A novel trajectory similarity–based approach for location prediction

Abstract

Location prediction impacts a wide range of research areas in mobile environment. The abundant mobility data, produced by mobile devices, make this research area attractive. Randomness makes people's future whereabouts hard to predict, although studies have proved that human mobility shows strong regularity. Most previous works, in general, tend to discover an association between a user's social relations in real world and variances in trajectory and then utilize this association to model the user's mobility which is used for location prediction. However, these methods normally require some specific data, which make them hard to be migrated to other platforms. Moreover, by focusing on social relations, these methods neglect the potential value of the associations among strangers' trajectory. Based on this argument, this article has proposed a novel location prediction approach trajectory similarity-based location prediction. It applies the social contagion theory and introduces a novel similarity computing-based trajectory method along with the trajectory sampling, which is achieved by covering algorithm to accelerate the process of computing similarity. Experiment results on real dataset show that trajectory similarity-based location prediction achieves higher accuracy and stability comparing to the state-of-the-art approaches.

A Survey for the Ranking of Trajectory Prediction Algorithms on Ubiquitous Wireless Sensors

“…The TLP algorithm works on the social mobility paradigm. The group of users are discovered having high trajectory similarity [ 20 ]. TLP is a multi-step process.…”

“…A future location prediction method, i.e., Spatial-Temporal Recurrent Neural Networks (ST-RNN) was presented by [ 20 ]. The experimental results on real datasets showed that ST-RNN outperformed the futuristic methods and can model the spatial and temporal contexts.…”

A Systematic Analysis of Fine-Grained Human Mobility Prediction with On-Device Contextual Data

“…Generally, location data have three forms. A "location" is actually a point of interest (POI) (e.g., checkin data [18,5,15,8,12,13,19,20,22,23]), a connected region (e.g., a region covered by a base station [10,11,2] or a surveillance camera [17,6], or a pair of coordinates (e.g., GPS coordinates [16,3,4,7,9,1,21]). For POI data, the location granularity is fully determined by the granularity of POIs.…”

“…The ability to predict the next location of a user is widely believed to be beneficial for many applications and services, including but not limited to smart transportation, personalized service recommendation, public resource management, and so on. Up to now, a large amount of mobility prediction methods have been proposed, ranging from pattern-based methods [1,2,3,4,5], to Markov model-based methods [6,7,8,9,10,11,12,13,14,15,16,17], and to deep neural networks [18,19,20,21]. These models are applied to various scenarios, including indoor walking [9], venue recommendation [15], urban commuting [19], or even intercontinental trips [18].…”