Abstract
Similarity measurement is one of the key tasks in spatial data analysis. It has a great impact on applications i.e., position prediction, mining and analysis of social behavior pattern. Existing methods mainly focus on the exact matching of polylines which result in the trajectories. However, for the applications like travel/drive behavior analysis, even for objects passing by the same route the trajectories are not the same due to the accuracy of positioning and the fact that objects may move on different lanes of the road. Further, in most cases of spatial data mining, locations and sometimes sequences of locations on trajectories are most important, while how objects move from location to location (the exact geometries of trajectories) is of less interest. For the abovementioned situations, the existing approaches cannot work anymore. In this paper, we propose a grid aware approach to convert trajectories into sequences of codes, so that shape details of trajectories are neglected while emphasizing locations where trajectories pass through. Experiments with Shanghai Float Car Data (FCD) show that the proposed method can calculate trajectories with high similarity if these pass through the same locations. In addition, the proposed methods are very efficient since the data volume is considerably reduced when trajectories are converted into grid-codes.
Highlights
With the development of sensor technology for positioning, ubiquitous Global NavigationSatellite Systems (GNSS) enabled mobile devices to generate huge amounts of trajectory data [1,2].These trajectory data are sequences containing various information such as location, time, speed and direction, which enables the rapid development of Location-Based Services (LBSs) and applications [3,4,5,6].Similarity measurements, as one of the crucial tasks of data mining, have been widely used in the location-based applications to find all similar trajectories from a large collection [7,8]
Through similarity measurements of trajectory data, valuable information can be mined from large collections of trajectories, such as traffic flows and hot routes [9,10]
This paper proposes a grid-based approach called Spatial Grid Coding Distance (SGCD)
Summary
With the development of sensor technology for positioning, ubiquitous Global Navigation. Due to the accuracy of GNSS measurements and the fact that cars may drive on different lanes on the same road, the shape and distance between sequences of the same route may be different, leading to inaccuracy in the calculation results Another type of similarity method is calculated based on the road network. In applications of spatial data mining based on trajectory data, this type of method is an improvement over previous measurements and can be used for vehicle navigation and route recommendation This kind of method requires detailed road information which are lacking in the vehicle trajectory data. This paper proposes a grid-based approach called Spatial Grid Coding Distance for measuring similarity of trajectories.
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