# Context-aware multi-head self-attentional neural network model for next location prediction

arXiv: https://arxiv.org/abs/2212.01953 · HTML: https://mlsanity.com/paper/2212.01953/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 86 references that are in the map.
- First posted on arXiv 4 Dec 2022.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Survey on Deep Learning for Human Mobility](https://mlsanity.com/paper/2012.02825/) (2020)
- [MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Prediction](https://mlsanity.com/paper/2110.01401/) (2021)
- [Learning dynamic and hierarchical traffic spatiotemporal features with Transformer](https://mlsanity.com/paper/2104.05163/) (2021)
- [Deep Learning for Road Traffic Forecasting: Does it Make a Difference?](https://mlsanity.com/paper/2012.02260/) (2020)
- [A Survey of Location Prediction on Twitter](https://mlsanity.com/paper/1705.03172/) (2017)
- [Point-of-Interest Recommender Systems based on Location-Based Social Networks: A Survey from an Experimental Perspective](https://mlsanity.com/paper/2106.10069/) (2021)
- [A Survey of Data Fusion in Smart City Applications](https://mlsanity.com/paper/1905.11933/) (2019)
- [Understanding metropolitan patterns of daily encounters](https://mlsanity.com/paper/1301.5979/) (2013)
- [Understanding Predictability and Exploration in Human Mobility](https://mlsanity.com/paper/1608.01939/) (2016)
- [Modeling Taxi Drivers' Behaviour for the Next Destination Prediction](https://mlsanity.com/paper/1807.08173/) (2018)
- [Contrasting social and non-social sources of predictability in human mobility](https://mlsanity.com/paper/2104.13282/) (2021)

## Built on by (most cited first, 21 of 21)

- [Where Would I Go Next? Large Language Models as Human Mobility Predictors](https://mlsanity.com/paper/2308.15197/) (2023)
- [Llama-Mob: Instruction-Tuning Llama-3-8B Excels in City-Scale Mobility Prediction](https://mlsanity.com/paper/2410.23692/) (2024)
- [Enriching Location Representation with Detailed Semantic Information](https://mlsanity.com/paper/2506.02744/) (2025)
- [NextLocLLM: Location Semantics Modeling and Coordinate-Based Next Location Prediction with LLMs](https://mlsanity.com/paper/2410.09129/) (2024)
- [Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited Places](https://mlsanity.com/paper/2506.14070/) (2025)
- [ELLMob: Event-Driven Human Mobility Generation with Self-Aligned LLM Framework](https://mlsanity.com/paper/2603.07946/) (2026)
- [Beyond Regularity: Modeling Chaotic Mobility Patterns for Next Location Prediction](https://mlsanity.com/paper/2509.11713/) (2025)
- [HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization](https://mlsanity.com/paper/2405.08043/) (2024)
- [Mixture-of-Experts for Personalized and Semantic-Aware Next Location Prediction](https://mlsanity.com/paper/2505.24597/) (2025)
- [A causal intervention framework for synthesizing mobility data and evaluating predictive neural networks](https://mlsanity.com/paper/2311.11749/) (2023)
- [Predicting Next Useful Location With Context-Awareness: The State-Of-The-Art](https://mlsanity.com/paper/2401.08081/) (2024)
- [Next Point-of-interest (POI) Recommendation Model Based on Multi-modal Spatio-temporal Context Feature Embedding](https://mlsanity.com/paper/2509.22661/) (2025)
- [Evaluating geospatial context information for travel mode detection](https://mlsanity.com/paper/2305.19428/) (2023)
- [A Foundational Individual Mobility Prediction Model based on Open-Source Large Language Models](https://mlsanity.com/paper/2503.16553/) (2025)
- [Efficient Temporal Tokenization for Mobility Prediction with Large Language Models](https://mlsanity.com/paper/2507.14017/) (2025)
- [Causal Sensitivity Identification using Generative Learning](https://mlsanity.com/paper/2509.01352/) (2025)
- [Deep Generative Model for Human Mobility Behavior](https://mlsanity.com/paper/2510.06473/) (2025)
- [CrossEarth-Gate: Fisher-Guided Adaptive Tuning Engine for Efficient Adaptation of Cross-Domain Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2511.20302/) (2025)
- [LE4Mob: Towards Inductive, Distance-Aware and General-Purpose Location Embedding for Human Mobility Modelling](https://mlsanity.com/paper/2609.22117/) (2026)
- [Modelling daily activity patterns from mobile phone location data via deep representation learning](https://mlsanity.com/paper/2609.22121/) (2026)
- [Encounter-Driven City-Scale Human Mobility Generation with Large Language Models](https://mlsanity.com/paper/2610.17672/) (2026)

## Abstract (arXiv)

Accurate activity location prediction is a crucial component of many mobility applications and is particularly required to develop personalized, sustainable transportation systems. Despite the widespread adoption of deep learning models, next location prediction models lack a comprehensive discussion and integration of mobility-related spatio-temporal contexts. Here, we utilize a multi-head self-attentional (MHSA) neural network that learns location transition patterns from historical location visits, their visit time and activity duration, as well as their surrounding land use functions, to infer an individual's next location. Specifically, we adopt point-of-interest data and latent Dirichlet allocation for representing locations' land use contexts at multiple spatial scales, generate embedding vectors of the spatio-temporal features, and learn to predict the next location with an MHSA network. Through experiments on two large-scale GNSS tracking datasets, we demonstrate that the proposed model outperforms other state-of-the-art prediction models, and reveal the contribution of various spatio-temporal contexts to the model's performance. Moreover, we find that the model trained on population data achieves higher prediction performance with fewer parameters than individual-level models due to learning from collective movement patterns. We also reveal mobility conducted in the recent past and one week before has the largest influence on the current prediction, showing that learning from a subset of the historical mobility is sufficient to obtain an accurate location prediction result. We believe that the proposed model is vital for context-aware mobility prediction. The gained insights will help to understand location prediction models and promote their implementation for mobility applications.