A predictive modeling framework for forecasting cumulative sales of euro-compliant, battery-electric and autonomous vehicles

Abstract

The automotive industry faces a transformative time with the advent of advanced vehicle technologies and evolving consumer preferences. This research employs predictive modeling to forecast the adoption of Euro 6d and Euro 7 compliant vehicles, Battery Electric Vehicles (BEVs), and Autonomous Vehicles (AVs) in the European Union’s 27 member states and the United Kingdom. This study provides insightful projections and comparative analyses of these technologies’ future trajectories using the modified Gaussian and Logistic models. The modified Gaussian model projected relatively sharp growth curves for all vehicle types, signaling rapid initial adoption followed by saturation. Conversely, the Logistic model depicted more gradual and continuous growth patterns, suggesting sustained market interest over time. Comparative analyses highlight the unique strengths and limitations of each model. The modified Gaussian model proves effective for identifying early market responses and pivotal intervention points, while the Logistic model aids in strategic long-term planning and trend anticipation. However, disparities between the models show the complexity of forecasting automotive market dynamics, emphasizing the need for multifaceted frameworks and approaches. Thus, enhancing predictive accuracy by refining models and integrating additional variables will be pivotal in navigating the dynamic landscape of emerging automotive technologies. This research stands out for its approach to applying analytical methods to predict future market dynamics, offering valuable guidance for policymakers and industry stakeholders in strategizing for the forthcoming technological shift.