Inferring and Modeling Migration Flows Using Mobile Phone Network Data

Soranan Hankaew,Santi Phithakkitnukoon,Lina Kattan,Merkebe Getachew Demissie,Carlo Ratti,Zbigniew Smoreda

doi:10.1109/access.2019.2952911

Abstract

Estimating migration flows and forecasting future trends is important, both to understand the causes and effects of migration and to implement policies directed at supplying particular services. Over the years, less research has been done on modeling migration flows than the efforts allocated to modeling other flow types, for instance, commute. Limited data availability has been one of the major impediments for empirical analyses and for theoretical advances in the modeling of migration flows. As a migration trip takes place much less frequent compared to the commute, it requires a longitudinal set of data for the analysis. This study makes use a massive mobile phone network data to infer migration trips and their distribution. Insightful characteristics of the inferred migration trips are revealed, such as intra/inter-district migration flows, migration distance distribution, and origin-destination (O-D) movements. For migration trip distribution modelling, log-linear model, traditional gravity model, and recently introduced radiation model were examined with different approaches taken in defining parameters for each model. As the result, the gravity and log-linear models with a direct distance (displacement) used as its travel cost and district centroids used as the reference points perform best among the other alternative models. A radiation model that considers district population performs best among the radiation models, but worse than that of the gravity and log-linear models.

Highlights

Movement of human beings both individuals as well as groups over short and long distances has long been studied
A trip distribution model is developed to predict or estimate the number of trips that will be made between a pair of zones, e.g., migration flows in our case
We described our methodology that includes data preprocessing, subject selection, and migration trip inference from which some exploratory results were revealed, such as intra/inter-district migration flow characteristics, migration distance distribution, and migration origin-destination (O-D) movements

Summary

Introduction

Movement of human beings both individuals as well as groups over short and long distances has long been studied. Human mobility over short distances are mostly regular trips [1] such as daily commute to work and shopping that can be home-based or non-home-based, while long distance trips are largely associated with temporary or permanent change of the place of residence. The permanent change of residence or migration is a type of long distance trips that are influenced by socio-political, economic, and ecological factors [2] such as occupation opportunities and family. Long distance trips account for a much smaller portion, less regular, and are not well represented in most regional as well as national travel demand models. It is important that these trips are included in the travel demand models.

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Results

Conclusion