Zero-Shot Cellular Trajectory Map Matching

TL;DR

This paper introduces a zero-shot cellular trajectory map-matching method that leverages transferable geospatial knowledge, probabilistic modeling, and spatial-temporal features to accurately align cellular data to road networks without region-specific training.

Contribution

The paper proposes a novel pixel-based trajectory calibration approach combined with a Gaussian mixture model and spatial-temporal modules for zero-shot map matching, improving adaptability and accuracy.

Findings

Outperforms existing methods by 16.8% in accuracy.

Effectively captures sequential features and location uncertainty.

Enhances zero-shot map matching without additional training.

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\%.