# NLPMM: a Next Location Predictor with Markov Modeling

arXiv: https://arxiv.org/abs/2003.07037 · HTML: https://mlsanity.com/paper/2003.07037/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2020.

## Built on by (most cited first, 10 of 10)

- [How do you go where? Improving next location prediction by learning travel mode information using transformers](https://mlsanity.com/paper/2210.04095/) (2022)
- [Human Mobility Modeling During the COVID-19 Pandemic via Deep Graph Diffusion Infomax](https://mlsanity.com/paper/2212.05707/) (2022)
- [NextLocLLM: Location Semantics Modeling and Coordinate-Based Next Location Prediction with LLMs](https://mlsanity.com/paper/2410.09129/) (2024)
- [CoBAD: Modeling Collective Behaviors for Human Mobility Anomaly Detection](https://mlsanity.com/paper/2508.14088/) (2025)
- [Towards Effective Next POI Prediction: Spatial and Semantic Augmentation with Remote Sensing Data](https://mlsanity.com/paper/2404.04271/) (2024)
- [Beyond Regularity: Modeling Chaotic Mobility Patterns for Next Location Prediction](https://mlsanity.com/paper/2509.11713/) (2025)
- [Where have you been? A Study of Privacy Risk for Point-of-Interest Recommendation](https://mlsanity.com/paper/2310.18606/) (2023)
- [UniMove: A Unified Model for Multi-city Human Mobility Prediction](https://mlsanity.com/paper/2508.06986/) (2025)
- [An Accurate and Low-Parameter Machine Learning Architecture for Next Location Prediction](https://mlsanity.com/paper/2402.00306/) (2024)
- [Uncertainty-aware Human Mobility Modeling and Anomaly Detection](https://mlsanity.com/paper/2410.01281/) (2024)

## Abstract (arXiv)

In this paper, we solve the problem of predicting the next locations of the moving objects with a historical dataset of trajectories. We present a Next Location Predictor with Markov Modeling (NLPMM) which has the following advantages: (1) it considers both individual and collective movement patterns in making prediction, (2) it is effective even when the trajectory data is sparse, (3) it considers the time factor and builds models that are suited to different time periods. We have conducted extensive experiments in a real dataset, and the results demonstrate the superiority of NLPMM over existing methods.