Traditionally, urban traveling patterns have been obtained through origin-destination surveys. This method presents drawbacks such as high costs, reduced representativeness of the surveyed population, and low spatial and temporal resolution of the results obtained. This study proposes using historical data collected massively and passively from GPS-enabled cell phones to describe population mobility patterns with high spatial and temporal resolution levels. The solutions formulated to tackle the challenges posed by this method are described, as well as the algorithms used to obtain dynamic origin-destination matrices, quantify the average number of daily trips and kilometers traveled per inhabitant, population density per hour, the tracks of roads more frequently used, and to identify the destinations attractor of most trips. As an illustrative example, the traveling patterns were derived for a megacity in Latin America (the metropolitan area of Monterrey, Mexico) with a database of 0.7 million users monitored with high temporal resolution (<1 min between pings) for three months in 2019, which resulted in more than $10^{10}$ data points.