Traffic Violation Modeling Using K-Means Clustering Method: A Case Study in Bandung, Indonesia

Violations of traffic regulations are both an issue and a problem that persists as a feature of life, especially in metropolitan regions such as Bandung. Traffic violation has both behavioral and environmental patterns, with different types of violations occurring at different times during the day. This negligence stems largely from not properly equipping the vehicle with the necessary documents, especially for drivers who do not pay attention to proper document preparation. With the goal of increasing road safety, law enforcement bodies face the ongoing challenge of managing rising traffic violation rates which results in a growing backlog of violation cases and a corresponding backlog workload for police departments. Comprehensive preventive strategies for the problem are extremely difficult to implement in the absence of streamlined mechanisms for the efficient allocation of limited police resources. Currently, agencies responsible for managing violation records are still using a manual desktop system based on Microsoft Excel spreadsheets. This method impedes the analysis of large datasets to derive actionable insights that could inform targeted, data-driven strategies needed to guide proactive measures. In this regard, this study attempts to implement the K-Means clustering technique in order to identify and classify high-incidence traffic violation areas in Bandung. Using this technique, the research classifies the city into three violation risk clusters: very prone, prone, and moderately prone areas. The map of the classes demonstrates the distribution of these clusters spatially, illustrating clearly and vividly how stakeholders can visualise the pattern of traffic violations. This method improves the understanding of data and at the same time boosts purposeful planning for the safety and public traffic order anticipations.