This study is devoted to understanding traffic cruising causation through exploring and enhancing parking data. Five recent (2017-2020) studies modeling parking congestion relied on occupancy as their only parking lot feature, then compared modeling techniques using this feature, to find the best performance. However, recently some computer scientists pointed out that it is more effective for the computer science community to focus more on data preparation for performance improvements, rather than exclusively comparing modeling techniques. This inspired us to add more parking lot features and evaluate them, to investigate how they should be composed into a congestion score, acting as a more accurate picture of reality. The score is then compared to the performance of a version where occupancy is the only parking lot feature. An experimental case study is designed in three parts. The first measures how the features should be summed into a score according to drivers' expectations. The second analyzes how much data can be reused from the real data, and whether spatial or temporal comparisons are better for data synthesis of parking data. The third part compares the performance of the score against the occupancy-only version using k-means clustering algorithm and dynamic time warping distance. The experimental results show performance improvements in all spatial and temporal categories, and increasing improvement as the sample sizes grow. © 2021 IEEE.