The paper reviews different methodologies and technologies used in urban traffic management and analysis systems based on Big Data. Several techniques are discussed, such as the installation of sensors, IoT devices and surveillance cameras for data collection, the use of machine learning algorithms and technologies for vehicle identification and classification, the use of Map Reduce techniques for data processing, and the communication between several social network applications to obtain real-time data about the location and movement of users. In addition, models based on neural networks and algorithms such as ABC are proposed for signal change prediction and traffic flow management. The advantages and disadvantages of each approach and how they can be applied to improve urban traffic management and reduce vehicle pollution are highlighted.