NLP-enabled Trajectory Map-matching in Urban Road Networks using a Transformer-based Encoder-decoder

TL;DR

This paper presents a transformer-based deep learning framework for map-matching GPS trajectories to urban road networks, improving accuracy by capturing driver preferences and spatial noise variations in an end-to-end manner.

Contribution

It introduces a novel NLP-inspired, deep learning approach for trajectory map-matching that outperforms traditional heuristic methods, leveraging contextual representations of GPS data.

Findings

Outperforms conventional methods in synthetic data tests

Achieves 75% accuracy on real-world GPS traces from Manhattan

Demonstrates the effectiveness of transformers in urban mobility modeling

Abstract

Vehicular trajectory data from geolocation telematics is vital for analyzing urban mobility patterns. Map-matching aligns noisy, sparsely sampled GPS trajectories with digital road maps to reconstruct accurate vehicle paths. Traditional methods rely on geometric proximity, topology, and shortest-path heuristics, but they overlook two key factors: (1) drivers may prefer routes based on local road characteristics rather than shortest paths, revealing learnable shared preferences, and (2) GPS noise varies spatially due to multipath effects. These factors can reduce the effectiveness of conventional methods in complex scenarios and increase the effort required for heuristic-based implementations. This study introduces a data-driven, deep learning-based map-matching framework, formulating the task as machine translation, inspired by NLP. Specifically, a transformer-based encoder-decoder model learns contextual representations of noisy GPS points to infer trajectory behavior and road structures in an end-to-end manner. Trained on large-scale trajectory data, the method improves path estimation accuracy. Experiments on synthetic trajectories show that this approach outperforms conventional methods by integrating contextual awareness. Evaluation on real-world GPS traces from Manhattan, New York, achieves 75% accuracy in reconstructing navigated routes. These results highlight the effectiveness of transformers in capturing drivers' trajectory behaviors, spatial dependencies, and noise patterns, offering a scalable, robust solution for map-matching. This work contributes to advancing trajectory-driven foundation models for geospatial modeling and urban mobility applications.