Focusing on the issue that the HBase storage without spatio-temporal index degrades the traffic data query performance, some HBase spatio-temporal indexes based on row keys were proposed for massive traffic data. Firstly, the dimensionality reduction method based on Geohash was used to convert two-dimensional spatial position data into a one-dimensional code. Then the code was combined with the temporal dimension. Secondly, four index models were put forward based on combination order, and the structures of the models and their adaption conditions for traffic data query were discussed. Finally, the algorithm of index creation as well as traffic data query algorithm was proposed. Experimental results show that the proposed HBase spatio-temporal index structure can effectively enhance the traffic data query performance. In addition, the query performance of four different spatio-temporal index structures in different data size, different query radius and different query time range were compared, which verified the different adaption scenes of different index structures in traffic data query.