discrete engineering upgrades and modifications across varying models and production years, consequently influencing corresponding maintenance protocols. Thus, different assembly processes and component selections at various assembly facilities result in discrepancies in maintenance requirements. Drawing upon the aforementioned context, to address the prevalent challenges encountered in predicting vehicle maintenance projects, we propose a methodology founded upon vehicle maintenance record data and vehicle base information data. The former encapsulates maintenance projects and maintenance mileage, while the latter encompasses vehicle's location, manufacturer, type, engine type, engine displacement, model year, production year, and assembly plant.