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

arXiv: https://arxiv.org/abs/2404.12460 · HTML: https://mlsanity.com/paper/2404.12460/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 79 references that are in the map.
- First posted on arXiv 18 Apr 2024.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction](https://mlsanity.com/paper/2010.09885/) (2020)
- [Adaptive Input Representations for Neural Language Modeling](https://mlsanity.com/paper/1809.10853/) (2018)
- [OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks](https://mlsanity.com/paper/1611.01890/) (2016)
- [Efficient softmax approximation for GPUs](https://mlsanity.com/paper/1609.04309/) (2016)
- [Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture](https://mlsanity.com/paper/1802.06338/) (2018)
- [Improving Multilingual Models with Language-Clustered Vocabularies](https://mlsanity.com/paper/2010.12777/) (2020)
- [A Survey on Map-Matching Algorithms](https://mlsanity.com/paper/1910.13065/) (2019)
- [Pre-training Contextual Location Embeddings in Personal Trajectories via Efficient Hierarchical Location Representations](https://mlsanity.com/paper/2310.01252/) (2023)
- [Applications of Trajectory Data from the Perspective of a Road Transportation Agency: Literature Review and Maryland Case Study](https://mlsanity.com/paper/1708.07193/) (2017)
- [Los Angeles Metro Bus Data Analysis Using GPS Trajectory and Schedule Data (Demo Paper)](https://mlsanity.com/paper/1909.00955/) (2019)
- [Big Data Generated by Connected and Automated Vehicles for Safety Monitoring, Assessment and Improvement, Final Report (Year 3)](https://mlsanity.com/paper/2101.06106/) (2021)

## Built on by (most cited first, 2 of 2)

- [Rectify and Align GPS Points to Parking Spots via Rank-1 Constraint](https://mlsanity.com/paper/2510.13439/) (2025)
- [SceneGTMM: A Conformal Mapping-based Scene-Aware Transferable GNN-Transformer Dual-Graph Interaction Framework for Map Matching](https://mlsanity.com/paper/2608.19298/) (2026)

## Abstract (arXiv)

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.