Zero-Shot Cellular Trajectory Map Matching

2025-08-08

AI Summary

Existing cellular trajectory map matching methods fail in unseen regions due to reliance on region-specific identifiers and labeled training data, resulting in poor generalization. To address this zero-shot cross-domain challenge, we propose Zero-shot Cellular Trajectory Map Matching (Zero-shot CTMM). Our framework introduces three key innovations: (1) a pixel-level trajectory calibration mechanism grounded in transferable geospatial knowledge; (2) a soft-clustering–driven scene-adaptive mixture-of-experts model; and (3) a spatiotemporal-aware neural network integrating variational autoencoders with Gaussian mixture models, jointly constraining path search to ensure topologically compliant reconstruction. Critically, Zero-shot CTMM requires no training data from the target region. Under zero-shot cross-domain evaluation, it achieves a 16.8% absolute improvement in matching accuracy over state-of-the-art methods, significantly enhancing robustness and alignment fidelity of cellular trajectories in previously unobserved geographic areas.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Localization, Mapping, and Navigation
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Cellular Trajectory Map-Matching (CTMM) aims to align cellular location sequences to road networks, which is a necessary preprocessing in location-based services on web platforms like Google Maps, including navigation and route optimization. Current approaches mainly rely on ID-based features and region-specific data to learn correlations between cell towers and roads, limiting their adaptability to unexplored areas. To enable high-accuracy CTMM without additional training in target regions, Zero-shot CTMM requires to extract not only region-adaptive features, but also sequential and location uncertainty to alleviate positioning errors in cellular data. In this paper, we propose a pixel-based trajectory calibration assistant for zero-shot CTMM, which takes advantage of transferable geospatial knowledge to calibrate pixelated trajectory, and then guide the path-finding process at the road network level. To enhance knowledge sharing across similar regions, a Gaussian mixture model is incorporated into VAE, enabling the identification of scenario-adaptive experts through soft clustering. To mitigate high positioning errors, a spatial-temporal awareness module is designed to capture sequential features and location uncertainty, thereby facilitating the inference of approximate user positions. Finally, a constrained path-finding algorithm is employed to reconstruct the road ID sequence, ensuring topological validity within the road network. This process is guided by the calibrated trajectory while optimizing for the shortest feasible path, thus minimizing unnecessary detours. Extensive experiments demonstrate that our model outperforms existing methods in zero-shot CTMM by 16.8%.

Problem

• Align cellular location sequences to road networks accurately
• Enable high-accuracy map matching without region-specific training
• Mitigate positioning errors in cellular trajectory data

Innovation

• Pixel-based trajectory calibration using geospatial knowledge
• Gaussian mixture VAE for scenario-adaptive expert identification
• Spatial-temporal module capturing sequential and uncertainty features