Large-scale joint map matching of GPS traces

Abstract

We present a robust method for solving the map matching problem exploiting massive GPS trace data. Map matching is the problem of determining the path of a user on a map from a sequence of GPS positions of that user -what we call a trajectory. Commonly obtained from GPS devices, such trajectory data is often sparse and noisy. As a result, the accuracy of map matching is limited due to ambiguities in the possible routes consistent with trajectory samples. Our approach is based on the observation that many regularity patterns exist among common trajectories of human beings or vehicles as they normally move around. Among all possible connected k-segments on the road network (i.e., consecutive edges along the network whose total length is approximately k units), a typical trajectory collection only utilizes a small fraction. This motivates our data-driven map matching method, which optimizes the projected paths of the input trajectories so that the number of the k-segments being used is minimized. We present a formulation that admits efficient computation via alternating optimization. Furthermore, we have created a benchmark for evaluating the performance of our algorithm and others alike. Experimental results demonstrate that the proposed approach is superior to state-of-art single trajectory map matching techniques. Moreover, we also show that the extracted popular k-segments can be used to process trajectories that are not present in the original trajectory set. This leads to a map matching algorithm that is as efficient as existing single trajectory map matching algorithms, but with much improved map matching accuracy.

Uncertainty-Based Map Matching: The Space-Time Prism and k-Shortest Path Algorithm

“…Li et al [22] propose a so-called multi-track MM algorithm (opposite the traditional single-track MM). The rationale behind this method is the observation that in many cases, moving objects move following the same pattern, so they apply MM techniques to a group of trajectories in a sample, and not individually.…”

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.…”

“…Next, the points with the distance higher than 2 , 7, 14 6 of 21 ases, such location error at the processing phase can be solved by map matching ain principle behind map matching methods is to minimize the distance between n the map and the input trajectory [66][67][68]. Remaining noisy data are removed f a Kalman filter to smooth the positions by recursively modifying error values.…”

Robust Map Matching for Heterogeneous Data via Dominance Decompositions

“…The parameters might just be ill suited. Other approaches leverage homogeneity directly for traces of similar data origin by optimizing a numeric model jointly for a set of input traces [10].…”