A Regression Framework for Predicting User's Next Location using Call Detail Records

TL;DR

This paper introduces a deep neural network regression framework utilizing Call Detail Records to accurately predict users' next locations, significantly reducing prediction error compared to traditional models.

Contribution

It presents a novel data processing framework and a deep recurrent neural network model specifically designed for next location prediction using CDR data.

Findings

Prediction error decreased from 74% to 55%.

The framework outperforms traditional models.

Applicable to urban planning and digital marketing.

Abstract

With the growth of using cell phones and the increase in diversity of smart mobile devices, a massive volume of data is generated continuously in the process of using these devices. Among these data, Call Detail Records, CDR, is highly remarkable. Since CDR contains both temporal and spatial labels, mobility analysis of CDR is one of the favorite subjects of study among the researchers. The user next location prediction is one of the main problems in the field of human mobility analysis. In this paper, we propose a data processing framework to predict user next location. We propose domain-specific data processing strategies and design a deep neural network model which is based on recurrent neurons and perform regression tasks. Using this prediction framework, the error of the prediction decreases from 74% to 55% in comparison to the worst and best performing traditional models. Methods, strategies, the framework and the results of this paper can be helpful in many applications such as urban planning and digital marketing.