Development of predictive model on traffic violations through data analytics enhancing road safety policies and transportation efficiency in Tacloban City - Elgera, Lean Jane Letrodo; Pestañas, Hilary Aniban; Ramos, Csharleen Angela Cruz; Vidal, Vanessa Ending
The growing urbanization and motorization in Tacloban City have contributed to a significant increase in traffic violations, posing persistent challenges to road safety, public order, and transportation efficiency. This study seeks to address these issues by developing a predictive model for traffic violations through data analytics, with the broader goal of supporting data-driven policymaking and enhancing local transportation systems. The study employed a number of datasets like historical traffic violation records, weather, fuel prices, and regulatory off-days data collected from government agencies. These datasets were subjected to a mix of statistical methods and machine learning algorithms like logistic regression, decision trees, and J48 classifiers. The analysis identified patterns of peak violation periods, thus enabling the development of targeted interventions and resource optimization strategies. One of the major outcomes of this research is the creation of a predictive model built into an operating application that functions as a useful decision-support system, providing insights for predicting traffic violation patterns. More so, it also promotes planning operations by facilitating the advance deployment of staff, maximizing the timing of action, and guiding the adjustment of infrastructure. The research underscored the utility of predictive analysis in urban transportation planning, offering empirical evidence in violation patterns. In general, the research shows the potential of evidence-based policymaking, enhanced transport efficiency, and improved road safety through data-driven methods in emerging cities such as Tacloban City.