A pattern accumulated compression method for trajectories constrained by urban road networks

In an edge computing framework, a moving object in an urban road network can collect both its own and others’ trajectories to answer spatial–temporal queries. How to store the ever increasing amount of trajectories with limited storage space is a great challenge. We observe that the traces of moving objects in urban road networks usually exhibit great repetition in terms of spatial and velocity distributions. So we propose two strategies, Pattern Accumulated Compression based on Average Velocity (PAC-AV) and Pattern Accumulated Compression based on Representative Velocity (PAC-RV), to compress trajectories online on the mobile devices. The former mainly exploits the path pattern to compress trajectories while the latter exploits both the path pattern and the velocity pattern to compress trajectories. Both PAC-AV and PAC-RV compress the trajectories by two stages. First, we extract the spatial as well as velocity components according to a velocity deviation threshold. Second, we compress the components into a series of identifiers leveraging a Path Pattern Dictionary (PPD) and a Velocity Pattern Dictionaries (VPD). Our method not only greatly cuts down the storage space but also directly supports the velocity-related queries on the compressed trajectories. Experimental results demonstrate the effectiveness and efficiency of our method.