# A Survey on Map-Matching Algorithms

arXiv: https://arxiv.org/abs/1910.13065 · HTML: https://mlsanity.com/paper/1910.13065/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2019.

## Built on by (most cited first, 13 of 13)

- [Origin-Destination Travel Time Oracle for Map-based Services](https://mlsanity.com/paper/2307.03048/) (2023)
- [Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities](https://mlsanity.com/paper/2305.19591/) (2023)
- [Blurred Encoding for Trajectory Representation Learning](https://mlsanity.com/paper/2511.13741/) (2025)
- [UVTM: Universal Vehicle Trajectory Modeling with ST Feature Domain Generation](https://mlsanity.com/paper/2402.07232/) (2024)
- [PTrajM: Efficient and Semantic-rich Trajectory Learning with Pretrained Trajectory-Mamba](https://mlsanity.com/paper/2408.04916/) (2024)
- [RLOMM: An Efficient and Robust Online Map Matching Framework with Reinforcement Learning](https://mlsanity.com/paper/2502.06825/) (2025)
- [Transformer-based Map Matching Model with Limited Ground-Truth Data using Transfer-Learning Approach](https://mlsanity.com/paper/2108.00439/) (2021)
- [NLP-enabled Trajectory Map-matching in Urban Road Networks using a Transformer-based Encoder-decoder](https://mlsanity.com/paper/2404.12460/) (2024)
- [TrajLearn: Trajectory Prediction Learning using Deep Generative Models](https://mlsanity.com/paper/2501.00184/) (2024)
- [Online Navigation Refinement: Achieving Lane-Level Guidance by Associating Standard-Definition and Online Perception Maps](https://mlsanity.com/paper/2507.07487/) (2025)
- [Zero-Shot Cellular Trajectory Map Matching](https://mlsanity.com/paper/2508.06674/) (2025)
- [Rectify and Align GPS Points to Parking Spots via Rank-1 Constraint](https://mlsanity.com/paper/2510.13439/) (2025)
- [Reconstructing Movement from Sparse Samples: Enhanced Spatio-Temporal Matching Strategies for Low-Frequency Data](https://mlsanity.com/paper/2603.09412/) (2026)

## Abstract (arXiv)

The map-matching is an essential preprocessing step for most of the trajectory-based applications. Although it has been an active topic for more than two decades and, driven by the emerging applications, is still under development. There is a lack of categorisation of existing solutions recently and analysis for future research directions. In this paper, we review the current status of the map-matching problem and survey the existing algorithms. We propose a new categorisation of the solutions according to their map-matching models and working scenarios. In addition, we experimentally compare three representative methods from different categories to reveal how matching model affects the performance. Besides, the experiments are conducted on multiple real datasets with different settings to demonstrate the influence of other factors in map-matching problem, like the trajectory quality, data compression and matching latency.