Enhancing Interactive Voting-Based Map Matching: Improving Efficiency and Robustness for Heterogeneous GPS Trajectories
This work addresses the challenge of processing heterogeneous GPS trajectories for map matching applications, though it is incremental as it builds upon an existing algorithm.
The paper tackles the problem of accurately reconstructing GPS trajectories with varying sampling rates by enhancing the Interactive Voting-Based Map Matching algorithm. The result is a more efficient and robust method that can be applied to any geographic region covered by OpenStreetMap's road network.
This paper presents an enhanced version of the Interactive Voting-Based Map Matching algorithm, designed to efficiently process trajectories with varying sampling rates. The main aim is to reconstruct GPS trajectories with high accuracy, independent of input data quality. Building upon the original algorithm, developed exclusively for aligning GPS signals to road networks, we extend its capabilities by integrating trajectory imputation. Our improvements also include the implementation of a distance-bounded interactive voting strategy to reduce computational complexity, as well as modifications to address missing data in the road network. Furthermore, we incorporate a custom-built asset derived from OpenStreetMap, enabling this approach to be smoothly applied in any geographic region covered by OpenStreetMap's road network. These advancements preserve the core strengths of the original algorithm while significantly extending its applicability to diverse real-world scenarios.