Traffic stream data has characteristics of multi-source, high speed and large volume, etc. When dealing with these data, the traditional methods and systems of data storage have exposed the problems of weak scalability and low real-time storage. To address these problems, this work designed and implemented a HBase-based real-time storage system for traffic streaming data. The system adopted the distributed storage architecture, standardized data through front-end preprocessing, divided different kinds of streaming data into different queues by using multi-source cache structure, and combined the consistent Hash algorithm, multi-thread and row-key optimization strategy to write data into HBase cluster in parallel. The experimental results demonstrate that, compared with the real-time storage system based on Oracle, the storage performance of the system has 3-5 times increment. When compared with the original HBase, it has 2-3 times increment of storage performance and it also has good scalability.