Parallel clustering of big data of spatio-temporal trajectory

Generally, computing efficiency of many spatial data analysis algorithm will sharply decline as data size increase. It is very meaningful for extending the analysis method of spatial data and enhancing computational efficiency by introducing the distributed parallel computing model. Considering the features of spatio-temporal trajectory data, which is massive, related to time and dynamic, we proposed the fast calculation method of the trajectory similarity based on coarse-grained Dynamic Time Warping. The algorithm will reduce the consuming time greatly when the length of trajectory sequences are very long. We also proposed the parallel trajectory clustering strategy of big data under the Hadoop MapReduce model in this paper. The big data of trajectory are sliced, and the trajectory similarity and the iteration computation of cluster center are dealt with by multiwork nodes simultaneously. The experimental results of the parallel trajectory clustering, which based on the open source project Mahout, implemented on the vehicle trajectory data show that the clustering results are valid. The computing performance of parallel clustering are obviously improved as the trajectory data size increases. And the new parallel clustering method outperforms the traditional algorithm like k-means algorithm.