Reconstructing Movement from Sparse Samples: Enhanced Spatio-Temporal Matching Strategies for Low-Frequency Data

2026-03-10

AI Summary

This study addresses the challenges of low matching accuracy and poor computational efficiency in map-matching low-frequency GPS trajectories within dense road networks. To this end, the authors propose an enhanced spatiotemporal trajectory matching method that integrates a dynamic buffer mechanism, an adaptive observation probability model, an improved temporal scoring function, and a path inference strategy based on historical behavioral patterns. Notably, the approach achieves high-quality matching without requiring ground-truth annotations. Experimental evaluation on real-world trajectory data from Milan demonstrates the superiority of the proposed method, with significant improvements across multiple metrics compared to existing algorithms, while simultaneously maintaining high computational efficiency and accurate path reconstruction.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Localization, Mapping, and Navigation
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Security and Privacy: Tracking, profiling, and countermeasures against them

Abstract

This paper explores potential improvements to the Spatial-Temporal Matching algorithm for matching the GPS trajectories to road networks. While this algorithm is effective, it presents some limitations in computational efficiency and the accuracy of the results, especially in dense environments with relatively high sampling intervals. To address this, the paper proposes four modifications to the original algorithm: a dynamic buffer, an adaptive observation probability, a redesigned temporal scoring function, and a behavioral analysis to account for the historical mobility patterns. The enhancements are assessed using real-world data from the urban area of Milan, and through newly defined evaluation metrics to be applied in the absence of ground truth. The results of the experiment show significant improvements in performance efficiency and path quality across various metrics.

Problem

• trajectory reconstruction
• sparse sampling
• map matching
• low-frequency GPS data
• spatio-temporal matching

Innovation

• spatio-temporal matching
• low-frequency GPS trajectories
• dynamic buffer
• adaptive observation probability
• behavioral mobility patterns