Temporal Model for Transportation Management

Abstract

Over the past few years, with improvements in the accuracy of spatiotemporal data acquisition and the diversification of collection methods, the volume of spatiotemporal data has expanded rapidly. As a result, there is an urgent need for spatiotemporal data models that can accurately track the motion trajectories of spatiotemporal objects and meet the needs of different applications. The emergence of a model based on Markov chain spatiotemporal solves this problem to a certain extent. The spatial position of the spatiotemporal object at the next moment T n+1 is only related to the spatial position of the current moment Tn, while it is independent of the spatial position of T 0 , …, T n-1 at the previous time, which exactly aligns with the Markov characteristic. Based on the Markov chain spatiotemporal model, this paper applies the sparse matrix storage idea to the spatiotemporal model according to the motion characteristics of spatiotemporal objects and determines the specific storage format of the spatiotemporal data. Both Huffman coding and arithmetic coding are used to compress spatiotemporal object motion trajectory data. Finally, this method is applied to a study case and the advantages and disadvantages of two compression algorithms applied to spatiotemporal object trajectory storage are compared, thereby demonstrating the feasibility of the compression storage method.