Driving School Analytics

Our analysis of the latest CBR data reveals a wide variation in driving exam success rates across different exam centers in the Netherlands. The lowest pass rate was recorded at Examen­centrum Barendrecht, where only 39% of candidates passed their driving exam. In contrast, Examen­centrum Maastricht showed the highest success rate at over 64%, making it the most favorable location for test-takers in 2025. A clear pattern emerges in the distribution: urban centers such as Rotterdam, Amsterdam, and Utrecht cluster in the lower range of success rates (typically between 39% and 48%), whereas more peripheral or rural locations tend to exhibit higher pass rates, often exceeding 55%. This distribution likely reflects differences in driving environments, including road complexity, congestion, and examiner expectations. Understanding these regional disparities may help learners, especially international students, choose exam centers where they have statistically higher chances of passing.

This analysis explores the relationship between first and second attempt driving test success rates across Dutch driving schools. The scatter plot shows a moderate positive correlation between the two, captured by the regression line: y = 0.45x + 31.75. This suggests that driving schools with high first attempt pass rates generally tend to also perform better on second attempts, though the relationship is far from perfect. While many schools cluster between 40% and 70% on both metrics, there's significant spread, especially among schools with lower first attempt success rates. The positive slope implies that improved first-time preparation could also lead to better outcomes on retakes. To maintain statistical reliability, the analysis excluded schools with fewer than 5 exams or with 0% pass rates on either attempt. For international students or learners evaluating where to enroll, these results highlight the importance of selecting schools with consistently strong outcomes, not just on the first try, but in follow-up attempts as well.

This analysis investigates whether there is a link between a driving school's pass rate and the number of exams it conducts. The scatter plot reveals a wide dispersion in success percentages among schools with fewer exams, indicating significant variability in performance at smaller schools. Conversely, larger schools—those conducting over 1,000 exams, tend to cluster around the national average, suggesting more consistent outcomes. These metrics show that while the average performance across schools is fairly balanced, many smaller schools either significantly overperform or underperform. This may reflect differences in student demographics, teaching quality, or even exam-taking strategies. For prospective students comparing driving schools, this chart suggests that volume alone is not a strong predictor of quality, but it may indicate more stable and predictable outcomes.

This report presents the results of a K-means clustering analysis of Dutch driving schools based on quantitative characteristics such as exam success rates, total exam volume, lesson types, and retake outcomes. The dataset was filtered to exclude schools with fewer than 10 exams. Latitude and longitude were removed due to low variance, ensuring that clustering was driven by performance-related variables. The elbow method was used to determine the optimal number of clusters.

Four distinct clusters were identified and interpreted as follows:

Cluster 0 – Low Success, Modest Size This cluster contains schools with the lowest average success rate (40.2%) and first-attempt pass rate (35.3%). They offer very few lesson types, including no theory or automatic transmission training. These may represent low-cost or low-preparation schools focused on getting students to the exam quickly.

Cluster 1 – High Success, Low Volume Schools in this cluster achieve the highest pass rates (65.6% success, 63.8% first-attempt). However, they handle the fewest exams on average (49.7). These schools likely focus on quality instruction and smaller student bases, offering well-rounded training programs including theory and practical preparation.

Cluster 2 – Automated High-Volume Providers This cluster is characterized by very high exam volume (205.1) and dominant use of automatic transmission vehicles (100%). They offer consistent lesson availability and maintain decent success rates (55.4% first-attempt). These are likely large-scale urban schools or franchises specializing in automatic licensing.

Cluster 3 – Balanced All-Rounders These schools combine solid exam performance (56.5% success rate) with a full suite of lesson offerings and moderate exam volume (175.1). With both manual and automatic transmission lessons and high practical training availability, they likely represent full-service, experienced driving schools.

The principal component analysis (PCA) confirmed that success rates, exam volume, and lesson type diversity were the most influential dimensions in differentiating the clusters. This segmentation supports both students and policy makers in identifying driving school strategies based on data-driven traits.

This report presents a scatter plot comparing the difficulty of passing the driving exam at each location (measured as 100 minus the passing rate) with the combined skill scores of the top 5 driving schools associated with that location. The analysis shows no clear correlation between the exam location difficulty and the aggregated skill level of the top driving schools. This suggests that even at more challenging exam centers, top driving schools can maintain strong performance, and location difficulty is not necessarily a reflection of school quality.