Name
Abstract | ||
|---|---|---|
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 mov- ing 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. To address the sparsity in low-sampling-rate trajectories, we develop a Reachability-based prediction model on Time-constrained Mobility Graph (RTMG) to predict locations for distant-time queries. Specifically, we de- sign an adaptive temporal exploration approach to extract effective supporting trajectories that are temporally close to the query time. Based on the supporting trajectories, a Time-constrained mobility Graph (TG) is constructed to capture mobility information at the given query time. In light of TG, we further derive the reacha- bility probabilities among locations in TG. Thus, a location with maximum reachability from the current location among all possi- ble locations in supporting trajectories is considered as the predic- tion result. To efficiently process queries, we proposed the index structure Sorted Interval-Tree (SOIT) to organize location records. Extensive experiments with real data demonstrated the effective- ness and efficiency of RTMG. First, RTMG with adaptive tempo- ral exploration significantly outperforms the existing pattern-based prediction model HPM [2] over varying data sparsity in terms of higher accuracy and higher coverage. Also, the proposed index structure SOIT can efficiently speedup RTMG in large-scale trajec- tory dataset. In the future, we could extend RTMG by considering more factors (e.g., staying durations in locations, application us- ages in smart phones) to further improve the prediction accuracy. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2487575.2487707 | KDD |
Keywords | Field | DocType |
sampling-rate trajectory,prediction accuracy,existing prediction model,existing pattern-based prediction model,ble location,distant-time location prediction,reachability-based prediction model,query time,low-sampling-rate trajectory,speedup rtmg,reachability,sparsity | Data point,Data mining,Graph,Computer science,Sampling (signal processing),Reachability,Artificial intelligence,Predictive modelling,Location prediction,Machine learning,Speedup | Conference |
Citations | PageRank | References |
6 | 0.45 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meng-Fen Chiang | 1 | 91 | 8.54 |
| Yung-Hsiang Lin | 2 | 7 | 0.83 |
| Wen-Chih Peng | 3 | 1645 | 106.49 |
| Philip S. Yu | 4 | 30670 | 3474.16 |