Traffic in urban environments vary significantly depending on the areas of the city and the time of day. Nowadays, thanks to the large number of GPS devices that are integrated in all kinds of devices, it is possible to make a quantitative analysis of traffic. This contribution presents an application developed to analyse this information in a simple way and with a visual representation. One of its main advantages is that it is adaptable to any city in the world, as its internal algorithms adapt to the available data are presenting and adjusting it to the traffic through the city. This is done by extracting information directly from the data provided by GPS devices moving around the city. In addition, an analysis of the execution times of all application processes is presented to determine which parts involve a higher execution cost and determine the overall scalability of the application.

The density of traffic within urban areas depends on multiple factors, and among those; one that has major impact is the weather. This paper presents a study that empirically analyzes the traffic flow velocity depending on the atmospherically conditions and the day schedule. The objective is to assess systematically to which extent there is a correlation between the vehicle velocity on an urban environment and the weather, depending on the time of day. The presented case of study uses a set of real data, specifically the trips made by taxis in the city of Porto (Portugal). First, the vehicle GPS routes are analyzed to identify departure and arrival points and estimate the route average velocity and adding weather and time conditions. The data is processed with different regressive techniques to obtain the influence of the variables on the velocity. The results show differences between days of the day and weekends, as well as differences in speeds with a favorable climate for driving compared to other more adverse ones.