Distant-Time Location Prediction in Low-Sampling-Rate Trajectories

With the growth of location-based services and social services, low-sampling-rate trajectories from check-in data or photos with geo-tag information becomes ubiquitous. In general, most detailed moving information in low-sampling-rate trajectories are lost. Prior works have elaborated on distant-time location prediction in high-sampling-rate trajectories. However, existing prediction models are pattern-based and thus not applicable due to the sparsity of data points in low-sampling-rate trajectories. For example, it becomes difficult to derive trajectory patterns, let alone utilizing trajectory patterns for distant-time location prediction. In this paper, given a query time, the current location and time, we aim to predict the location of an object at the query time. To address the sparsity in low-sampling-rate trajectories, we develop a Reachability-based prediction model on Time-constrained Mobility Graph (abbreviated as RTMG) to predict locations for distant-time queries. Specifically, we design an adaptive temporal exploration approach to extract effective supporting trajectories that are temporally close to the query time. These data points are then represented as a Time-constrained user mobility Graph (refers to as TG). In light of TG, we further derive the reachability probabilities among locations in TG. Thus, a location with maximum reachability from the current location among all possible locations in supporting trajectories is considered as the prediction result. To efficiently process queries, we proposed an index structure SOIT to organize location records for on-line query processing. We conduct extensive experiments on real low-sampling-rate datasets and demonstrate the effectiveness and efficiency of RTMG.