Xian Meng, Lijun Wang, and Jianshuang Liu
Trajectory data, spatiotemporal aggregation, logistics vehicles, transport movements, visualisation
The analysis of common data features based on two-dimensional location information neglects to take into account the temporal dimension, which leads to poor analytical results in the process of recognising geographic information. It is crucial to improve the motion analysis of moving objects to explore their spatiotemporal motion patterns. The study proposes a method for spatiotemporal aggregation (SA) based on trajectory data (TD) to analyse logistics vehicle transportation movement. Specifically, the stacked spatiotemporal density method is recommended for spatiotemporal analysis of moving objects, along with the design of the voxel layer and density decay function (DF). For a better spatial study of moving objects, the four-dimensional temporal density metric is also added to the TD analysis. The results demonstrated that the method effectively realises the visual analysis of moving objects due to its superior SA of trajectories without crossover and significantly shorter calculation time sum (7 s, average time 4.42 s) than the conventional density calculation method (>900 s, average time 1,263.47 s). The approach is useful for describing how objects travel, and its emphasis on temporal and spatial aspects can successfully serve as a baseline for analyses of air transport paths.
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