Discovery of contrast corridors from trajectory data in heterogeneous dynamic cellular networks

In 5G networks, the deployment and management of mobile edge computing infrastructure is a major challenge for mobile operators. Most recent work focused on extracting static movement patterns of mobile users from the trajectories generated during a specific time period to help with the management and orchestration of network resources. However, movement patterns of mobile users are not static over time. Understanding significant differences in mobile users’ movement during different time periods can provide insights for mobile operators to dynamically reconfigure the network in response to the changes in traffic flows by time of day. Therefore, in this paper, we propose a framework based on contrast data mining to identify significantly different movement patterns, which we model as corridors, during different time periods. To measure the difference, an improved distance measure based on a modified Hausdorff distance and Earth Movers’ distance is proposed to calculate the dissimilarity between the identified corridors, which considers the spatial heterogeneity of mobile networks. To further extract the significantly different corridors, we formulate the definition of contrast corridors of mobile users’ movement. Experimental results on synthetic datasets as well as real-life datasets collected by China Mobile show that our method can effectively and robustly detect contrast corridors from trajectories generated from different time periods in mobile networks by improving the F1 score by 20% on average.

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