Multi-track map matching

Abstract

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 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.

A Geometric Framework for Detection of Critical Points in a Trajectory Using Convex Hulls

“…This method applies a recursive process to measurements that are observed over time (i.e., the positions coming in the GPS receiver), and predicts positions that tend to be closer to the true values of the measurements [48,[69][70][71][72]. This means that after linear estimation of the points for each trajectory, the standard deviation of distance ( 2018, 7, 14 6 of 21 y cases, such location error at the processing phase can be solved by map matching e main principle behind map matching methods is to minimize the distance between h on the map and the input trajectory [66][67][68]. Remaining noisy data are removed n of a Kalman filter to smooth the positions by recursively modifying error values.…”

Large-scale joint map matching of GPS traces

“…They are referred as single-track map matching algorithms in [5]. The focus of this work is in multi-track map matching, where one matches a large number of possibly sparse trajectories simultaneously to the map.…”

“…The focus of this work is in multi-track map matching, where one matches a large number of possibly sparse trajectories simultaneously to the map. In [5], multiple sparse trajectories of the same route, and with the same starting and ending positions are used to produce an accurate path on the map. This method extracts a global order on the sample points based on the partial order defined by individual trajectories, then uses a singletrack map matching to produce the matched path.…”

“…We refer them as single-track map matching algorithms (SMM) [5]. They are typically formulated with the objective of minimizing the distance between the projected path on the map and the input trajectory, and of achieving some other regularization objectives, such as minimizing the length of the path.…”

“…Moreover, the overlapping parts of these trajectories are jointly determined, which improves the robustness of the map matching procedure. The multi-track idea was first seen in [5], with the assumption that all trajectories are sampled from the same underlying path. Our algorithm, designed to apply multi-track map matching on heterogeneous data, offers more practical use on large-scale map matching applications.…”