标题： 多轨迹匹配 显示英文标题

标题： Multi-track Map Matching

摘要： 我们研究用于将用户轨迹（由时间有序的位置点组成）与道路网络中的路径进行匹配的算法。 以往的工作集中在位置数据线性有序且包含密集且规律的样本的场景中。 在本工作中，我们考虑\emph{多轨迹匹配}的情况，其中位置数据来自同一路径上的不同行程，每个行程的样本非常稀疏。 这捕捉了现实场景，即用户反复在常规路线上行驶，由于能耗限制或用户仅在主动使用服务时才收集样本，导致样本稀疏。 在多轨迹问题中，所有组合位置的集合只是部分有序，而不是之前地图匹配算法所需的全局有序。 我们提出了两种方法，迭代投影方案和图拉普拉斯方案，通过使用单轨迹地图匹配子程序来解决多轨迹问题。 我们还提出了一种增强技术，可以应用于这两种方法中的一种，以提高估计路径的准确性。 此外，为了处理单轨迹地图匹配中的可变采样率，我们提出了一种基于特定正则化成本函数的方法，该方法可以适应不同的采样率和测量误差。 我们评估了我们的技术在不同采样误差和采样率配置下重建轨迹的有效性。 显示英文摘要

摘要： We study algorithms for matching user tracks, consisting of time-ordered location points, to paths in the road network. Previous work has focused on the scenario where the location data is linearly ordered and consists of fairly dense and regular samples. In this work, we consider the \emph{multi-track map matching}, where the location data comes from different trips on the same route, each with very sparse samples. This captures the realistic scenario where users repeatedly travel on regular routes and samples are sparsely collected, either due to energy consumption constraints or because samples are only collected when the user actively uses a service. In the multi-track problem, the total set of combined locations is only partially ordered, rather than globally ordered as required by previous map-matching algorithms. We propose two methods, the iterative projection scheme and the graph Laplacian scheme, to solve the multi-track problem by using a single-track map-matching subroutine. We also propose a boosting technique which may be applied to either approach to improve the accuracy of the estimated paths. In addition, in order to deal with variable sampling rates in single-track map matching, we propose a method based on a particular regularized cost function that can be adapted for different sampling rates and measurement errors. We evaluate the effectiveness of our techniques for reconstructing tracks under several different configurations of sampling error and sampling rate.