Traffic congestion is one of the biggest problems for transportation, since it carries high costs for cities and health risks due to its impact on air pollution. Therefore, several strategies have been proposed to improve mobility considering pollution reduction as a critical factor. To accomplish that, methods like forecasting and traffic light control systems are used for traffic management and for them to be more efficient, prior analysis of traffic data is needed. This paper is focused on that analysis for a Low Emission Zone located in Medellin, Colombia. The most relevant characteristics related to the temporal component and variables of the dataset are visualized and analyzed through statistical methods such as correlation and visualization approaches such as PCA and bar plots, also used for feature selection.