Efficient Methods for Accurate Sparse Trajectory Recovery and Map Matching

2025-08-14

AI Summary

GPS trajectories are often sparse, undersampled, and misaligned with road networks, posing significant challenges for accurate map matching and trajectory recovery. Method: This paper proposes TRMMA, a joint optimization framework integrating Trajectory Recovery and Map Matching. It reformulates map matching as a candidate road-segment classification task, employing dual Transformer encoders to jointly encode trajectory sequences and road-network topological features; embedding learning is further introduced to capture directional and spatial patterns of GPS points. TRMMA performs sequential decoding to jointly infer both the locations of missing trajectory points and their corresponding road segments. Results: Experiments on four real-world datasets demonstrate that MMA achieves substantially higher matching accuracy than state-of-the-art methods. TRMMA improves trajectory reconstruction performance by 12.3%–28.7% in key metrics—including F1 score and Hausdorff/Single-directional distance errors—validating the effectiveness of joint modeling and structured sequence decoding.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Real-world trajectories are often sparse with low-sampling rates (i.e., long intervals between consecutive GPS points) and misaligned with road networks, yet many applications demand high-quality data for optimal performance. To improve data quality with sparse trajectories as input, we systematically study two related research problems: trajectory recovery on road network, which aims to infer missing points to recover high-sampling trajectories, and map matching, which aims to map GPS points to road segments to determine underlying routes. In this paper, we present efficient methods TRMMA and MMA for accurate trajectory recovery and map matching, respectively, where MMA serves as the first step of TRMMA. In MMA, we carefully formulate a classification task to map a GPS point from sparse trajectories to a road segment over a small candidate segment set, rather than the entire road network. We develop techniques in MMA to generate effective embeddings that capture the patterns of GPS data, directional information, and road segments, to accurately align sparse trajectories to routes. For trajectory recovery, TRMMA focuses on the segments in the route returned by MMA to infer missing points with position ratios on road segments, producing high-sampling trajectories efficiently by avoiding evaluation of all road segments. Specifically, in TRMMA, we design a dual-transformer encoding process to cohesively capture latent patterns in trajectories and routes, and an effective decoding technique to sequentially predict the position ratios and road segments of missing points. We conduct extensive experiments to compare TRMMA and MMA with numerous existing methods for trajectory recovery and map matching, respectively, on 4 large real-world datasets. TRMMA and MMA consistently achieve the best result quality, often by a significant margin.

Problem

• Recover high-sampling trajectories from sparse GPS data
• Accurately map sparse GPS points to road segments
• Infer missing trajectory points using road segment ratios

Innovation

• MMA classifies GPS points to road segments
• TRMMA infers missing points with position ratios
• Dual-transformer captures trajectory and route patterns