Network-less trajectory imputation
Network-less trajectory imputation
复制标题
DOI：
10.1145/3557915.3560942
复制
发表时间：
2022-11
期刊：
影响因子：
--
通讯作者：
M. Elshrif;Keivin Isufaj;M. Mokbel
中科院分区：
文献类型：
--
作者：
M. Elshrif;Keivin Isufaj;M. Mokbel
通过支持 GPS 的设备收集大量轨迹数据的能力已经实现了每天广泛使用的无数非常重要的应用程序。这包括城市计算、交通以及用于路线和导航的地图 API。不幸的是，所有这些应用的一个主要障碍是收集的轨迹的准确性。由于采样率较低，每两个连续收集点之间的空间和时间距离较大，轨迹通常很稀疏。本文介绍了 TrImpute；一种新颖的轨迹插补框架，在真实点之间插入人工 GPS 点，使得插补轨迹最终与以更高采样率收集此类轨迹的情况非常相似。与所有现有轨迹插补技术不同，TrImpute 不假设了解底层道路网络。当底层道路网络不可用或不准确时，这使得它更加实用。真实数据集上的实验结果和 TrImpute 的实际部署表明，它具有高度可扩展性、准确性，并且可以通过向轨迹应用程序提供高度准确的轨迹来显着提高轨迹应用程序的性能。
The ability to collect large numbers of trajectory data through GPS-enabled devices have enabled a myriad of very important applications that are widely used on a daily basis. This includes urban computing, transportation, and map APIs for routing and navigation. Unfortunately, a major hinder for all these applications is the accuracy of collected trajectories. Due to low sampling rates, trajectories are usually sparse in terms of the large spatial and temporal distances between each two consecutive collected points. This paper presents TrImpute; a novel framework for trajectory imputation that inserts artificial GPS points between the real ones in a way that the imputed trajectories end up to be very similar to the case if such trajectories were collected with a much higher sampling rate. Unlike all prior trajectory imputation techniques, TrImpute does not assume the knowledge of the underlying road network. This makes it more practical when the underlying road network is not available or inaccurate. Experimental results on real datasets and a real deployment of TrImpute show that it is highly scalable, accurate, and can significantly boost the performance of trajectory applications by feeding them highly accurate trajectories.