从部分观测值推断不确定轨迹
Inferring Uncertain Trajectories from Partial Observations
摘要：
全球定位系统设备的爆炸式增长带来了丰富的轨迹数据。然而，实际上，这些轨迹中的大多数是以低采样率收集的，并且仅提供关于其实际穿过的路线的部分观测。因此，他们陷入了不确定性。在本文中，我们开发了一种称为InferTra的技术，从网络约束的部分观测中推断不确定的轨迹。而不是预测最可能的路线，推断的不确定轨迹采取边加权图的形式，并以整体的方式总结所有可能的路线。对于轨迹推断，InferTra通过从历史轨迹数据库中学习网络移动性模型（NMM）来采用吉布斯采样。在真实轨迹数据库上进行的大量实验表明，InferTra基于图形的方法比最先进的技术准确率高出50%，速度快20倍，并且功能更加广泛。
英文摘要：
The explosion in the availability of GPS-enabled devices has resulted in an abundance of trajectory data. In reality, however, majority of these trajectories are collected at a low sampling rate and only provide partial observations on their actually traversed routes. Consequently, they are mired with uncertainty. In this paper, we develop a technique called InferTra to infer uncertain trajectories from network-constrained partial observations. Rather than predicting the most likely route, the inferred uncertain trajectory takes the form of an edge-weighted graph and summarizes all probable routes in a holistic manner. For trajectory inference, InferTra employs Gibbs sampling by learning a Network Mobility Model (NMM) from a database of historical trajectories. Extensive experiments on real trajectory databases show that the graph-based approach of InferTra is up to 50% more accurate, 20 times faster, and immensely more versatile than state-of-the-art techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
