LNSP models city-wide GPS error distributions from fixed bus routes and uses them, plus detour detection, to match sparse trajectories more accurately than three existing map matching methods.
A Survey on Map-Matching Algorithms
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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.
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cs.SI 1years
2025 1verdicts
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Offline Map Matching Based on Localization Error Distribution Modeling
LNSP models city-wide GPS error distributions from fixed bus routes and uses them, plus detour detection, to match sparse trajectories more accurately than three existing map matching methods.