When and Where: Predicting Human Movements Based on Social Spatial-Temporal Events

TL;DR

This paper introduces a novel approach for predicting human movement times and locations by modeling social interactions and temporal dynamics using ARMA models and Kalman filtering, validated on real datasets.

Contribution

It proposes the concept of Social Spatial-Temporal Events (SSTE) and combines ARMA and ranking models for improved movement prediction accuracy.

Findings

Effective prediction of human movement timing and location.

Kalman Filter enhances ARMA model adaptability.

Validated approach on real-world datasets.

Abstract

Predicting both the time and the location of human movements is valuable but challenging for a variety of applications. To address this problem, we propose an approach considering both the periodicity and the sociality of human movements. We first define a new concept, Social Spatial-Temporal Event (SSTE), to represent social interactions among people. For the time prediction, we characterise the temporal dynamics of SSTEs with an ARMA (AutoRegressive Moving Average) model. To dynamically capture the SSTE kinetics, we propose a Kalman Filter based learning algorithm to learn and incrementally update the ARMA model as a new observation becomes available. For the location prediction, we propose a ranking model where the periodicity and the sociality of human movements are simultaneously taken into consideration for improving the prediction accuracy. Extensive experiments conducted on real data sets validate our proposed approach.