# Predicting Next Useful Location With Context-Awareness: The State-Of-The-Art

arXiv: https://arxiv.org/abs/2401.08081 · HTML: https://mlsanity.com/paper/2401.08081/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 78 references that are in the map.
- First posted on arXiv 16 Jan 2024.

## Builds on (most cited first, 10 of 10)

- [Deep Learning for Spatio-Temporal Data Mining: A Survey](https://mlsanity.com/paper/1906.04928/) (2019)
- [Where Would I Go Next? Large Language Models as Human Mobility Predictors](https://mlsanity.com/paper/2308.15197/) (2023)
- [A Survey on Deep Learning for Human Mobility](https://mlsanity.com/paper/2012.02825/) (2020)
- [Context-aware multi-head self-attentional neural network model for next location prediction](https://mlsanity.com/paper/2212.01953/) (2022)
- [A Survey of Point-of-interest Recommendation in Location-based Social Networks](https://mlsanity.com/paper/1607.00647/) (2016)
- [An analytical framework to nowcast well-being using mobile phone data](https://mlsanity.com/paper/1606.06279/) (2016)
- [Understanding Predictability and Exploration in Human Mobility](https://mlsanity.com/paper/1608.01939/) (2016)
- [NEXT: A Neural Network Framework for Next POI Recommendation](https://mlsanity.com/paper/1704.04576/) (2017)
- [ST-PIL: Spatial-Temporal Periodic Interest Learning for Next Point-of-Interest Recommendation](https://mlsanity.com/paper/2104.02262/) (2021)
- [Individual Mobility Prediction: An Interpretable Activity-based Hidden Markov Approach](https://mlsanity.com/paper/2101.03996/) (2021)

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

- [Causal Sensitivity Identification using Generative Learning](https://mlsanity.com/paper/2509.01352/) (2025)

## Abstract (arXiv)

Predicting the future location of mobile objects reinforces location-aware services with proactive intelligence and helps businesses and decision-makers with better planning and near real-time scheduling in different applications such as traffic congestion control, location-aware advertisements, and monitoring public health and well-being. The recent developments in the smartphone and location sensors technology and the prevalence of using location-based social networks alongside the improvements in artificial intelligence and machine learning techniques provide an excellent opportunity to exploit massive amounts of historical and real-time contextual information to recognise mobility patterns and achieve more accurate and intelligent predictions. This survey provides a comprehensive overview of the next useful location prediction problem with context-awareness. First, we explain the concepts of context and context-awareness and define the next location prediction problem. Then we analyse nearly thirty studies in this field concerning the prediction method, the challenges addressed, the datasets and metrics used for training and evaluating the model, and the types of context incorporated. Finally, we discuss the advantages and disadvantages of different approaches, focusing on the usefulness of the predicted location and identifying the open challenges and future work on this subject by introducing two potential use cases of next location prediction in the automotive industry.