Nowadays, ordinary route planners compute paths by choosing the shortest or fastest route. However, there exist additional metrics from which users with varying preferences could benefit. Personalized route planning offers the possibility to combine different metrics with personal preferences. Nevertheless, personalized route planning has mainly been tested with correlated metrics. But when including uncorrelated metrics, the computing time increases significantly. Previous work found that the speedup technique “Customizable Route Planning” can lead to feasible speedups for single metric calculations. Thus, in this work, we investigate how this speedup technique for Dijkstra improves the query performances of “Personalizable Route Planning” compared to “Personalizable Contraction Hierarchies”. Furthermore, we study the performances on uncorrelated metrics. We introduce a graph structure to compare the personalized speedup techniques “Personalizable Contraction Hierarchies”, “Personalizable Customizable Route Planning” and “Personalizable Route Planning”. Three graph partitioning algorithms have been implemented to realize “Customizable Route Planning”: K-means, Gonzales, and Merge. Our experiments show that Merge works well in combination with “Personalizable Contraction Hierarchies” preprocessing. We found that “Personalizable Customizable Route Planning” is a good alternative, as it uses much fewer edges for finding the costs of the shortest path. For uncorrelated metrics, “Personalizable Customizable Route Planning” and “Personalizable Route Planning” achieved speedups higher than “Personalizable Contraction Hierarchies”. Our contribution comprises a novel graph structure for comparing different Dijkstra variants. With our experiments, we provide a deeper understanding of the personalized route planning problem. Additionally, we propose improvements for “Personalizable Contraction Hierarchies” for less contracted graphs with uncorrelated metrics.