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

TL;DR

This paper introduces a multi-head self-attentional neural network that integrates spatio-temporal and land use contexts for improved next location prediction, outperforming existing models on large datasets.

Contribution

The study develops a novel MHSA-based model that incorporates diverse mobility-related contexts and demonstrates its superior performance and efficiency over state-of-the-art methods.

Findings

Model outperforms existing prediction models on large datasets.

Population-level training achieves higher accuracy with fewer parameters.

Recent mobility history has the greatest influence on prediction accuracy.

Abstract

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.