# Online Co-movement Pattern Prediction in Mobility Data

arXiv: https://arxiv.org/abs/2102.08870 · HTML: https://mlsanity.com/paper/2102.08870/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 38 references that are in the map.
- First posted on arXiv 17 Feb 2021.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks](https://mlsanity.com/paper/1701.05923/) (2017)
- [Modeling Taxi Drivers' Behaviour for the Next Destination Prediction](https://mlsanity.com/paper/1807.08173/) (2018)
- [Scalable Distributed Subtrajectory Clustering](https://mlsanity.com/paper/1906.06956/) (2019)

## Built on by (most cited first, 1 of 1)

- [MobilityDL: A Review of Deep Learning From Trajectory Data](https://mlsanity.com/paper/2402.00732/) (2024)

## Abstract (arXiv)

Predictive analytics over mobility data are of great importance since they can assist an analyst to predict events, such as collisions, encounters, traffic jams, etc. A typical example of such analytics is future location prediction, where the goal is to predict the future location of a moving object,given a look-ahead time. What is even more challenging is being able to accurately predict collective behavioural patterns of movement, such as co-movement patterns. In this paper, we provide an accurate solution to the problem of Online Prediction of Co-movement Patterns. In more detail, we split the original problem into two sub-problems, namely Future Location Prediction and Evolving Cluster Detection. Furthermore, in order to be able to calculate the accuracy of our solution, we propose a co-movement pattern similarity measure, which facilitates us to match the predicted clusters with the actual ones. Finally, the accuracy of our solution is demonstrated experimentally over a real dataset from the maritime domain.