Abstract
Positioning devices allow users’ movement to be recorded. The GPS (Global Positioning System) trajectory data typically consists of spatiotemporal points, which make up the major part of the big data concerning urban life. Existing knowledge extraction methods about the trajectory share a general limitation—they only investigate data from a spatiotemporal aspect, but fail to take the semantic information of trajectories into consideration. Therefore, extracting the semantic information of trajectories with the context of big data is challenging pattern recognition task that has practical application prospects. In this paper, a system is proposed to extract the semantic trajectory patterns of positioning device users. Firstly, a spatiotemporal threshold and clustering based pre-processing model is proposed to process the raw data. Then, we design a probabilistic generative model to annotate the semantic information of each trajectory after the pre-processing procedure. Finally, we apply the PrefixSpan algorithm to mine the semantic trajectory patterns. We verify our system on a large dataset of users’ real trajectories over a period of 5 years in Beijing, China. The results of the experiment indicate that our system produces meaningful patterns.
Highlights
The increasing use of private vehicles with positioning services and the rapid advance in wireless mobile communication technology enable us to collect large-scale GPS trajectories [1]
Mining trajectory patterns from the GPS data of users have incited wide interest in both academia and industry since they are valuable for a variety of urban applications, i.e., solving transportation problems and developing reasonable urban planning
If a number of users are found to pass from La to Lb at a certain time, relevant departments can consider opening a new bus line connecting La to Lb
Summary
The increasing use of private vehicles with positioning services and the rapid advance in wireless mobile communication technology (such as 4G and beyond) enable us to collect large-scale GPS trajectories [1]. Mining trajectory patterns from the GPS data of users have incited wide interest in both academia and industry since they are valuable for a variety of urban applications, i.e., solving transportation problems and developing reasonable urban planning. A general limitation—the failure to take the semantics of a trajectory into consideration—exists in the present research [5,6,7,8,9,10] on trajectory pattern mining. Our work is based on service provider data, the bottom layer of the spatiotemporal data, which is continuous but without any obvious semantic information. Based on the extracted stopover points and the map information, we propose a probabilistic generative model to accomplish the semantic annotation. We mine semantic trajectory patterns with the sequence mining algorithm to construct a framework of mining semantic information from spatiotemporal data
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