<p>Utilizing parked vehicles as fog nodes for task offloading offers an effective way to reduce computing resource bottlenecks in vehicular edge computing (VEC). This approach ensures the quality of service (QoS) for users in the vehicular environment. However, the considerable number of parked vehicles in the area presents various opportunities to meet vehicular application requirements. Consequently, selecting the most appropriate parked vehicle at a given location to serve as a fog node remains a notable research gap. To address this gap, we propose an adaptive scheme based on the neutrosophic AHP-TOPSIS techniques for prioritizing parked vehicles. Initially, the entire road network is partitioned into distinct regions using a Voronoi diagram to mitigate computational complexity and effectively cluster parked vehicles. Subsequently, the Analytic Hierarchy Process (AHP) is used to refine the importance of the criteria. We include parked vehicles’ load, delay, and energy indicators as decision-making criteria for selecting these vehicles as fog nodes. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is also applied to prioritize the feasible parked vehicles. We tested various scenarios with different numbers of tasks and parked vehicles. Our simulations show that, in terms of the performance metrics, our proposed scheme effectively outperforms rival schemes in both tested situations. These metrics include task completion time, the ratio of successful tasks, the offloading ratio, and the energy consumption of parked vehicles.</p>