The development of applications for advanced Intelligent Transportation Systems (ITS), is a growing research area, pushed especially by the need to mitigate problems related to transportation inefficiency, overuse and pollution, mainly caused by the increasing of traffic congestions in big cities. Nowadays, traffic related data coming from the road infrastructure, user's cars or mobile phones, is generated in unprecedent volume and speed. Therefore, ITS face new challenges, with respect to growing demand to process large volumes of traffic data in real-time, and extract value that can be immediately used for decision making. The work presented here, proposes a scalable architecture supported by Big Data technologies, capable of processing real-time traffic data captured from 349 inductive loop counters, placed in Slovenian road network. The main goal is to propose an approach which can be adopted by national road operators, capable of monitoring, in real-time, the current status of the road network. Traffic events need to be detected in real-time and require the collection of data from several fixed sensors. To deal with data volume and velocity challenges, a data stream management system was used, able to collect traffic data that arrives and process it on a continuous (24×7) basis. Results achieved, suggest that there's a great advantage in the adoption of a data stream management system, with respect to traditional database management systems, illustrated by practical examples, providing quantitative metrics about data processing performance.