Combining Clustering and Factor Analysis as Complementary Techniques

The study of driver behavior and associated accidents has been of interest to researchers and insurance companies. From the perspective of insurance companies, identifying factors that contribute to traffic violations plays a significant role in providing insurance quotes as it establishes the basis for charging appropriate insurance rates to customers. This study assesses the traffic violations intensity for 64 counties in the state of Florida, USA by using the publicly available traffic violations data set. This data set consists of 3,669,796 records with 11 attributes, which include race, gender, driver's age, type of driving violation, etc. The 187 types of traffic violations are categorized into 11 broad traffic violations categories. Two machine learning algorithms, factor analysis and k-means clustering, were applied in this study. After applying factor analysis, a new comprehensive traffic violation index (TVI) was developed, which quantified the traffic violation intensity of each county. All the counties in the data set were ranked with the TVI scores, and the counties with high TVI scores were identified. K-means clustering algorithm was then applied to the same data, and four clusters of counties were derived. The counties that were grouped in each cluster were compared with the TVI scores to check if the counties in each cluster had similar TVI scores. The counties with the highest TVI scores are found to be grouped in one cluster, followed by counties with the next high TVI scores in the second cluster, and so on. Thus, it is observed that there is a perfect match in the results of both models. They serve as two techniques complementary to each other, in that the k-means clustering method groups counties with comparable traffic violation intensities and factor analysis is able to also rank individual counties according to the TVI. These techniques have identified the counties with high traffic violation intensities, which helps the policymakers to take adequate measures for traffic management.