Inferring distant-time location in low-sampling-rate trajectories

Meng-Fen Chiang, Yung-Hsiang Lin, Wen-Chih Peng, Philip S. Yu
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引用次数: 18

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.
在低采样率轨迹中推断遥远时间的位置
随着基于位置的服务和社交服务的发展,从签到数据或带有地理标签信息的照片中获取低采样率的轨迹变得无处不在。一般来说,在低采样率的轨迹中,大多数详细的运动信息都会丢失。先前的工作已经详细阐述了在高采样率轨迹下的远时间定位预测。然而,由于低采样率轨迹中数据点的稀疏性,现有的预测模型是基于模式的,因此不适用。为了解决低采样率轨迹的稀疏性问题,我们在时间约束迁移图(RTMG)上开发了一个基于可达性的预测模型来预测远程查询的位置。具体来说,我们设计了一种自适应时间探索方法来提取在时间上接近查询时间的有效支持轨迹。在支持轨迹的基础上,构造了一个时间约束迁移图(TG)来捕获给定查询时间的迁移信息。在热重的基础上,进一步推导了热重中各位置之间的可达性概率。因此,在支持轨迹的所有可能位置中,与当前位置可达性最大的位置被认为是预测结果。为了有效地处理查询,我们提出了排序区间树(SOIT)索引结构来组织位置记录。大量的实际数据实验证明了RTMG的有效性和高效性。首先,在不同的数据稀疏度下,具有自适应节奏探索的RTMG在更高的精度和更高的覆盖率方面显著优于现有的基于模式的预测模型HPM[2]。此外,所提出的索引结构SOIT可以有效地加速大规模轨迹数据集的RTMG。在未来,我们可以通过考虑更多的因素(例如,在某个地点停留的时间、智能手机的应用时间)来扩展RTMG,以进一步提高预测精度。
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