## 1 Introduction  
In modern transportation, the integration of sophisticated vehicular technologies is significantly improving road safety and navigation efficiency. Modern vehicles not only feature advanced electronic control units (ECUs) but also make use of extensive networks of sensors and communication protocols. Among these, vehicle-to-vehicle (V2V) [1] and vehicle-to-infrastructure (V2I) [2] communications are critical. V2V communication allows vehicles to transmit data to each other, facilitating real-time traffic and road condition updates, while V2I communication connects vehicles to road infrastructure like traffic signals and roadside sensors, enabling broader data sharing and smarter traffic management. However, leveraging these data streams for predictive analytics introduces substantial challenges, particularly in deploying machine learning models capable of handling the complex dynamics of vehicular data. The primary objective of the research is to expedite the decision-making process for connected vehicles by analyzing data via machine learning. Consequently, this approach aims to decrease the fuel consumption rate of connected vehicles running on fossil fuels and shorten the time needed to recharge electric connected vehicles. In addition, in a prior study conducted by the authors, the focus was on reducing diesel fuel consumption for trucks upon reaching intersections by ensuring smooth traffic flow without halting at intersections [3]. Traditional machine learning models, such as decision trees [4] and K-nearest neighbors [5], have long been the cornerstone of predictive analytics in vehicular data analysis [6]. While these methods have provided valuable insights, they inherently struggle with several critical issues when applied to the complex environments of connected vehicle systems. Two prominent issues are the \"black box\" nature of many machine learning models and the limitations of relying on single-model predictions [7]. A significant challenge with advanced machine learning models, particularly in the realm of deep learning, is their \"black box\" nature. While these models are often highly effective in making predictions, they typically provide little to no insight into the decision-making process. This lack of transparency can be problematic [8], especially in applications like autonomous driving and real-time traffic management, where understanding the rationale behind a decision is as crucial as the decision itself. The inability to interpret model predictions can hinder trust among stakeholders and end-users, posing a barrier to the broader adoption of these technologies in safety-critical systems [9; 10]. Single machine learning models, despite their individual strengths, often exhibit specific weaknesses when faced with the vast and varied data types found in vehicular systems. For example, a decision tree might handle categorical data well and provide easy-to-understand rules but can suffer from overfitting and lack the ability to generalize across different datasets. Similarly, K-nearest neighbors excels at capturing local regularities but can perform poorly with high-dimensional data due to the curse of dimensions. Moreover, single models do not always adequately capture the complex interactions and nonlinear relationships present in vehicular data. This can lead to suboptimal predictions and models that are brittle under varying conditions or scenarios that deviate from those seen during training. This research harnesses the power of ensemble learning [11; 12] methodologies to improve the predictive accuracy of vehicular data analysis concerning driving style, road surface quality, and traffic conditions. By integrating multiple models via a voting classifier ensemble method [13], we address the limitations of individual approaches and enhance overall predictive performance. This approach is particularly pertinent for intelligent transportation systems, where the potential for ensemble learning remains largely underexplored.