A Survey on Map-Matching Algorithms

TL;DR

This survey reviews current map-matching algorithms, categorizes solutions based on models and scenarios, and experimentally compares representative methods to understand factors affecting performance.

Contribution

It introduces a new categorization of map-matching solutions and provides experimental analysis on how different factors influence algorithm performance.

Findings

Matching model significantly impacts performance

Trajectory quality affects matching accuracy

Data compression influences latency

Abstract

The map-matching is an essential preprocessing step for most of the trajectory-based applications. Although it has been an active topic for more than two decades and, driven by the emerging applications, is still under development. There is a lack of categorisation of existing solutions recently and analysis for future research directions. In this paper, we review the current status of the map-matching problem and survey the existing algorithms. We propose a new categorisation of the solutions according to their map-matching models and working scenarios. In addition, we experimentally compare three representative methods from different categories to reveal how matching model affects the performance. Besides, the experiments are conducted on multiple real datasets with different settings to demonstrate the influence of other factors in map-matching problem, like the trajectory quality, data compression and matching latency.