# Inferring Uncertain Trajectories from Partial Observations

arXiv: https://arxiv.org/abs/1603.07641 · HTML: https://mlsanity.com/paper/1603.07641/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2016.

## Built on by (most cited first, 4 of 4)

- [LightTR: A Lightweight Framework for Federated Trajectory Recovery](https://mlsanity.com/paper/2405.03409/) (2024)
- [PLMTrajRec: A Scalable and Generalizable Trajectory Recovery Method with Pre-trained Language Models](https://mlsanity.com/paper/2410.14281/) (2024)
- [Pairwise Spatiotemporal Partial Trajectory Matching for Co-movement Analysis](https://mlsanity.com/paper/2412.02879/) (2024)
- [Efficient Methods for Accurate Sparse Trajectory Recovery and Map Matching](https://mlsanity.com/paper/2508.10460/) (2025)

## Abstract (arXiv)

The explosion in the availability of GPS-enabled devices has resulted in an abundance of trajectory data. In reality, however, majority of these trajectories are collected at a low sampling rate and only provide partial observations on their actually traversed routes. Consequently, they are mired with uncertainty. In this paper, we develop a technique called InferTra to infer uncertain trajectories from network-constrained partial observations. Rather than predicting the most likely route, the inferred uncertain trajectory takes the form of an edge-weighted graph and summarizes all probable routes in a holistic manner. For trajectory inference, InferTra employs Gibbs sampling by learning a Network Mobility Model (NMM) from a database of historical trajectories. Extensive experiments on real trajectory databases show that the graph-based approach of InferTra is up to 50% more accurate, 20 times faster, and immensely more versatile than state-of-the-art techniques.