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Complex Event Processing (CEP) systems process large streams of data trying to detect events of interest. Traditional CEP systems, such as Esper, lack the required scalability and processing capability to cope with the constantly increasing amount of data that needs to be processed. Furthermore, user defined rules are static so changes in the monitored environment cannot be easily detected. In this paper we investigate the development of a scalable and dynamic traffic management system. Our work makes several contributions: We propose a novel system that combines Esper with a stream processing framework, Storm, in order to parallelize the processing of larger amounts of data. We propose a novel rules’ assignment algorithm for distributing Esper rules to the available CEP engines, in a way that maximizes the overall system’s throughput. Finally, our system adapts to changes of the environment by processing historical data via Hadoop and dynamically updating the Esper rules based on the generated results. Our work has been evaluated using real data, in several traffic monitoring scenarios for the city of Dublin. Our detailed experimental results indicate the benefits in the working of our approach and the significant increase in the system’s throughput when a large number of Esper rules were examined concurrently.