A Study on map-matching and map inference problems

Abstract

The recent upsurge of GPS(Global Positioning System)-equipped devices has enabled the tracking of users'/vehicles' locations. Meanwhile, the emergence of various location-based services emphasises the crucial role of the underlying digital map. However, the intrinsic inaccuracy of both maps and GPS trajectories has been a major issue that hinders the development of location-based applications, like navigation, location-based recommendation and traffic analysis. Several techniques are proposed to deal with the data quality issues in maps and GPS trajectories, respectively: (1) To avoid the excessive cost of ground surveying for map construction, the map inference algorithm aims to construct a digital map from GPS trajectories automatically. (2) To ensure the recency of digital maps, the map update algorithm focusses on updating an existing map using recent GPS trajectories. (3) To get rid family), Dr.

Evaluation of map‐matching algorithms for smartphone‐based active travel data

“… Success rate compares the map‐matched route to the ground‐truth route to see if they are identical, with a binary outcome [14]. Route mismatch fraction is the cumulative length erroneously subtracted or added to the ground‐truth route by the map‐matching algorithm, divided by the length of the ground‐truth route [18, 48]. See Figure 2 for an illustration. The overlapping ratio is the ratio of the number of common links between the ground‐truth and map‐matched routes to the union of all the links present in the two [46, 47].…”

“… The length index is the ratio of the length of the map‐matched route to the cumulative distance between each consecutive pair of GPS records [24, 25, 48]. See Figure 6 for an illustration. …”

“…2. Route mismatch fraction is the cumulative length erroneously subtracted or added to the ground-truth route by the map-matching algorithm, divided by the length of the ground-truth route [18,48]. See Figure 2 for an illustration.…”

A Practical Guide to an Open-Source Map-Matching Approach for Big GPS Data

“…Other examples for map-matching frameworks are pgMapMatch 4 and Fast Map-Matching (FFM). 5 However, as the advantages of OSRM and Valhalla outweigh the other frameworks, they are not discussed here.…”

“…A comparison between different map-matching algorithms and generally evaluating the map-matching quality is not straightforward [38], since the "true" positions of the recorded points are unknown. However, almost all the matching algorithms implemented by available mapmatching services demonstrate good performance when the sampling rate is high, i.e., time interval between measured GPS points of less than 30 s [4][5][6].…”