Offline Map Matching Based on Localization Error Distribution Modeling
🤖 AI Summary
Offline map matching faces two key challenges: (i) existing methods assume uniform localization error distribution (LED) across urban areas, ignoring environmental heterogeneity and thus over-expanding the path search space; (ii) distinguishing local non-shortest paths from deliberate detours remains difficult. This paper addresses sparse-trajectory matching by proposing a fine-grained LED modeling and dynamic path optimization framework. We introduce the first bus-fixed-route–based, region-level LED estimation method. Additionally, we design a subregion-dependent graph search algorithm coupled with a sliding-window dynamic path scoring mechanism to jointly model local detours and global path consistency. Evaluated on real-world bus and taxi datasets, our approach significantly outperforms state-of-the-art methods in matching accuracy, while simultaneously reducing search space and improving computational efficiency.
Application Category
📝 Abstract
Offline map matching involves aligning historical trajectories of mobile objects, which may have positional errors, with digital maps. This is essential for applications in intelligent transportation systems (ITS), such as route analysis and traffic pattern mining. Existing methods have two main limitations: (i) they assume a uniform Localization Error Distribution (LED) across urban areas, neglecting environmental factors that lead to suboptimal path search ranges, and (ii) they struggle to efficiently handle local non-shortest paths and detours. To address these issues, we propose a novel offline map matching method for sparse trajectories, called LNSP, which integrates LED modeling and non-shortest path detection. Key innovations include: (i) leveraging public transit trajectories with fixed routes to model LED in finer detail across different city regions, optimizing path search ranges, and (ii) scoring paths using sub-region dependency LED and a sliding window, which reduces global map matching errors. Experimental results using real-world bus and taxi trajectory datasets demonstrate that the LNSP algorithm significantly outperforms existing methods in both efficiency and matching accuracy.
Problem
Research questions and friction points this paper is trying to address.
Modeling localization error distribution for accurate offline map matching
Addressing non-shortest paths and detours in sparse trajectory alignment
Optimizing path search ranges using region-specific error distributions
Innovation
Methods, ideas, or system contributions that make the work stand out.
Models LED using public transit trajectories
Optimizes path search ranges regionally
Scores paths with sub-region LED
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