# AttnMove: History Enhanced Trajectory Recovery via Attentional Network

arXiv: https://arxiv.org/abs/2101.00646 · HTML: https://mlsanity.com/paper/2101.00646/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 3 Jan 2021.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [A Neural Attention Model for Abstractive Sentence Summarization](https://mlsanity.com/paper/1509.00685/) (2015)
- [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://mlsanity.com/paper/1810.11921/) (2018)
- [BRITS: Bidirectional Recurrent Imputation for Time Series](https://mlsanity.com/paper/1805.10572/) (2018)
- [NAIS: Neural Attentive Item Similarity Model for Recommendation](https://mlsanity.com/paper/1809.07053/) (2018)
- [Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation](https://mlsanity.com/paper/1907.08489/) (2019)

## Built on by (most cited first, 11 of 11)

- [Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Era](https://mlsanity.com/paper/2403.14151/) (2024)
- [RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer](https://mlsanity.com/paper/2211.13234/) (2022)
- [LightTR: A Lightweight Framework for Federated Trajectory Recovery](https://mlsanity.com/paper/2405.03409/) (2024)
- [Micro-Macro Spatial-Temporal Graph-based Encoder-Decoder for Map-Constrained Trajectory Recovery](https://mlsanity.com/paper/2404.19141/) (2024)
- [Enhancing Large Language Models for Mobility Analytics with Semantic Location Tokenization](https://mlsanity.com/paper/2506.11109/) (2025)
- [Learning Universal Human Mobility Patterns with a Foundation Model for Cross-domain Data Fusion](https://mlsanity.com/paper/2503.15779/) (2025)
- [UVTM: Universal Vehicle Trajectory Modeling with ST Feature Domain Generation](https://mlsanity.com/paper/2402.07232/) (2024)
- [PLMTrajRec: A Scalable and Generalizable Trajectory Recovery Method with Pre-trained Language Models](https://mlsanity.com/paper/2410.14281/) (2024)
- [DiffMove: Group Mobility Tendency Enhanced Trajectory Recovery via Diffusion Model](https://mlsanity.com/paper/2503.18302/) (2025)
- [ProDiff: Prototype-Guided Diffusion for Minimal Information Trajectory Imputation](https://mlsanity.com/paper/2505.23048/) (2025)
- [Efficient Methods for Accurate Sparse Trajectory Recovery and Map Matching](https://mlsanity.com/paper/2508.10460/) (2025)

## Abstract (arXiv)

A considerable amount of mobility data has been accumulated due to the proliferation of location-based service. Nevertheless, compared with mobility data from transportation systems like the GPS module in taxis, this kind of data is commonly sparse in terms of individual trajectories in the sense that users do not access mobile services and contribute their data all the time. Consequently, the sparsity inevitably weakens the practical value of the data even it has a high user penetration rate. To solve this problem, we propose a novel attentional neural network-based model, named AttnMove, to densify individual trajectories by recovering unobserved locations at a fine-grained spatial-temporal resolution. To tackle the challenges posed by sparsity, we design various intra- and inter- trajectory attention mechanisms to better model the mobility regularity of users and fully exploit the periodical pattern from long-term history. We evaluate our model on two real-world datasets, and extensive results demonstrate the performance gain compared with the state-of-the-art methods. This also shows that, by providing high-quality mobility data, our model can benefit a variety of mobility-oriented down-stream applications.